Assessment of insulin sensitivity by the hyperinsulinemic euglycemic clamp: Comparison with the spectral analysis of photoplethysmography.

PubMed

De Souza, Aglecio Luiz; Batista, Gisele Almeida; Alegre, Sarah Monte

2017-01-01

We compare spectral analysis of photoplethysmography (PTG) with insulin resistance measured by the hyperinsulinemic euglycemic clamp (HEC) technique. A total of 100 nondiabetic subjects, 43 men and 57 women aged 20-63years, 30 lean, 42 overweight and 28 obese were enrolled in the study. These patients underwent an examination with HEC, and an examination with the PTG spectral analysis and calculation of the PTG Total Power (PTG-TP). Receiver-operating characteristic (ROC) curves were constructed to determine the specificity and sensitivity of PTG-TP in the assessment of insulin resistance. There is a moderate correlation between insulin sensitivity (M-value) and PTG-TP (r=- 0.64, p<0.0001). The ROC curves showed that the most relevant cutoff to the whole study group was a PTG-TP>406.2. This cut-off had a sensitivity=95.7%, specificity =84,4% and the area under the ROC curve (AUC)=0.929 for identifying insulin resistance. All AUC ROC curve analysis were significant (p<0.0001). The use of the PTG-TP marker measured from the PTG spectral analysis is a useful tool in screening and follow up of IR, especially in large-scale studies. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Evaluation and simplification of the occupational slip, trip and fall risk-assessment test

PubMed Central

NAKAMURA, Takehiro; OYAMA, Ichiro; FUJINO, Yoshihisa; KUBO, Tatsuhiko; KADOWAKI, Koji; KUNIMOTO, Masamizu; ODOI, Haruka; TABATA, Hidetoshi; MATSUDA, Shinya

2016-01-01

Objective: The purpose of this investigation is to evaluate the efficacy of the occupational slip, trip and fall (STF) risk assessment test developed by the Japan Industrial Safety and Health Association (JISHA). We further intended to simplify the test to improve efficiency. Methods: A previous cohort study was performed using 540 employees aged ≥50 years who took the JISHA’s STF risk assessment test. We conducted multivariate analysis using these previous results as baseline values and answers to questionnaire items or score on physical fitness tests as variables. The screening efficiency of each model was evaluated based on the obtained receiver operating characteristic (ROC) curve. Results: The area under the ROC obtained in multivariate analysis was 0.79 when using all items. Six of the 25 questionnaire items were selected for stepwise analysis, giving an area under the ROC curve of 0.77. Conclusion: Based on the results of follow-up performed one year after the initial examination, we successfully determined the usefulness of the STF risk assessment test. Administering a questionnaire alone is sufficient for screening subjects at risk of STF during the subsequent one-year period. PMID:27021057

Quantitative contrast-enhanced ultrasound evaluation of pathological complete response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy.

PubMed

Wan, Cai-Feng; Liu, Xue-Song; Wang, Lin; Zhang, Jie; Lu, Jin-Song; Li, Feng-Hua

2018-06-01

To clarify whether the quantitative parameters of contrast-enhanced ultrasound (CEUS) can be used to predict pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC). Fifty-one patients with histologically proved locally advanced breast cancer scheduled for NAC were enrolled. The quantitative data for CEUS and the tumor diameter were collected at baseline and before surgery, and compared with the pathological response. Multiple logistic regression analysis was performed to examine quantitative parameters at CEUS and the tumor diameter to predict the pCR, and receiver operating characteristic (ROC) curve analysis was used as a summary statistic. Multiple logistic regression analysis revealed that PEAK (the maximum intensity of the time-intensity curve during bolus transit), PEAK%, TTP% (time to peak), and diameter% were significant independent predictors of pCR, and the area under the ROC curve was 0.932(Az 1 ), and the sensitivity and specificity to predict pCR were 93.7% and 80.0%. The area under the ROC curve for the quantitative parameters was 0.927(Az 2 ), and the sensitivity and specificity to predict pCR were 81.2% and 94.3%. For diameter%, the area under the ROC curve was 0.786 (Az 3 ), and the sensitivity and specificity to predict pCR were 93.8% and 54.3%. The values of Az 1 and Az 2 were significantly higher than that of Az 3 (P = 0.027 and P = 0.034, respectively). However, there was no significant difference between the values of Az 1 and Az 2 (P = 0.825). Quantitative analysis of tumor blood perfusion with CEUS is superior to diameter% to predict pCR, and can be used as a functional technique to evaluate tumor response to NAC. Copyright © 2018. Published by Elsevier B.V.

Describing three-class task performance: three-class linear discriminant analysis and three-class ROC analysis

NASA Astrophysics Data System (ADS)

He, Xin; Frey, Eric C.

2007-03-01

Binary ROC analysis has solid decision-theoretic foundations and a close relationship to linear discriminant analysis (LDA). In particular, for the case of Gaussian equal covariance input data, the area under the ROC curve (AUC) value has a direct relationship to the Hotelling trace. Many attempts have been made to extend binary classification methods to multi-class. For example, Fukunaga extended binary LDA to obtain multi-class LDA, which uses the multi-class Hotelling trace as a figure-of-merit, and we have previously developed a three-class ROC analysis method. This work explores the relationship between conventional multi-class LDA and three-class ROC analysis. First, we developed a linear observer, the three-class Hotelling observer (3-HO). For Gaussian equal covariance data, the 3- HO provides equivalent performance to the three-class ideal observer and, under less strict conditions, maximizes the signal to noise ratio for classification of all pairs of the three classes simultaneously. The 3-HO templates are not the eigenvectors obtained from multi-class LDA. Second, we show that the three-class Hotelling trace, which is the figureof- merit in the conventional three-class extension of LDA, has significant limitations. Third, we demonstrate that, under certain conditions, there is a linear relationship between the eigenvectors obtained from multi-class LDA and 3-HO templates. We conclude that the 3-HO based on decision theory has advantages both in its decision theoretic background and in the usefulness of its figure-of-merit. Additionally, there exists the possibility of interpreting the two linear features extracted by the conventional extension of LDA from a decision theoretic point of view.

A new method to predict anatomical outcome after idiopathic macular hole surgery.

PubMed

Liu, Peipei; Sun, Yaoyao; Dong, Chongya; Song, Dan; Jiang, Yanrong; Liang, Jianhong; Yin, Hong; Li, Xiaoxin; Zhao, Mingwei

2016-04-01

To investigate whether a new macular hole closure index (MHCI) could predict anatomic outcome of macular hole surgery. A vitrectomy with internal limiting membrane peeling, air-fluid exchange, and gas tamponade were performed on all patients. The postoperative anatomic status of the macular hole was defined by spectral-domain OCT. MHCI was calculated as (M+N)/BASE based on the preoperative OCT status. M and N were the curve lengths of the detached photoreceptor arms, and BASE was the length of the retinal pigment epithelial layer (RPE layer) detaching from the photoreceptors. Postoperative anatomical outcomes were divided into three grades: A (bridge-like closure), B (good closure), and C (poor closure or no closure). Correlation analysis was performed between anatomical outcomes and MHCI. Receiver operating characteristic (ROC) curves were derived for MHCI, indicating good model discrimination. ROC curves were also assessed by the area under the curve, and cut-offs were calculated. Other predictive parameters reported previously, which included the MH minimum, the MH height, the macular hole index (MHI), the diameter hole index (DHI), and the tractional hole index (THI) had been compared as well. MHCI correlated significantly with postoperative anatomical outcomes (r = 0.543, p = 0.000), but other predictive parameters did not. The areas under the curves indicated that MHCI could be used as an effective predictor of anatomical outcome. Cut-off values of 0.7 and 1.0 were obtained for MHCI from ROC curve analysis. MHCI demonstrated a better predictive effect than other parameters, both in the correlation analysis and ROC analysis. MHCI could be an easily measured and accurate predictive index for postoperative anatomical outcomes.

Comparison of Paired ROC Curves through a Two-Stage Test.

PubMed

Yu, Wenbao; Park, Eunsik; Chang, Yuan-Chin Ivan

2015-01-01

The area under the receiver operating characteristic (ROC) curve (AUC) is a popularly used index when comparing two ROC curves. Statistical tests based on it for analyzing the difference have been well developed. However, this index is less informative when two ROC curves cross and have similar AUCs. In order to detect differences between ROC curves in such situations, a two-stage nonparametric test that uses a shifted area under the ROC curve (sAUC), along with AUCs, is proposed for paired designs. The new procedure is shown, numerically, to be effective in terms of power under a wide range of scenarios; additionally, it outperforms two conventional ROC-type tests, especially when two ROC curves cross each other and have similar AUCs. Larger sAUC implies larger partial AUC at the range of low false-positive rates in this case. Because high specificity is important in many classification tasks, such as medical diagnosis, this is an appealing characteristic. The test also implicitly analyzes the equality of two commonly used binormal ROC curves at every operating point. We also apply the proposed method to synthesized data and two real examples to illustrate its usefulness in practice.

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation.

PubMed

Perandini, Simone; Soardi, G A; Larici, A R; Del Ciello, A; Rizzardi, G; Solazzo, A; Mancino, L; Zeraj, F; Bernhart, M; Signorini, M; Motton, M; Montemezzi, S

2017-05-01

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. • The Herder model showed a ROC AUC of 0.807 on 180 SPNs. • The BIMC model showed a ROC AUC of 0.822 on 180 SPNs. • Decision analysis is more favourable to the BIMC model.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

Impact of Wisteria floribunda Agglutinin-Positive Mac-2-Binding Protein in Patients with Hepatitis C Virus-Related Compensated Liver Cirrhosis.

PubMed

Hasegawa, Kunihiro; Takata, Ryo; Nishikawa, Hiroki; Enomoto, Hirayuki; Ishii, Akio; Iwata, Yoshinori; Miyamoto, Yuho; Ishii, Noriko; Yuri, Yukihisa; Nakano, Chikage; Nishimura, Takashi; Yoh, Kazunori; Aizawa, Nobuhiro; Sakai, Yoshiyuki; Ikeda, Naoto; Takashima, Tomoyuki; Iijima, Hiroko; Nishiguchi, Shuhei

2016-09-12

We aimed to examine the effect of Wisteria floribunda agglutinin-positive Mac-2-binding protein (WFA⁺-M2BP) level on survival comparing with other laboratory liver fibrosis markers in hepatitis C virus (HCV)-related compensated liver cirrhosis (LC) (n = 165). For assessing prognostic performance of continuous fibrosis markers, we adapted time-dependent receiver operating characteristics (ROC) curves for clinical outcome. In time-dependent ROC analysis, annual area under the ROCs (AUROCs) were plotted. We also calculated the total sum of AUROCs in all time-points (TAAT score) in each fibrosis marker. WFA⁺-M2BP value ranged from 0.66 cutoff index (COI) to 19.95 COI (median value, 5.29 COI). Using ROC analysis for survival, the optimal cutoff point for WFA⁺-M2BP was 6.15 COI (AUROC = 0.79348, sensitivity = 80.0%, specificity = 74.78%). The cumulative five-year survival rate in patients with WFA⁺-M2BP ≥ 6.15 COI (n = 69) was 43.99%, while that in patients with WFA⁺-M2BP < 6.15 COI (n = 96) was 88.40% (p < 0.0001). In the multivariate analysis, absence of hepatocellular carcinoma (p = 0.0008), WFA⁺-M2BP < 6.15 COI (p = 0.0132), achievement of sustained virological response (p < 0.0001) and des-γ-carboxy prothrombin < 41 mAU/mL (p = 0.0018) were significant favorable predictors linked to survival. In time-dependent ROC analysis in all cases, WFA⁺-M2BP had the highest TAAT score among liver fibrosis markers. In conclusion, WFA⁺-M2BP can be a useful predictor in HCV-related compensated LC.

The use of intraoperative triggered electromyography to detect misplaced pedicle screws: a systematic review and meta-analysis.

PubMed

Mikula, Anthony L; Williams, Seth K; Anderson, Paul A

2016-04-01

Insertion of instruments or implants into the spine carries a risk for injury to neural tissue. Triggered electromyography (tEMG) is an intraoperative neuromonitoring technique that involves electrical stimulation of a tool or screw and subsequent measurement of muscle action potentials from myotomes innervated by nerve roots near the stimulated instrument. The authors of this study sought to determine the ability of tEMG to detect misplaced pedicle screws (PSs). The authors searched the U.S. National Library of Medicine, the Web of Science Core Collection database, and the Cochrane Central Register of Controlled Trials for PS studies. A meta-analysis of these studies was performed on a per-screw basis to determine the ability of tEMG to detect misplaced PSs. Sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were calculated overall and in subgroups. Twenty-six studies were included in the systematic review. The authors analyzed 18 studies in which tEMG was used during PS placement in the meta-analysis, representing data from 2932 patients and 15,065 screws. The overall sensitivity of tEMG for detecting misplaced PSs was 0.78, and the specificity was 0.94. The overall ROC AUC was 0.96. A tEMG current threshold of 10-12 mA (ROC AUC 0.99) and a pulse duration of 300 µsec (ROC AUC 0.97) provided the most accurate testing parameters for detecting misplaced screws. Screws most accurately conducted EMG signals (ROC AUC 0.98). Triggered electromyography has very high specificity but only fair sensitivity for detecting malpositioned PSs.

Nonparametric EROC analysis for observer performance evaluation on joint detection and estimation tasks

NASA Astrophysics Data System (ADS)

Wunderlich, Adam; Goossens, Bart

2014-03-01

The majority of the literature on task-based image quality assessment has focused on lesion detection tasks, using the receiver operating characteristic (ROC) curve, or related variants, to measure performance. However, since many clinical image evaluation tasks involve both detection and estimation (e.g., estimation of kidney stone composition, estimation of tumor size), there is a growing interest in performance evaluation for joint detection and estimation tasks. To evaluate observer performance on such tasks, Clarkson introduced the estimation ROC (EROC) curve, and the area under the EROC curve as a summary figure of merit. In the present work, we propose nonparametric estimators for practical EROC analysis from experimental data, including estimators for the area under the EROC curve and its variance. The estimators are illustrated with a practical example comparing MRI images reconstructed from different k-space sampling trajectories.

Estimating the Area Under ROC Curve When the Fitted Binormal Curves Demonstrate Improper Shape.

PubMed

Bandos, Andriy I; Guo, Ben; Gur, David

2017-02-01

The "binormal" model is the most frequently used tool for parametric receiver operating characteristic (ROC) analysis. The binormal ROC curves can have "improper" (non-concave) shapes that are unrealistic in many practical applications, and several tools (eg, PROPROC) have been developed to address this problem. However, due to the general robustness of binormal ROCs, the improperness of the fitted curves might carry little consequence for inferences about global summary indices, such as the area under the ROC curve (AUC). In this work, we investigate the effect of severe improperness of fitted binormal ROC curves on the reliability of AUC estimates when the data arise from an actually proper curve. We designed theoretically proper ROC scenarios that induce severely improper shape of fitted binormal curves in the presence of well-distributed empirical ROC points. The binormal curves were fitted using maximum likelihood approach. Using simulations, we estimated the frequency of severely improper fitted curves, bias of the estimated AUC, and coverage of 95% confidence intervals (CIs). In Appendix S1, we provide additional information on percentiles of the distribution of AUC estimates and bias when estimating partial AUCs. We also compared the results to a reference standard provided by empirical estimates obtained from continuous data. We observed up to 96% of severely improper curves depending on the scenario in question. The bias in the binormal AUC estimates was very small and the coverage of the CIs was close to nominal, whereas the estimates of partial AUC were biased upward in the high specificity range and downward in the low specificity range. Compared to a non-parametric approach, the binormal model led to slightly more variable AUC estimates, but at the same time to CIs with more appropriate coverage. The improper shape of the fitted binormal curve, by itself, ie, in the presence of a sufficient number of well-distributed points, does not imply unreliable AUC-based inferences. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system.

PubMed

Tanaka, Toyohiko; Nitta, Norihisa; Ohta, Shinichi; Kobayashi, Tsuyoshi; Kano, Akiko; Tsuchiya, Keiko; Murakami, Yoko; Kitahara, Sawako; Wakamiya, Makoto; Furukawa, Akira; Takahashi, Masashi; Murata, Kiyoshi

2009-12-01

A computer-aided detection (CAD) system was evaluated for its ability to detect microcalcifications and masses on images obtained with a digital phase-contrast mammography (PCM) system, a system characterised by the sharp images provided by phase contrast and by the high resolution of 25-μm-pixel mammograms. Fifty abnormal and 50 normal mammograms were collected from about 3,500 mammograms and printed on film for reading on a light box. Seven qualified radiologists participated in an observer study based on receiver operating characteristic (ROC) analysis. The average of the areas under ROC curve (AUC) values for the ROC analysis with and without CAD were 0.927 and 0.897 respectively (P = 0.015). The AUC values improved from 0.840 to 0.888 for microcalcifications (P = 0.034) and from 0.947 to 0.962 for masses (P = 0.025) respectively. The application of CAD to the PCM system is a promising approach for the detection of breast cancer in its early stages.

Utility of histogram analysis of apparent diffusion coefficient maps obtained using 3.0T MRI for distinguishing uterine carcinosarcoma from endometrial carcinoma.

PubMed

Takahashi, Masahiro; Kozawa, Eito; Tanisaka, Megumi; Hasegawa, Kousei; Yasuda, Masanori; Sakai, Fumikazu

2016-06-01

We explored the role of histogram analysis of apparent diffusion coefficient (ADC) maps for discriminating uterine carcinosarcoma and endometrial carcinoma. We retrospectively evaluated findings in 13 patients with uterine carcinosarcoma and 50 patients with endometrial carcinoma who underwent diffusion-weighted imaging (b = 0, 500, 1000 s/mm(2) ) at 3T with acquisition of corresponding ADC maps. We derived histogram data from regions of interest drawn on all slices of the ADC maps in which tumor was visualized, excluding areas of necrosis and hemorrhage in the tumor. We used the Mann-Whitney test to evaluate the capacity of histogram parameters (mean ADC value, 5th to 95th percentiles, skewness, kurtosis) to discriminate uterine carcinosarcoma and endometrial carcinoma and analyzed the receiver operating characteristic (ROC) curve to determine the optimum threshold value for each parameter and its corresponding sensitivity and specificity. Carcinosarcomas demonstrated significantly higher mean vales of ADC, 95th, 90th, 75th, 50th, 25th percentiles and kurtosis than endometrial carcinomas (P < 0.05). ROC curve analysis of the 75th percentile yielded the best area under the ROC curve (AUC; 0.904), sensitivity of 100%, and specificity of 78.0%, with a cutoff value of 1.034 × 10(-3) mm(2) /s. Histogram analysis of ADC maps might be helpful for discriminating uterine carcinosarcomas and endometrial carcinomas. J. Magn. Reson. Imaging 2016;43:1301-1307. © 2015 Wiley Periodicals, Inc.

Anxiety and Depression Screening for Youth in a Primary Care Population

PubMed Central

Katon, Wayne; Joan, Russo; Richardson, Laura; McCauley, Elizabeth; Lozano, Paula

2008-01-01

Objective Prior studies have shown a low rate of accurate identification by primary care physicians of mental health disorders in youth. This study tested the psychometric properties of two brief mental health screening questionnaires, the Mood and Feelings Questionnaire – Short Form (MFQ-SF) and Childhood Anxiety Sensitivity Index (ASI), in a large sample of youth. Methods In a sample of 1375 youth age 11 to 17 (779 with asthma, 596 randomly selected controls) enrolled in an HMO, the psychometric properties (optimum cutoffs on Receiver Operating Curve (ROC) curves, sensitivity, specificity, positive and negative predictive values) of two brief anxiety and depression screens were examined versus a “gold standard” structured psychiatric interview. Results Both the MFQ-SF and ASI performed well on ROC analysis for screening youth for one or more DSM-IV anxiety or depressive disorders. The MFQ-SF performed better on ROC analysis than the ASI for youth with major depression (area under the curve of 0.84 vs 0.77). For screening youth with anxiety disorders ROC curves showed that both the MFQ-SF and ASI only performed in the fair range (area under the curve of 0.76). Conclusion The MFQ-SF and ASI are two relatively brief questionnaires that performed well for screening youth for one or more DSM-IV anxiety or depressive disorders. The MFQ-SF performed better than the ASI for screening youth with major depression. Use of these instruments could increase the accuracy of identification of mental health disorders in youth by primary care physicians. PMID:18501865

Analysis of ROC on chest direct digital radiography (DR) after image processing in diagnosis of SARS

NASA Astrophysics Data System (ADS)

Lv, Guozheng; Lan, Rihui; Zeng, Qingsi; Zheng, Zhong

2004-05-01

The Severe Acute Respiratory Syndrome (SARS, also called Infectious Atypical Pneumonia), which initially broke out in late 2002, has threatened the public"s health seriously. How to confirm the patients contracting SARS becomes an urgent issue in diagnosis. This paper intends to evaluate the importance of Image Processing in the diagnosis on SARS at the early stage. Receiver Operating Characteristics (ROC) analysis has been employed in this study to compare the value of DR images in the diagnosis of SARS patients before and after image processing by Symphony Software supplied by E-Com Technology Ltd., and DR image study of 72 confirmed or suspected SARS patients were reviewed respectively. All the images taken from the studied patients were processed by Symphony. Both the original and processed images were taken into ROC analysis, based on which the ROC graph for each group of images has been produced as described below: For processed images: a = 1.9745, b = 1.4275, SA = 0.8714; For original images: a = 0.9066, b = 0.8310, SA = 0.7572; (a - intercept, b - slop, SA - Area below the curve). The result shows significant difference between the original images and processed images (P<0.01). In summary, the images processed by Symphony are superior to the original ones in detecting the opacity lesion, and increases the accuracy of SARS diagnosis.

Fatty acids and plasmalogens of the phospholipids of the sperm membranes and their relation with the post-thaw quality of stallion spermatozoa.

PubMed

Macías García, B; González Fernández, L; Ortega Ferrusola, C; Morillo Rodríguez, A; Gallardo Bolaños, J M; Rodríguez Martinez, H; Tapia, J A; Morcuende, D; Peña, F J

2011-03-15

Fatty acids and plasmalogens were extracted from the phospholipids of the plasma membrane of stallion spermatozoa, to determine their relation with sperm quality after freezing and thawing. Sperm quality was rated using a quality index that combined the results of the analysis of sperm motility and velocity (CASA analysis), membrane status and mitochondrial membrane potential (flow cytometry) post thaw. Receiving operating system (ROC) curves were used to evaluate the value of specific lipid components of the sperm membrane herein studied as forecast of potential freezeability. From all parameters studied the ratio of percentage of C16 plasmalogens related to total phospholipids was the one with the better diagnostic value. For potentially bad freezers, the significant area under the ROC-curve was 0.74, with 75% sensitivity and 79.9% specificity for a cut off value of 26.9. Also the percentage of plasmalogens respect to total phospholipids gave good diagnostic value for bad freezers. On the other hand, the percentage of C18 fatty aldehydes related to total phospholipids of the sperm membrane properly forecasted freezeability with an area under the ROC curve of 0.70 with 70% sensitivity and 62.5% specificity for a cut off value of 0.32. Copyright © 2011 Elsevier Inc. All rights reserved.

Statistical properties of a utility measure of observer performance compared to area under the ROC curve

NASA Astrophysics Data System (ADS)

Abbey, Craig K.; Samuelson, Frank W.; Gallas, Brandon D.; Boone, John M.; Niklason, Loren T.

2013-03-01

The receiver operating characteristic (ROC) curve has become a common tool for evaluating diagnostic imaging technologies, and the primary endpoint of such evaluations is the area under the curve (AUC), which integrates sensitivity over the entire false positive range. An alternative figure of merit for ROC studies is expected utility (EU), which focuses on the relevant region of the ROC curve as defined by disease prevalence and the relative utility of the task. However if this measure is to be used, it must also have desirable statistical properties keep the burden of observer performance studies as low as possible. Here, we evaluate effect size and variability for EU and AUC. We use two observer performance studies recently submitted to the FDA to compare the EU and AUC endpoints. The studies were conducted using the multi-reader multi-case methodology in which all readers score all cases in all modalities. ROC curves from the study were used to generate both the AUC and EU values for each reader and modality. The EU measure was computed assuming an iso-utility slope of 1.03. We find mean effect sizes, the reader averaged difference between modalities, to be roughly 2.0 times as big for EU as AUC. The standard deviation across readers is roughly 1.4 times as large, suggesting better statistical properties for the EU endpoint. In a simple power analysis of paired comparison across readers, the utility measure required 36% fewer readers on average to achieve 80% statistical power compared to AUC.

Proximal caries detection: Sirona Sidexis versus Kodak Ektaspeed Plus.

PubMed

Khan, Emad A; Tyndall, Donald A; Ludlow, John B; Caplan, Daniel

2005-01-01

This study compared the accuracy of intraoral film and a charge-coupled device (CCD) receptor for proximal caries detection. Four observers evaluated images of the proximal surfaces of 40 extracted posterior teeth. The presence or absence of caries was scored using a five-point confidence scale. The actual status of each surface was determined from ground section histology. Responses were evaluated by means of receiver operating characteristic (ROC) analysis. Areas under ROC curves (Az) were assessed through a paired t-test. The performance of the CCD-based intraoral sensor was not different statistically from Ektaspeed Plus film in detecting proximal caries.

OP007. PLGF in combination with other commonly utilised tests and other biomarkers for predicting need for delivery for pre-eclampsia within 14days in women presenting prior to 35weeks' gestation.

PubMed

Lucy, Chappell; Suzy, Duckworth; Melanie, Griffin; Paul, Seed; Christopher, Redman; Andrew, Shennan

2013-04-01

Current means of assessing women presenting with suspected pre-eclampsia using BP and proteinuria are of limited use in predicting need for imminent delivery. We undertook a prospective multicentre study to determine diagnostic accuracy of PlGF <5th centile (Triage assay) and other candidate biomarkers in women presenting with suspected pre-eclampsia at 20-35weeks' gestation, in determining need for delivery for pre-eclampsia within 14days. We calculated ROC curves for predictive potential and undertook principal factor analysis to determine additional predictive ability for biomarker combinations. In 287 women enrolled prior to 35weeks, ROC area (0.88, SE 0.03) for PlGF <5th centile for pre-eclampsia requiring delivery within 14days was greater than all other commonly utilised tests (systolic and diastolic BP, urate, ALT), either singly (range 0.58-0.68), or in combination (0.69) (p<0.001 for all comparisons), and was greater than that of all other biomarkers; addition of 2 other biomarker panels (either procalcitonin, nephrin and BNP; or cystatin and PAPP-A) increased ROC area to 0.90 but these biomarkers had limited predictive ability on their own. In women presenting prior to 35weeks' gestation with suspected pre-eclampsia, low PlGF has a greater ROC area than other commonly utilised tests. Additional biomarkers add only a small increment to the predictive value of a single PlGF measurement. Copyright © 2013. Published by Elsevier B.V.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome.

PubMed

Lalonde, Michel; Wells, R Glenn; Birnie, David; Ruddy, Terrence D; Wassenaar, Richard

2014-07-01

Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.

Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard; Wells, R. Glenn

2014-07-15

Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: Aboutmore » 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster analysis results were similar to SPECT RNA phase analysis (ROC AUC = 0.78, p = 0.73 vs cluster AUC; sensitivity/specificity = 59%/89%) and PET scar size analysis (ROC AUC = 0.73, p = 1.0 vs cluster AUC; sensitivity/specificity = 76%/67%). Conclusions: A SPECT RNA cluster analysis algorithm was developed for the prediction of CRT outcome. Cluster analysis results produced results equivalent to those obtained from Fourier and scar analysis.« less

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

PubMed

Becker, A S; Blüthgen, C; Phi van, V D; Sekaggya-Wiltshire, C; Castelnuovo, B; Kambugu, A; Fehr, J; Frauenfelder, T

2018-03-01

To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients. In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix. The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa). Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Influences on emergency department length of stay for older people.

PubMed

Street, Maryann; Mohebbi, Mohammadreza; Berry, Debra; Cross, Anthony; Considine, Julie

2018-02-14

The aim of this study was to examine the influences on emergency department (ED) length of stay (LOS) for older people and develop a predictive model for an ED LOS more than 4 h. This retrospective cohort study used organizational data linkage at the patient level from a major Australian health service. The study population was aged 65 years or older, attending an ED during the 2013/2014 financial year. We developed and internally validated a clinical prediction rule. Discriminatory performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. An integer-based risk score was developed using multivariate logistic regression. The risk score was evaluated using ROC analysis. There were 33 926 ED attendances: 57.5% (n=19 517) had an ED LOS more than 4 h. The area under ROC for age, usual accommodation, triage category, arrival by ambulance, arrival overnight, imaging, laboratory investigations, overcrowding, time to be seen by doctor, ED visits with admission and access block relating to ED LOS more than 4 h was 0.796, indicating good performance. In the validation set, area under ROC was 0.80, P-value was 0.36 and prediction mean square error was 0.18, indicating good calibration. The risk score value attributed to each risk factor ranged from 2 to 68 points. The clinical prediction rule stratified patients into five levels of risk on the basis of the total risk score. Objective identification of older people at intermediate and high risk of an ED LOS more than 4 h early in ED care enables targeted approaches to streamline the patient journey, decrease ED LOS and optimize emergency care for older people.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

THE VALIDITY OF USING ROC SOFTWARE FOR ANALYSING VISUAL GRADING CHARACTERISTICS DATA: AN INVESTIGATION BASED ON THE NOVEL SOFTWARE VGC ANALYZER.

PubMed

Hansson, Jonny; Månsson, Lars Gunnar; Båth, Magnus

2016-06-01

The purpose of the present work was to investigate the validity of using single-reader-adapted receiver operating characteristics (ROC) software for analysis of visual grading characteristics (VGC) data. VGC data from four published VGC studies on optimisation of X-ray examinations, previously analysed using ROCFIT, were reanalysed using a recently developed software dedicated to VGC analysis (VGC Analyzer), and the outcomes [the mean and 95 % confidence interval (CI) of the area under the VGC curve (AUCVGC) and the p-value] were compared. The studies included both paired and non-paired data and were reanalysed both for the fixed-reader and the random-reader situations. The results showed good agreement between the softwares for the mean AUCVGC For non-paired data, wider CIs were obtained with VGC Analyzer than previously reported, whereas for paired data, the previously reported CIs were similar or even broader. Similar observations were made for the p-values. The results indicate that the use of single-reader-adapted ROC software such as ROCFIT for analysing non-paired VGC data may lead to an increased risk of committing Type I errors, especially in the random-reader situation. On the other hand, the use of ROC software for analysis of paired VGC data may lead to an increased risk of committing Type II errors, especially in the fixed-reader situation. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Influence of optic disc size on the diagnostic performance of macular ganglion cell complex and peripapillary retinal nerve fiber layer analyses in glaucoma.

PubMed

Cordeiro, Daniela Valença; Lima, Verônica Castro; Castro, Dinorah P; Castro, Leonardo C; Pacheco, Maria Angélica; Lee, Jae Min; Dimantas, Marcelo I; Prata, Tiago Santos

2011-01-01

To evaluate the influence of optic disc size on the diagnostic accuracy of macular ganglion cell complex (GCC) and conventional peripapillary retinal nerve fiber layer (pRNFL) analyses provided by spectral domain optical coherence tomography (SD-OCT) in glaucoma. Eighty-two glaucoma patients and 30 healthy subjects were included. All patients underwent GCC (7 × 7 mm macular grid, consisting of RNFL, ganglion cell and inner plexiform layers) and pRNFL thickness measurement (3.45 mm circular scan) by SD-OCT. One eye was randomly selected for analysis. Initially, receiver operating characteristic (ROC) curves were generated for different GCC and pRNFL parameters. The effect of disc area on the diagnostic accuracy of these parameters was evaluated using a logistic ROC regression model. Subsequently, 1.5, 2.0, and 2.5 mm(2) disc sizes were arbitrarily chosen (based on data distribution) and the predicted areas under the ROC curves (AUCs) and sensitivities were compared at fixed specificities for each. Average mean deviation index for glaucomatous eyes was -5.3 ± 5.2 dB. Similar AUCs were found for the best pRNFL (average thickness = 0.872) and GCC parameters (average thickness = 0.824; P = 0.19). The coefficient representing disc area in the ROC regression model was not statistically significant for average pRNFL thickness (-0.176) or average GCC thickness (0.088; P ≥ 0.56). AUCs for fixed disc areas (1.5, 2.0, and 2.5 mm(2)) were 0.904, 0.891, and 0.875 for average pRNFL thickness and 0.834, 0.842, and 0.851 for average GCC thickness, respectively. The highest sensitivities - at 80% specificity for average pRNFL (84.5%) and GCC thicknesses (74.5%) - were found with disc sizes fixed at 1.5 mm(2) and 2.5 mm(2). Diagnostic accuracy was similar between pRNFL and GCC thickness parameters. Although not statistically significant, there was a trend for a better diagnostic accuracy of pRNFL thickness measurement in cases of smaller discs. For GCC analysis, an inverse effect was observed.

Automatic Target Recognition Classification System Evaluation Methodology

DTIC Science & Technology

2002-09-01

Testing Set of Two-Class XOR Data (250 Samples)......................................... 2-59 2.27 Decision Analysis Process Flow Chart...ROC curve meta - analysis , which is the estimation of the true ROC curve of a given diagnostic system through ROC analysis across many studies or...technique can be very effective in sensitivity analysis ; trying to determine which data points have the most effect on the solution, and in

Predictive inference for best linear combination of biomarkers subject to limits of detection.

PubMed

Coolen-Maturi, Tahani

2017-08-15

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Diagnostic accuracy and receiver-operating characteristics curve analysis in surgical research and decision making.

PubMed

Søreide, Kjetil; Kørner, Hartwig; Søreide, Jon Arne

2011-01-01

In surgical research, the ability to correctly classify one type of condition or specific outcome from another is of great importance for variables influencing clinical decision making. Receiver-operating characteristic (ROC) curve analysis is a useful tool in assessing the diagnostic accuracy of any variable with a continuous spectrum of results. In order to rule a disease state in or out with a given test, the test results are usually binary, with arbitrarily chosen cut-offs for defining disease versus health, or for grading of disease severity. In the postgenomic era, the translation from bench-to-bedside of biomarkers in various tissues and body fluids requires appropriate tools for analysis. In contrast to predetermining a cut-off value to define disease, the advantages of applying ROC analysis include the ability to test diagnostic accuracy across the entire range of variable scores and test outcomes. In addition, ROC analysis can easily examine visual and statistical comparisons across tests or scores. ROC is also favored because it is thought to be independent from the prevalence of the condition under investigation. ROC analysis is used in various surgical settings and across disciplines, including cancer research, biomarker assessment, imaging evaluation, and assessment of risk scores.With appropriate use, ROC curves may help identify the most appropriate cutoff value for clinical and surgical decision making and avoid confounding effects seen with subjective ratings. ROC curve results should always be put in perspective, because a good classifier does not guarantee the expected clinical outcome. In this review, we discuss the fundamental roles, suggested presentation, potential biases, and interpretation of ROC analysis in surgical research.

Assessing the Classification Accuracy of Early Numeracy Curriculum-Based Measures Using Receiver Operating Characteristic Curve Analysis

ERIC Educational Resources Information Center

Laracy, Seth D.; Hojnoski, Robin L.; Dever, Bridget V.

2016-01-01

Receiver operating characteristic curve (ROC) analysis was used to investigate the ability of early numeracy curriculum-based measures (EN-CBM) administered in preschool to predict performance below the 25th and 40th percentiles on a quantity discrimination measure in kindergarten. Areas under the curve derived from a sample of 279 students ranged…

Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine

NASA Astrophysics Data System (ADS)

Obuchowski, Nancy A.; Bullen, Jennifer A.

2018-04-01

Receiver operating characteristic (ROC) analysis is a tool used to describe the discrimination accuracy of a diagnostic test or prediction model. While sensitivity and specificity are the basic metrics of accuracy, they have many limitations when characterizing test accuracy, particularly when comparing the accuracies of competing tests. In this article we review the basic study design features of ROC studies, illustrate sample size calculations, present statistical methods for measuring and comparing accuracy, and highlight commonly used ROC software. We include descriptions of multi-reader ROC study design and analysis, address frequently seen problems of verification and location bias, discuss clustered data, and provide strategies for testing endpoints in ROC studies. The methods are illustrated with a study of transmission ultrasound for diagnosing breast lesions.

The three-class ideal observer for univariate normal data: Decision variable and ROC surface properties

PubMed Central

Edwards, Darrin C.; Metz, Charles E.

2012-01-01

Although a fully general extension of ROC analysis to classification tasks with more than two classes has yet to be developed, the potential benefits to be gained from a practical performance evaluation methodology for classification tasks with three classes have motivated a number of research groups to propose methods based on constrained or simplified observer or data models. Here we consider an ideal observer in a task with underlying data drawn from three univariate normal distributions. We investigate the behavior of the resulting ideal observer’s decision variables and ROC surface. In particular, we show that the pair of ideal observer decision variables is constrained to a parametric curve in two-dimensional likelihood ratio space, and that the decision boundary line segments used by the ideal observer can intersect this curve in at most six places. From this, we further show that the resulting ROC surface has at most four degrees of freedom at any point, and not the five that would be required, in general, for a surface in a six-dimensional space to be non-degenerate. In light of the difficulties we have previously pointed out in generalizing the well-known area under the ROC curve performance metric to tasks with three or more classes, the problem of developing a suitable and fully general performance metric for classification tasks with three or more classes remains unsolved. PMID:23162165

Software for illustrative presentation of basic clinical characteristics of laboratory tests--GraphROC for Windows.

PubMed

Kairisto, V; Poola, A

1995-01-01

GraphROC for Windows is a program for clinical test evaluation. It was designed for the handling of large datasets obtained from clinical laboratory databases. In the user interface, graphical and numerical presentations are combined. For simplicity, numerical data is not shown unless requested. Relevant numbers can be "picked up" from the graph by simple mouse operations. Reference distributions can be displayed by using automatically optimized bin widths. Any percentile of the distribution with corresponding confidence limits can be chosen for display. In sensitivity-specificity analysis, both illness- and health-related distributions are shown in the same graph. The following data for any cutoff limit can be shown in a separate click window: clinical sensitivity and specificity with corresponding confidence limits, positive and negative likelihood ratios, positive and negative predictive values and efficiency. Predictive values and clinical efficiency of the cutoff limit can be updated for any prior probability of disease. Receiver Operating Characteristics (ROC) curves can be generated and combined into the same graph for comparison of several different tests. The area under the curve with corresponding confidence interval is calculated for each ROC curve. Numerical results of analyses and graphs can be printed or exported to other Microsoft Windows programs. GraphROC for Windows also employs a new method, developed by us, for the indirect estimation of health-related limits and change limits from mixed distributions of clinical laboratory data.

Fair lineups are better than biased lineups and showups, but not because they increase underlying discriminability.

PubMed

Smith, Andrew M; Wells, Gary L; Lindsay, R C L; Penrod, Steven D

2017-04-01

Receiver Operating Characteristic (ROC) analysis has recently come in vogue for assessing the underlying discriminability and the applied utility of lineup procedures. Two primary assumptions underlie recommendations that ROC analysis be used to assess the applied utility of lineup procedures: (a) ROC analysis of lineups measures underlying discriminability, and (b) the procedure that produces superior underlying discriminability produces superior applied utility. These same assumptions underlie a recently derived diagnostic-feature detection theory, a theory of discriminability, intended to explain recent patterns observed in ROC comparisons of lineups. We demonstrate, however, that these assumptions are incorrect when ROC analysis is applied to lineups. We also demonstrate that a structural phenomenon of lineups, differential filler siphoning, and not the psychological phenomenon of diagnostic-feature detection, explains why lineups are superior to showups and why fair lineups are superior to biased lineups. In the process of our proofs, we show that computational simulations have assumed, unrealistically, that all witnesses share exactly the same decision criteria. When criterial variance is included in computational models, differential filler siphoning emerges. The result proves dissociation between ROC curves and underlying discriminability: Higher ROC curves for lineups than for showups and for fair than for biased lineups despite no increase in underlying discriminability. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A better way to evaluate remote monitoring programs in chronic disease care: receiver operating characteristic analysis.

PubMed

Brown Connolly, Nancy E

2014-12-01

This foundational study applies the process of receiver operating characteristic (ROC) analysis to evaluate utility and predictive value of a disease management (DM) model that uses RM devices for chronic obstructive pulmonary disease (COPD). The literature identifies a need for a more rigorous method to validate and quantify evidence-based value for remote monitoring (RM) systems being used to monitor persons with a chronic disease. ROC analysis is an engineering approach widely applied in medical testing, but that has not been evaluated for its utility in RM. Classifiers (saturated peripheral oxygen [SPO2], blood pressure [BP], and pulse), optimum threshold, and predictive accuracy are evaluated based on patient outcomes. Parametric and nonparametric methods were used. Event-based patient outcomes included inpatient hospitalization, accident and emergency, and home health visits. Statistical analysis tools included Microsoft (Redmond, WA) Excel(®) and MedCalc(®) (MedCalc Software, Ostend, Belgium) version 12 © 1993-2013 to generate ROC curves and statistics. Persons with COPD were monitored a minimum of 183 days, with at least one inpatient hospitalization within 12 months prior to monitoring. Retrospective, de-identified patient data from a United Kingdom National Health System COPD program were used. Datasets included biometric readings, alerts, and resource utilization. SPO2 was identified as a predictive classifier, with an optimal average threshold setting of 85-86%. BP and pulse were failed classifiers, and areas of design were identified that may improve utility and predictive capacity. Cost avoidance methodology was developed. RESULTS can be applied to health services planning decisions. Methods can be applied to system design and evaluation based on patient outcomes. This study validated the use of ROC in RM program evaluation.

Analysis of confidence level scores from an ROC study: comparison of three mammographic systems for detection of simulated calcifications

NASA Astrophysics Data System (ADS)

Lai, Chao-Jen; Shaw, Chris C.; Whitman, Gary J.; Yang, Wei T.; Dempsey, Peter J.

2005-04-01

The purpose of this study is to compare the detection performance of three different mammography systems: screen/film (SF) combination, a-Si/CsI flat-panel (FP-), and charge-coupled device (CCD-) based systems. A 5-cm thick 50% adipose/50% glandular breast tissue equivalent slab phantom was used to provide an uniform background. Calcium carbonate grains of three different size groups were used to simulate microcalcifications (MCs): 112-125, 125-140, and 140-150 μm overlapping with the uniform background. Calcification images were acquired with the three mammography systems. Digital images were printed on hardcopy films. All film images were displayed on a mammographic viewer and reviewed by 5 mammographers. The visibility of the MC was rated with a 5-point confidence rating scale for each detection task, including the negative controls. Scores were averaged over all readers for various detectors and size groups. Receiver operating characteristic (ROC) analysis was performed and the areas under the ROC curves (Az"s) were computed for various imaging conditions. The results shows that (1) the FP-based system performed significantly better than the SF and CCD-based systems for individual size groups using ROC analysis (2) the FP-based system also performed significantly better than the SF and CCD-based systems for individual size groups using averaged confidence scale, and (3) the results obtained from the Az"s were largely correlated with these from confidence level scores. However, the correlation varied slightly among different imaging conditions.

Effect of Edge-Preserving Adaptive Image Filter on Low-Contrast Detectability in CT Systems: Application of ROC Analysis

PubMed Central

Okumura, Miwa; Ota, Takamasa; Kainuma, Kazuhisa; Sayre, James W.; McNitt-Gray, Michael; Katada, Kazuhiro

2008-01-01

Objective. For the multislice CT (MSCT) systems with a larger number of detector rows, it is essential to employ dose-reduction techniques. As reported in previous studies, edge-preserving adaptive image filters, which selectively eliminate only the noise elements that are increased when the radiation dose is reduced without affecting the sharpness of images, have been developed. In the present study, we employed receiver operating characteristic (ROC) analysis to assess the effects of the quantum denoising system (QDS), which is an edge-preserving adaptive filter that we have developed, on low-contrast resolution, and to evaluate to what degree the radiation dose can be reduced while maintaining acceptable low-contrast resolution. Materials and Methods. The low-contrast phantoms (Catphan 412) were scanned at various tube current settings, and ROC analysis was then performed for the groups of images obtained with/without the use of QDS at each tube current to determine whether or not a target could be identified. The tube current settings for which the area under the ROC curve (Az value) was approximately 0.7 were determined for both groups of images with/without the use of QDS. Then, the radiation dose reduction ratio when QDS was used was calculated by converting the determined tube current to the radiation dose. Results. The use of the QDS edge-preserving adaptive image filter allowed the radiation dose to be reduced by up to 38%. Conclusion. The QDS was found to be useful for reducing the radiation dose without affecting the low-contrast resolution in MSCT studies. PMID:19043565

Acute respiratory distress syndrome in blunt trauma: identification of independent risk factors.

PubMed

Miller, Preston R; Croce, Martin A; Kilgo, Patrick D; Scott, John; Fabian, Timothy C

2002-10-01

Acute respiratory distress syndrome (ARDS) is a major contributor to morbidity and mortality in trauma patients. Although many injuries and conditions are believed to be associated with ARDS independent risk factors in trauma patients and their relative importance in development of the syndrome are undefined. The aim of this project is to identify independent risk factors for the development of ARDS in blunt trauma patients and to examine the contributions of each factor to ARDS development. Patients with ARDS were identified from the registry of a Level I trauma center over a 4.5-year period. Records were reviewed for demographics, injury characteristics, transfusion requirements, and hospital course. Variables examined included age >65 years, Injury Severity Score (ISS) >25, hypotension on admission (systolic blood pressure <90), significant metabolic acidosis (base deficit <-5.0), severe brain injury as shown by a Glasgow Coma Scale score (GCS) <8 on admission, 24-hour transfusion requirement >10 units packed red blood cells, pulmonary contusion (PC), femur fracture, and major infection (pneumonia, empyema, or intra-abdominal abscess). Both univariate and stepwise logistic regression were used to identify independent risk factors, and receiver operating characteristic curve (ROC) analysis was used to determine the relative contribution of each risk factor. A total of 4397 patients having sustained blunt trauma were admitted to the intensive care unit and survived >24 hours between October 1995 and May 2000. Of these patients 200 (4.5%) developed ARDS. All studied variables were significantly associated with ARDS in univariate analyses. Stepwise logistic regression, however, demonstrated age >65 years, ISS >25, hypotension on admission, 24-hour transfusion requirement >10 units, and pulmonary contusion as independent risk factors, whereas admission metabolic acidosis, femur fracture, infection, and severe brain injury were not. Using a model based on the logistic regression equation derived yields better than 80 per cent discrimination in ARDS patients. The risk factors providing the greatest contribution to ARDS development were ISS >25 (ROC area 0.72) and PC (ROC area 0.68) followed by large transfusion requirement (ROC area 0.56), admission hypotension (ROC area 0.57), and age >65 (ROC area 0.54). Independent risk factors for ARDS in blunt trauma include ISS >25, PC, age >65 years, hypotension on admission, and 24-hour transfusion requirement >10 units but not admission metabolic acidosis, femur fracture, infection, or severe brain injury. Assessment of these variables allows accurate estimate of risk in the majority of cases, and the most potent contributors to the predictive value of the model are ISS >25 and PC. Improvement in understanding of which patients are actually at risk may allow for advances in treatment as well as prevention in the future.

Rockall score in predicting outcomes of elderly patients with acute upper gastrointestinal bleeding

PubMed Central

Wang, Chang-Yuan; Qin, Jian; Wang, Jing; Sun, Chang-Yi; Cao, Tao; Zhu, Dan-Dan

2013-01-01

AIM: To validate the clinical Rockall score in predicting outcomes (rebleeding, surgery and mortality) in elderly patients with acute upper gastrointestinal bleeding (AUGIB). METHODS: A retrospective analysis was undertaken in 341 patients admitted to the emergency room and Intensive Care Unit of Xuanwu Hospital of Capital Medical University with non-variceal upper gastrointestinal bleeding. The Rockall scores were calculated, and the association between clinical Rockall scores and patient outcomes (rebleeding, surgery and mortality) was assessed. Based on the Rockall scores, patients were divided into three risk categories: low risk ≤ 3, moderate risk 3-4, high risk ≥ 4, and the percentages of rebleeding/death/surgery in each risk category were compared. The area under the receiver operating characteristic (ROC) curve was calculated to assess the validity of the Rockall system in predicting rebleeding, surgery and mortality of patients with AUGIB. RESULTS: A positive linear correlation between clinical Rockall scores and patient outcomes in terms of rebleeding, surgery and mortality was observed (r = 0.962, 0.955 and 0.946, respectively, P = 0.001). High clinical Rockall scores > 3 were associated with adverse outcomes (rebleeding, surgery and death). There was a significant correlation between high Rockall scores and the occurrence of rebleeding, surgery and mortality in the entire patient population (χ2 = 49.29, 23.10 and 27.64, respectively, P = 0.001). For rebleeding, the area under the ROC curve was 0.788 (95%CI: 0.726-0.849, P = 0.001); For surgery, the area under the ROC curve was 0.752 (95%CI: 0.679-0.825, P = 0.001) and for mortality, the area under the ROC curve was 0.787 (95%CI: 0.716-0.859, P = 0.001). CONCLUSION: The Rockall score is clinically useful, rapid and accurate in predicting rebleeding, surgery and mortality outcomes in elderly patients with AUGIB. PMID:23801840

Detection of common carotid artery stenosis using duplex ultrasonography: a validation study with computed tomographic angiography.

PubMed

Slovut, David P; Romero, Javier M; Hannon, Kathleen M; Dick, James; Jaff, Michael R

2010-01-01

Severe stenosis of the common carotid artery (CCA), while uncommon, is associated with increased risk of transient ischemic attack and stroke. To date, no validated duplex ultrasound criteria have been established for grading the severity of CCA stenosis. The goal of this study was to use receiver-operating curve (ROC) analysis with computed tomographic angiography as the reference standard to establish duplex ultrasound criteria for diagnosing >or=50% CCA stenosis. The study cohort included 64 patients (42 men, 22 women) with a mean age of 65 +/- 12 years (range, 16-89 years) who had CCA peak systolic velocity (PSV) >or=150 cm/sec and underwent computed tomographic angiography (CTA) of the cervical and intracerebral vessels within 1 month of the duplex examination. One study was excluded because the CTA was technically inadequate, whereas another was excluded because the patient underwent bilateral CCA stenting. The CCA ipsilateral to any of the following was excluded from the analysis: innominate artery occlusion (n = 1), previous stenting of the ICA or CCA (n = 7), carotid endarterectomy (n = 1), or carotid-to-carotid bypass (n = 1). Thus, the data set included 62 patients and 115 vessels. Bland-Altman analysis was used to examine the agreement between two measures of luminal reduction measured by CTA: percent diameter stenosis and percent area stenosis. Receiver operating characteristic (ROC) analysis was used to determine optimal PSV and EDV thresholds for diagnosing >or=50% CCA stenosis. Severity of CCA stenosis was <50% in 76 vessels, 50%-59% in eight, 60%-69% in eight, 70%-79% in nine, 80%-89% in three, 90%-99% in five, and occluded in six. Duplex ultrasonography identified six of six (100%) patients with 100% CCA occlusion by CTA. Bland-Altman analysis showed poor agreement between percent stenosis determined by vessel diameter compared with percent stenosis determined by reduction in lumen area. Therefore, subsequent analysis was performed using percent stenosis by area. ROC analysis of different PSV thresholds for detecting stenosis >or=50% showed that >182 cm/sec was the most accurate with a sensitivity of 64% and specificity of 88% (P < .0001). Sensitivity, specificity, and accuracy of carotid duplex were higher when the stenosis was located in the mid or distal aspects of the CCA (sensitivity 76%, specificity 89%, area under curve 0.84, P < .001) than in the intrathoracic and proximal segment of the artery (P = NS). ROC analysis of different EDV thresholds for detecting CCA stenosis >or=50% showed that >30 cm/sec was the most accurate with a sensitivity of 54% and a specificity of 74% (P < .0239). Duplex ultrasonography is highly sensitive, specific, and accurate for detecting CCA lesions in the mid and distal CCA. Use of peak systolic velocity may lead to improved detection of CCA disease and initiation of appropriate therapy to reduce the risk of stroke. Copyright 2010 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Relationship between CT densitometry with a slice thickness of 0.5 mm and audiometry in otosclerosis.

PubMed

Kawase, Setsuko; Naganawa, Shinji; Sone, Michihiko; Ikeda, Mitsuru; Ishigaki, Takeo

2006-06-01

The appropriate cutoff Hounsfield unit (HU) value for the diagnosis of otosclerosis was determined and the correlation between the bone conduction threshold and the findings of computed tomography (CT) densitometry investigated. CT images, 0.5-mm thick, were evaluated in 24 ears with otosclerosis and 19 control ears. Eight regions of interest were set around the otic capsule. The mean HU values in the area anterior to the oval window (A-OW) and anterior to the internal auditory canal (A-IAC) were significantly lower in otosclerosis than in controls. Based on receiver operating characteristic (ROC) analysis, the cutoff HU value in A-OW was determined to be 2,187.3 HU. The mean HU value in retrofenestral otosclerosis was significantly lower in the area A-OW, A-IAC and around the cochlea than in controls. Based on ROC analysis, the cutoff HU value in the latter was determined to be 2,045 HU. A statistically significant correlation was found between the density of the area A-OW and the hearing level at 500 and 1,000 Hz, and between the density of the area around the cochlea and the hearing level at most frequencies. These results suggest the semi-automated diagnosis of otosclerosis may be possible.

Clinical value of the neutrophil/lymphocyte ratio in diagnosing adult strangulated inguinal hernia.

PubMed

Zhou, Huanhao; Ruan, Xiaojiao; Shao, Xia; Huang, Xiaming; Fang, Guan; Zheng, Xiaofeng

2016-12-01

Diagnosis of incarcerated inguinal hernia (IIH) is not difficult, but currently, there are no diagnostic criteria that can be used to differentiate it from strangulated inguinal hernia (SIH). This research aimed to evaluate the clinical value of the neutrophil/lymphocyte ratio (NLR) in diagnosing SIH. We retrospectively analyzed 263 patients with IIH who had undergone emergency operation. The patients were divided into two groups according to IIH severity: group A, patients with pure IIH validated during operation as having no bowel ischemia; group B, patients with SIH validated during operation as having obvious bowel ischemia, including bowel necrosis. We statistically evaluated the relation between several clinical features and SIH. The accuracy of different indices was then evaluated and compared using receiver operating characteristic (ROC) curve analyses, and the corresponding cutoff values were calculated. Univariate analysis showed eight clinical features that were significantly different between the two groups. They were then subjected to multivariate analysis, which showed that the NLR, type of hernia, and incarcerated organ were significantly related to SIH. ROC curve analysis showed that the NLR had the largest area under the ROC curve. Among the different clinical features, the NLR appears to be the best index in diagnosing SIH. Copyright © 2016 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

An ROC-type measure of diagnostic accuracy when the gold standard is continuous-scale.

PubMed

Obuchowski, Nancy A

2006-02-15

ROC curves and summary measures of accuracy derived from them, such as the area under the ROC curve, have become the standard for describing and comparing the accuracy of diagnostic tests. Methods for estimating ROC curves rely on the existence of a gold standard which dichotomizes patients into disease present or absent. There are, however, many examples of diagnostic tests whose gold standards are not binary-scale, but rather continuous-scale. Unnatural dichotomization of these gold standards leads to bias and inconsistency in estimates of diagnostic accuracy. In this paper, we propose a non-parametric estimator of diagnostic test accuracy which does not require dichotomization of the gold standard. This estimator has an interpretation analogous to the area under the ROC curve. We propose a confidence interval for test accuracy and a statistical test for comparing accuracies of tests from paired designs. We compare the performance (i.e. CI coverage, type I error rate, power) of the proposed methods with several alternatives. An example is presented where the accuracies of two quick blood tests for measuring serum iron concentrations are estimated and compared.

Aedes aegypti Larval Indices and Risk for Dengue Epidemics

PubMed Central

Sanchez, Lizet; Vanlerberghe, Veerle; Alfonso, Lázara; Marquetti, María del Carmen; Guzman, María Guadalupe; Bisset, Juan; van der Stuyft, Patrick

2006-01-01

We assessed in a case-control study the test-validity of Aedes larval indices for the 2000 Havana outbreak. "Cases" were blocks where a dengue fever patient lived during the outbreak. "Controls" were randomly sampled blocks. Before, during, and after the epidemic, we calculated Breteau index (BI) and house index at the area, neighborhood, and block level. We constructed receiver operating characteristic (ROC) curves to determine their performance as predictors of dengue transmission. We observed a pronounced effect of the level of measurement. The BImax (maximum block BI in a radius of 100 m) at 2-month intervals had an area under the ROC curve of 71%. At a cutoff of 4.0, it significantly (odds ratio 6.00, p<0.05) predicted transmission with 78% sensitivity and 63% specificity. Analysis of BI at the local level, with human-defined boundaries, could be introduced in control programs to identify neighborhoods at high risk for dengue transmission. PMID:16704841

PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R.

PubMed

Grau, Jan; Grosse, Ivo; Keilwagen, Jens

2015-08-01

Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance. Here, we present the R-package PRROC, which allows for computing and visualizing both PR and ROC curves. In contrast to available R-packages, PRROC allows for computing PR and ROC curves and areas under these curves for soft-labeled data using a continuous interpolation between the points of PR curves. In addition, PRROC provides a generic plot function for generating publication-quality graphics of PR and ROC curves. © The Author 2015. Published by Oxford University Press.

A Serum Circulating miRNA Signature for Short-Term Risk of Progression to Active Tuberculosis Among Household Contacts.

PubMed

Duffy, Fergal J; Thompson, Ethan; Downing, Katrina; Suliman, Sara; Mayanja-Kizza, Harriet; Boom, W Henry; Thiel, Bonnie; Weiner Iii, January; Kaufmann, Stefan H E; Dover, Drew; Tabb, David L; Dockrell, Hazel M; Ottenhoff, Tom H M; Tromp, Gerard; Scriba, Thomas J; Zak, Daniel E; Walzl, Gerhard

2018-01-01

Biomarkers that predict who among recently Mycobacterium tuberculosis (MTB)-exposed individuals will progress to active tuberculosis are urgently needed. Intracellular microRNAs (miRNAs) regulate the host response to MTB and circulating miRNAs (c-miRNAs) have been developed as biomarkers for other diseases. We performed machine-learning analysis of c-miRNA measurements in the serum of adult household contacts (HHCs) of TB index cases from South Africa and Uganda and developed a c-miRNA-based signature of risk for progression to active TB. This c-miRNA-based signature significantly discriminated HHCs within 6 months of progression to active disease from HHCs that remained healthy in an independent test set [ROC area under the ROC curve (AUC) 0.74, progressors < 6 Mo to active TB and ROC AUC 0.66, up to 24 Mo to active TB], and complements the predictions of a previous cellular mRNA-based signature of TB risk.

Comparative study of contrast-enhanced ultrasound qualitative and quantitative analysis for identifying benign and malignant breast tumor lumps.

PubMed

Liu, Jian; Gao, Yun-Hua; Li, Ding-Dong; Gao, Yan-Chun; Hou, Ling-Mi; Xie, Ting

2014-01-01

To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

Ultrasound bladder wall thickness measurement in diagnosis of recurrent urinary tract infections and cystitis cystica in prepubertal girls.

PubMed

Milošević, Danko; Trkulja, Vladimir; Turudić, Daniel; Batinić, Danica; Spajić, Borislav; Tešović, Goran

2013-12-01

To evaluate urinary bladder wall thickness (BWT) assessed by ultrasound as a diagnostic tool for cystitis cystica. This was a 9-year prospective study comprising 120 prepubertal girls. Sixty subjects of whom half underwent cystoscopy represented cases while the other 60 (those with a single urinary tract infection and healthy subjects) represented controls. Based on receiver operating characteristics (ROC) analysis, BWT discriminated very well between cases and controls with area under the ROC curve close to 1.0. At the optimum cut-off defined at 3.9 mm, negative predictive value (NPV) was 100% leaving no probability of cystic cystitis with BWT <3.9 mm. Positive predictive value (PPV) was also very high (95.2%), indicating only around 4.82% probability of no cystic cystitis in patients with BWT values ≥3.9 mm. BWT could also distinguish between healthy subjects and those with a cured single urinary tract infection, although discriminatory properties were moderate (area under ROC 86.7%, PPV 78.8%, NPV 85.2%). Ultrasound mucosal bladder wall measurement is a non-invasive, simple and quite reliable method in diagnosis of cystitis cystica in prepubertal girls with recurrent urinary tract infections. Copyright © 2013 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Morphological and wavelet features towards sonographic thyroid nodules evaluation.

PubMed

Tsantis, Stavros; Dimitropoulos, Nikos; Cavouras, Dionisis; Nikiforidis, George

2009-03-01

This paper presents a computer-based classification scheme that utilized various morphological and novel wavelet-based features towards malignancy risk evaluation of thyroid nodules in ultrasonography. The study comprised 85 ultrasound images-patients that were cytological confirmed (54 low-risk and 31 high-risk). A set of 20 features (12 based on nodules boundary shape and 8 based on wavelet local maxima located within each nodule) has been generated. Two powerful pattern recognition algorithms (support vector machines and probabilistic neural networks) have been designed and developed in order to quantify the power of differentiation of the introduced features. A comparative study has also been held, in order to estimate the impact speckle had onto the classification procedure. The diagnostic sensitivity and specificity of both classifiers was made by means of receiver operating characteristics (ROC) analysis. In the speckle-free feature set, the area under the ROC curve was 0.96 for the support vector machines classifier whereas for the probabilistic neural networks was 0.91. In the feature set with speckle, the corresponding areas under the ROC curves were 0.88 and 0.86 respectively for the two classifiers. The proposed features can increase the classification accuracy and decrease the rate of missing and misdiagnosis in thyroid cancer control.

Receiver operating characteristic analysis. Application to the study of quantum fluctuation effects in optic nerve of Rana pipiens

PubMed Central

1975-01-01

Receiver operating characteristic (ROC) analysis of nerve messages is described. The hypothesis that quantum fluctuations provide the only limit to the ability of frog ganglion cells to signal luminance change information is examined using ROC analysis. In the context of ROC analysis, the quantum fluctuation hypothesis predicts (a) the detectability of a luminance change signal should rise proportionally to the size of the change, (b) detectability should decrease as the square root of background, an implication of which is the deVries-Rose law, and (c) ROC curves should exhibit a shape particular to underlying Poisson distributions. Each of these predictions is confirmed for the responses of dimming ganglion cells to brief luminance decrements at scotopic levels, but none could have been tested using classical nerve message analysis procedures. PMID:172597

Setting Cut Scores on an EFL Placement Test Using the Prototype Group Method: A Receiver Operating Characteristic (ROC) Analysis

ERIC Educational Resources Information Center

Eckes, Thomas

2017-01-01

This paper presents an approach to standard setting that combines the prototype group method (PGM; Eckes, 2012) with a receiver operating characteristic (ROC) analysis. The combined PGM-ROC approach is applied to setting cut scores on a placement test of English as a foreign language (EFL). To implement the PGM, experts first named learners whom…

Wavelet coherence analysis: A new approach to distinguish organic and functional tremor types.

PubMed

Kramer, G; Van der Stouwe, A M M; Maurits, N M; Tijssen, M A J; Elting, J W J

2018-01-01

To distinguish tremor subtypes using wavelet coherence analysis (WCA). WCA enables to detect variations in coherence and phase difference between two signals over time and might be especially useful in distinguishing functional from organic tremor. In this pilot study, polymyography recordings were studied retrospectively of 26 Parkinsonian (PT), 26 functional (FT), 26 essential (ET), and 20 enhanced physiological (EPT) tremor patients. Per patient one segment of 20 s in duration, in which tremor was present continuously in the same posture, was selected. We studied several coherence and phase related parameters, and analysed all possible muscle combinations of the flexor and extensor muscles of the upper and fore arm. The area under the receiver operating characteristic curve (AUC-ROC) was applied to compare WCA and standard coherence analysis to distinguish tremor subtypes. The percentage of time with significant coherence (PTSC) and the number of periods without significant coherence (NOV) proved the most discriminative parameters. FT could be discriminated from organic (PT, ET, EPT) tremor by high NOV (31.88 vs 21.58, 23.12 and 10.20 respectively) with an AUC-ROC of 0.809, while standard coherence analysis resulted in an AUC-ROC of 0.552. EMG-EMG WCA analysis might provide additional variables to distinguish functional from organic tremor. WCA might prove to be of additional value to discriminate between tremor types. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Development and validation of a tool for identifying women with low bone mineral density and low-impact fractures: the São Paulo Osteoporosis Risk Index (SAPORI).

PubMed

Pinheiro, M M; Reis Neto, E T; Machado, F S; Omura, F; Szejnfeld, J; Szejnfeld, V L

2012-04-01

The performance of the São Paulo Osteoporosis Risk Index (SAPORI) was tested in 1,915 women from the original cohort, São Paulo Osteoporosis Study (SAPOS) (N = 4332). This new tool was able to identify women with low bone density (spine and hip) and low-impact fracture, with an area under the receiving operator curve (ROC) of 0.831, 0.724, and 0.689, respectively. A number of studies have demonstrated the clinical relevance of risk factors for identifying individuals at risk of fracture (Fx) and osteoporosis (OP). The SAPOS is an epidemiological study for the assessment of risk factors for Fx and low bone density in women from the community of the metropolitan area of São Paulo, Brazil. The aim of the present study was to develop and validate a tool for identifying women at higher risk for OP and low-impact Fx. A total of 4,332 pre-, peri-, and postmenopausal women were analyzed through a questionnaire addressing risk factors for OP and Fx. All of them performed bone densitometry at the lumbar spine and proximal femur (DPX NT, GE-Lunar). Following the identification of the main risk factors for OP and Fx through multivariate and logistic regression, respectively, the SAPORI was designed and subsequently validated on a second cohort of 1,915 women from the metropolitan community of São Paulo. The performance of this tool was assessed through ROC analysis. The main and significant risk factors associated with low bone density and low-impact Fx were low body weight, advanced age, Caucasian ethnicity, family history of hip Fx, current smoking, and chronic use of glucocorticosteroids. Hormonal replacement therapy and regular physical activity in the previous year played a protective role (p < 0.05). After the statistical adjustments, the SAPORI was able to identify women with low bone density (T-score ≤ -2 standard deviations) in the femur, with 91.4% sensitivity, 52% specificity, and an area under the ROC of 0.831 (p < 0.001). At the lumbar spine, the performance was similar (81.5% sensitivity, 50% specificity, and area under ROC of 0.724; p < 0.001). Regarding the identification of low-impact Fx, the sensitivity was 71%, the specificity was 52%, and the area under the ROC was 0.689 (p < 0.001). The SAPORI is a simple, useful, fast, practice, and valid tool for identifying women at higher risk for low bone density and osteoporotic fractures.

Bronchial lavage P 16INK4A gene promoter methylation and lung cancer diagnosis: A meta-analysis.

PubMed

Yifan, D; Qun, L; Yingshuang, H; Xulin, L; Jianjun, W; Qian, M; Yuman, Yu; Zhaoyang, R

2015-12-01

To evaluate the diagnostic value of bronchial lavage P16INK4A promoter methylation and lung cancer. The databases of PubMed, Medline, China National Knowledge Infrastructure, and Wanfang were electronically searched by two reviewers to find the suitable studies related to the association between P16INK4A promoter methylation and lung cancer. The P16INK4A promoter methylation rate was extracted from each included individual study. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic ROC curve of bronchial lavage P16INK4Aas a biomarker for diagnosis of lung cancer were pooled by stata 11.0 software (Stata Corporation, College Station, TX, USA). At last, 10 publications were included in this meta-analysis. Of the included 10 studies, five are published in English with relatively high quality and other five papers published in Chinese have relatively low quality. The pooled sensitivity and specificity of bronchial lavage P16INK4A promoter methylation for lung cancer diagnosis were 0.61 (95% confidence interval [CI]: 0.57-0.65) and 0.81 (95% CI: 0.78-0.85), respectively, with random effect model. The ROC curve were calculated and drawn according to Bayes' theorem by stata 11.0 software. The systematic area under the ROC was 0.72 (95% CI: 0.68-0.76), which indicated that the diagnostic value of bronchial lavage P16INK4A promoter methylation for lung cancer was relatively high. Moreover, no significant publication bias was existed in this meta-analysis (t = 0.69, P > 0.05). Bronchial lavage P16INK4A promoter methylation can be a potential biomarker for diagnosis of lung cancer.

The role of stenosis ratio as a predictor of surgical satisfaction in patients with lumbar spinal canal stenosis: a receiver-operator characteristic (ROC) curve analysis.

PubMed

Mohammadi, Hassanreza R; Azimi, Parisa; Benzel, Edward C; Shahzadi, Sohrab; Azhari, Shirzad

2016-09-01

The aim of this study was to elucidate independent factors that predict surgical satisfaction in lumbar spinal canal stenosis (LSCS) patients. Patients who underwent surgery were grouped based on the age, gender, duration of symptoms, walking distance, Neurogenic Claudication Outcome Score (NCOS) and the stenosis ratio (SR) described by Lurencin. We recorded on 2-year patient satisfaction using standardized measure. The optimal cut-off points in SR, NCOS and walking distance for predicting surgical satisfaction were estimated from sensitivity and specificity calculations and receiver operator characteristic (ROC) curves. One hundred fifty consecutive patients (51 male, 99 female, mean age 62.4±10.9 years) were followed up for 34±13 months (range 24-49). One, two, three and four level stenosis was observed in 10.7%, 39.3%, 36.0 % and 14.0% of patients, respectively. Post-surgical satisfaction was 78.5% at the 2 years follow up. In ROC curve analysis, the asymptotic significance is less than 0.05 in SR and the optimal cut-off value of SR to predict worsening surgical satisfaction was measured as more than 0.52, with 85.4% sensitivity and 77.4% specificity (AUC 0.798, 95% CI 0.73-0.90; P<0.01). The present study suggests that the SR, with a cut-off set a 0.52 cross-sectional area, may be superior to walking distance and NCOS in patients with degenerative lumbar stenosis considered for surgical treatment. Using a ROC curve analysis, a radiological feature, the SR, demonstrated superiority in predicting patient satisfaction, compared to functional and clinical characteristics such as walking distance and NCOS.

Comparison of FIB-4 index and aspartate aminotransferase to platelet ratio index on carcinogenesis in chronic hepatitis B treated with entecavir

PubMed Central

Nishikawa, Hiroki; Nishijima, Norihiro; Enomoto, Hirayuki; Sakamoto, Azusa; Nasu, Akihiro; Komekado, Hideyuki; Nishimura, Takashi; Kita, Ryuichi; Kimura, Toru; Iijima, Hiroko; Nishiguchi, Shuhei; Osaki, Yukio

2017-01-01

Aims: We sought to compare the effects of FIB-4 index and aspartate aminotransferase to platelet ratio index (APRI) on hepatocellular carcinoma (HCC) incidence in chronic hepatitis B (CHB) patients undergoing entecavir (ETV) therapy. Patient and methods: A total of 338 nucleosides analogue therapy naïve CHB patients initially treated with ETV were analyzed. The optimal cutoff points in each continuous variable were determined by receiver operating curve (ROC) analysis. The effects of FIB-4 index and APRI on HCC incidence were compared using time-dependent ROC analysis and factors linked to HCC incidence were also examined using univariate and multivariate analyses. Results: There were 215 males and 123 females with the median age of 52 years and the median baseline HBV-DNA level of 6.6 log copies/ml. The median follow-up interval after the initiation of ETV therapy was 4.99 years. During the follow-up period, 33 patients (9.8%) developed HCC. The 3-, 5- 7-year cumulative HCC incidence rates in all cases were 4.4%, 9.2% and 13.5%, respectively. In the multivariate analysis, FIB-4 index revealed to be an independent predictor associated with HCC incidence, while APRI was not. In the time-dependent ROC analyses for all cases and for all subgroups analyses stratified by viral status or cirrhosis status, all area under the ROCs in each time point (2-, 3-, 4-, 5-, 6-, and 7-year) of FIB-4 index were higher than those of APRI. Conclusion: FIB-4 index rather than APRI can be a useful predictor associated with HCC development for CHB patients undergoing ETV therapy. PMID:28243319

Comparison of FIB-4 index and aspartate aminotransferase to platelet ratio index on carcinogenesis in chronic hepatitis B treated with entecavir.

PubMed

Nishikawa, Hiroki; Nishijima, Norihiro; Enomoto, Hirayuki; Sakamoto, Azusa; Nasu, Akihiro; Komekado, Hideyuki; Nishimura, Takashi; Kita, Ryuichi; Kimura, Toru; Iijima, Hiroko; Nishiguchi, Shuhei; Osaki, Yukio

2017-01-01

We sought to compare the effects of FIB-4 index and aspartate aminotransferase to platelet ratio index (APRI) on hepatocellular carcinoma (HCC) incidence in chronic hepatitis B (CHB) patients undergoing entecavir (ETV) therapy. A total of 338 nucleosides analogue therapy naïve CHB patients initially treated with ETV were analyzed. The optimal cutoff points in each continuous variable were determined by receiver operating curve (ROC) analysis. The effects of FIB-4 index and APRI on HCC incidence were compared using time-dependent ROC analysis and factors linked to HCC incidence were also examined using univariate and multivariate analyses. There were 215 males and 123 females with the median age of 52 years and the median baseline HBV-DNA level of 6.6 log copies/ml. The median follow-up interval after the initiation of ETV therapy was 4.99 years. During the follow-up period, 33 patients (9.8%) developed HCC. The 3-, 5- 7-year cumulative HCC incidence rates in all cases were 4.4%, 9.2% and 13.5%, respectively. In the multivariate analysis, FIB-4 index revealed to be an independent predictor associated with HCC incidence, while APRI was not. In the time-dependent ROC analyses for all cases and for all subgroups analyses stratified by viral status or cirrhosis status, all area under the ROCs in each time point (2-, 3-, 4-, 5-, 6-, and 7-year) of FIB-4 index were higher than those of APRI. FIB-4 index rather than APRI can be a useful predictor associated with HCC development for CHB patients undergoing ETV therapy.

Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.

The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer's disease.

PubMed

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2013-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.

The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer’s Disease

PubMed Central

Wu, Xia; Li, Juan; Ayutyanont, Napatkamon; Protas, Hillary; Jagust, William; Fleisher, Adam; Reiman, Eric; Yao, Li; Chen, Kewei

2014-01-01

Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer’s disease (AD) studies, the single-index-based ROC underutilizes all available information. For a long time, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis. PMID:23702553

Development and Validation of a Novel Scoring System for Predicting Technical Success of Chronic Total Occlusion Percutaneous Coronary Interventions: The PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) Score.

PubMed

Christopoulos, Georgios; Kandzari, David E; Yeh, Robert W; Jaffer, Farouc A; Karmpaliotis, Dimitri; Wyman, Michael R; Alaswad, Khaldoon; Lombardi, William; Grantham, J Aaron; Moses, Jeffrey; Christakopoulos, Georgios; Tarar, Muhammad Nauman J; Rangan, Bavana V; Lembo, Nicholas; Garcia, Santiago; Cipher, Daisha; Thompson, Craig A; Banerjee, Subhash; Brilakis, Emmanouil S

2016-01-11

This study sought to develop a novel parsimonious score for predicting technical success of chronic total occlusion (CTO) percutaneous coronary intervention (PCI) performed using the hybrid approach. Predicting technical success of CTO PCI can facilitate clinical decision making and procedural planning. We analyzed clinical and angiographic parameters from 781 CTO PCIs included in PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) using a derivation and validation cohort (2:1 sampling ratio). Variables with strong association with technical success in multivariable analysis were assigned 1 point, and a 4-point score was developed from summing all points. The PROGRESS CTO score was subsequently compared with the J-CTO (Multicenter Chronic Total Occlusion Registry in Japan) score in the validation cohort. Technical success was 92.9%. On multivariable analysis, factors associated with technical success included proximal cap ambiguity (beta coefficient [b] = 0.88), moderate/severe tortuosity (b = 1.18), circumflex artery CTO (b = 0.99), and absence of "interventional" collaterals (b = 0.88). The resulting score demonstrated good calibration and discriminatory capacity in the derivation (Hosmer-Lemeshow chi-square = 2.633; p = 0.268, and receiver-operator characteristic [ROC] area = 0.778) and validation (Hosmer-Lemeshow chi-square = 5.333; p = 0.070, and ROC area = 0.720) subset. In the validation cohort, the PROGRESS CTO and J-CTO scores performed similarly in predicting technical success (ROC area 0.720 vs. 0.746, area under the curve difference = 0.026, 95% confidence interval = -0.093 to 0.144). The PROGRESS CTO score is a novel useful tool for estimating technical success in CTO PCI performed using the hybrid approach. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

PubMed

Yu, Jihnhee; Yang, Luge; Vexler, Albert; Hutson, Alan D

2016-06-15

The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

"Textural analysis of multiparametric MRI detects transition zone prostate cancer".

PubMed

Sidhu, Harbir S; Benigno, Salvatore; Ganeshan, Balaji; Dikaios, Nikos; Johnston, Edward W; Allen, Clare; Kirkham, Alex; Groves, Ashley M; Ahmed, Hashim U; Emberton, Mark; Taylor, Stuart A; Halligan, Steve; Punwani, Shonit

2017-06-01

To evaluate multiparametric-MRI (mpMRI) derived histogram textural-analysis parameters for detection of transition zone (TZ) prostatic tumour. Sixty-seven consecutive men with suspected prostate cancer underwent 1.5T mpMRI prior to template-mapping-biopsy (TPM). Twenty-six men had 'significant' TZ tumour. Two radiologists in consensus matched TPM to the single axial slice best depicting tumour, or largest TZ diameter for those with benign histology, to define single-slice whole TZ-regions-of-interest (ROIs). Textural-parameter differences between single-slice whole TZ-ROI containing significant tumour versus benign/insignificant tumour were analysed using Mann Whitney U test. Diagnostic accuracy was assessed by receiver operating characteristic area under curve (ROC-AUC) analysis cross-validated with leave-one-out (LOO) analysis. ADC kurtosis was significantly lower (p < 0.001) in TZ containing significant tumour with ROC-AUC 0.80 (LOO-AUC 0.78); the difference became non-significant following exclusion of significant tumour from single-slice whole TZ-ROI (p = 0.23). T1-entropy was significantly lower (p = 0.004) in TZ containing significant tumour with ROC-AUC 0.70 (LOO-AUC 0.66) and was unaffected by excluding significant tumour from TZ-ROI (p = 0.004). Combining these parameters yielded ROC-AUC 0.86 (LOO-AUC 0.83). Textural features of the whole prostate TZ can discriminate significant prostatic cancer through reduced kurtosis of the ADC-histogram where significant tumour is included in TZ-ROI and reduced T1 entropy independent of tumour inclusion. • MR textural features of prostate transition zone may discriminate significant prostatic cancer. • Transition zone (TZ) containing significant tumour demonstrates a less peaked ADC histogram. • TZ containing significant tumour reveals higher post-contrast T1-weighted homogeneity. • The utility of MR texture analysis in prostate cancer merits further investigation.

Recollection is a continuous process: Evidence from plurality memory receiver operating characteristics.

PubMed

Slotnick, Scott D; Jeye, Brittany M; Dodson, Chad S

2016-01-01

Is recollection a continuous/graded process or a threshold/all-or-none process? Receiver operating characteristic (ROC) analysis can answer this question as the continuous model and the threshold model predict curved and linear recollection ROCs, respectively. As memory for plurality, an item's previous singular or plural form, is assumed to rely on recollection, the nature of recollection can be investigated by evaluating plurality memory ROCs. The present study consisted of four experiments. During encoding, words (singular or plural) or objects (single/singular or duplicate/plural) were presented. During retrieval, old items with the same plurality or different plurality were presented. For each item, participants made a confidence rating ranging from "very sure old", which was correct for same plurality items, to "very sure new", which was correct for different plurality items. Each plurality memory ROC was the proportion of same versus different plurality items classified as "old" (i.e., hits versus false alarms). Chi-squared analysis revealed that all of the plurality memory ROCs were adequately fit by the continuous unequal variance model, whereas none of the ROCs were adequately fit by the two-high threshold model. These plurality memory ROC results indicate recollection is a continuous process, which complements previous source memory and associative memory ROC findings.

Simultaneous detection of antibodies to five Actinobacillus pleuropneumoniae serovars using bead-based multiplex analysis.

PubMed

Berger, Sanne Schou; Lauritsen, Klara Tølbøll; Boas, Ulrik; Lind, Peter; Andresen, Lars Ole

2017-11-01

We developed and made a preliminary validation of a bead-based multiplexed immunoassay for simultaneous detection of porcine serum antibodies to Actinobacillus pleuropneumoniae serovars 1, 2, 6, 7, and 12. Magnetic fluorescent beads were coupled with A. pleuropneumoniae antigens and tested with a panel of serum samples from experimentally infected pigs and with serum samples from uninfected and naturally infected pigs. The multiplex assay was compared to in-house ELISAs and complement fixation (CF) tests, which have been used for decades as tools for herd classification in the Danish Specific Pathogen Free system. Assay specificities and sensitivities as well as the corresponding cutoff values were determined using receiver operating characteristic (ROC) curve analysis, and the A. pleuropneumoniae multiplex assay showed good correlation with the in-house ELISAs and CF tests with areas under ROC curves ≥ 0.988. Benefits of multiplexed assays compared to ELISAs and CF tests include reduced serum sample volumes needed for analysis, less labor, and shorter assay time.

Utility of the Military Acute Concussion Evaluation as a screening tool for mild traumatic brain injury in a civilian trauma population.

PubMed

Stone, Melvin E; Safadjou, Saman; Farber, Benjamin; Velazco, Nerissa; Man, Jianliang; Reddy, Srinivas H; Todor, Roxanne; Teperman, Sheldon

2015-07-01

Mild traumatic brain injury (mTBI) constitutes 75% of more than 1.5 million traumatic brain injuries annually. There exists no consensus on point-of-care screening for mTBI. The Military Acute Concussion Evaluation (MACE) is a quick and easy test used by the US Army to screen for mTBI; however, its utility in civilian trauma is unclear. It has two parts: a history section and the Standardized Assessment of Concussion (SAC) score (0-30) previously validated in sports injury. As a performance improvement project, our institution sought to evaluate the MACE as a concussion screening tool that could be used by housestaff in a general civilian trauma population. From June 2013 to May 2014, patients 18 years to 65 years old with suspected concussion were given the MACE within 72 hours of admission to our urban Level I trauma center. Patients with a positive head computed tomography were excluded. Demographic data and MACE scores were recorded in prospect. Concussion was defined as loss of consciousness and/or posttraumatic amnesia; concussed patients were compared with those nonconcussed. Sensitivity and specificity for each respective MACE score were used to plot a receiver operating characteristic (ROC) curve. An ROC curve area of 0.8 was set as the benchmark for a good screening test to distinguish concussion from nonconcussion. There were 84 concussions and 30 nonconcussed patients. Both groups were similar; however, the concussion group had a lower mean MACE score than the nonconcussed patients. Data analysis demonstrated the sensitivity and specificity of a range of MACE scores used to generate an ROC curve area of only 0.65. The MACE showed a lower mean score for individuals with concussion, defined by loss of consciousness and/or posttraumatic amnesia. However, the ROC curve area of 0.65 highly suggests that MACE alone would be a poor screening test for mTBI in a general civilian trauma population. Diagnostic study, level II.

A diagnostic model for the detection of sensitization to wheat allergens was developed and validated in bakery workers.

PubMed

Suarthana, Eva; Vergouwe, Yvonne; Moons, Karel G; de Monchy, Jan; Grobbee, Diederick; Heederik, Dick; Meijer, Evert

2010-09-01

To develop and validate a prediction model to detect sensitization to wheat allergens in bakery workers. The prediction model was developed in 867 Dutch bakery workers (development set, prevalence of sensitization 13%) and included questionnaire items (candidate predictors). First, principal component analysis was used to reduce the number of candidate predictors. Then, multivariable logistic regression analysis was used to develop the model. Internal validation and extent of optimism was assessed with bootstrapping. External validation was studied in 390 independent Dutch bakery workers (validation set, prevalence of sensitization 20%). The prediction model contained the predictors nasoconjunctival symptoms, asthma symptoms, shortness of breath and wheeze, work-related upper and lower respiratory symptoms, and traditional bakery. The model showed good discrimination with an area under the receiver operating characteristic (ROC) curve area of 0.76 (and 0.75 after internal validation). Application of the model in the validation set gave a reasonable discrimination (ROC area=0.69) and good calibration after a small adjustment of the model intercept. A simple model with questionnaire items only can be used to stratify bakers according to their risk of sensitization to wheat allergens. Its use may increase the cost-effectiveness of (subsequent) medical surveillance.

Quantitative analysis of iris parameters in keratoconus patients using optical coherence tomography.

PubMed

Bonfadini, Gustavo; Arora, Karun; Vianna, Lucas M; Campos, Mauro; Friedman, David; Muñoz, Beatriz; Jun, Albert S

2015-01-01

To investigate the relationship between quantitative iris parameters and the presence of keratoconus. Cross-sectional observational study that included 15 affected eyes of 15 patients with keratoconus and 26 eyes of 26 normal age- and sex-matched controls. Iris parameters (area, thickness, and pupil diameter) of affected and unaffected eyes were measured under standardized light and dark conditions using anterior segment optical coherence tomography (AS-OCT). To identify optimal iris thickness cutoff points to maximize the sensitivity and specificity when discriminating keratoconus eyes from normal eyes, the analysis included the use of receiver operating characteristic (ROC) curves. Iris thickness and area were lower in keratoconus eyes than in normal eyes. The mean thickness at the pupillary margin under both light and dark conditions was found to be the best parameter for discriminating normal patients from keratoconus patients. Diagnostic performance was assessed by the area under the ROC curve (AROC), which had a value of 0.8256 with 80.0% sensitivity and 84.6% specificity, using a cutoff of 0.4125 mm. The sensitivity increased to 86.7% when a cutoff of 0.4700 mm was used. In our sample, iris thickness was lower in keratoconus eyes than in normal eyes. These results suggest that tomographic parameters may provide novel adjunct approaches for keratoconus screening.

Optimizing area under the ROC curve using semi-supervised learning

PubMed Central

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M.

2014-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.1 PMID:25395692

Developing and Testing a Model to Predict Outcomes of Organizational Change

PubMed Central

Gustafson, David H; Sainfort, François; Eichler, Mary; Adams, Laura; Bisognano, Maureen; Steudel, Harold

2003-01-01

Objective To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. Data Sources Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and the Netherlands collected between 1996 and 2000 for validation. Methods A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. Data Collection For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. Results Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. Conclusions A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted. PMID:12785571

Optimizing area under the ROC curve using semi-supervised learning.

PubMed

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

2015-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

PubMed

Guo, Jin-Cheng; Wu, Yang; Chen, Yang; Pan, Feng; Wu, Zhi-Yong; Zhang, Jia-Sheng; Wu, Jian-Yi; Xu, Xiu-E; Zhao, Jian-Mei; Li, En-Min; Zhao, Yi; Xu, Li-Yan

2018-04-09

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients. The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis.

PubMed

Linden, Ariel

2006-04-01

Diagnostic or predictive accuracy concerns are common in all phases of a disease management (DM) programme, and ultimately play an influential role in the assessment of programme effectiveness. Areas, such as the identification of diseased patients, predictive modelling of future health status and costs and risk stratification, are just a few of the domains in which assessment of accuracy is beneficial, if not critical. The most commonly used analytical model for this purpose is the standard 2 x 2 table method in which sensitivity and specificity are calculated. However, there are several limitations to this approach, including the reliance on a single defined criterion or cut-off for determining a true-positive result, use of non-standardized measurement instruments and sensitivity to outcome prevalence. This paper introduces the receiver operator characteristic (ROC) analysis as a more appropriate and useful technique for assessing diagnostic and predictive accuracy in DM. Its advantages include; testing accuracy across the entire range of scores and thereby not requiring a predetermined cut-off point, easily examined visual and statistical comparisons across tests or scores, and independence from outcome prevalence. Therefore the implementation of ROC as an evaluation tool should be strongly considered in the various phases of a DM programme.

Real-time Raman spectroscopy for automatic in vivo skin cancer detection: an independent validation.

PubMed

Zhao, Jianhua; Lui, Harvey; Kalia, Sunil; Zeng, Haishan

2015-11-01

In a recent study, we have demonstrated that real-time Raman spectroscopy could be used for skin cancer diagnosis. As a translational study, the objective of this study is to validate previous findings through a completely independent clinical test. In total, 645 confirmed cases were included in the analysis, including a cohort of 518 cases from a previous study, and an independent cohort of 127 new cases. Multi-variant statistical data analyses including principal component with general discriminant analysis (PC-GDA) and partial least squares (PLS) were used separately for lesion classification, which generated similar results. When the previous cohort (n = 518) was used as training and the new cohort (n = 127) was used as testing, the area under the receiver operating characteristic curve (ROC AUC) was found to be 0.889 (95 % CI 0.834-0.944; PLS); when the two cohorts were combined, the ROC AUC was 0.894 (95 % CI 0.870-0.918; PLS) with the narrowest confidence intervals. Both analyses were comparable to the previous findings, where the ROC AUC was 0.896 (95 % CI 0.846-0.946; PLS). The independent study validates that real-time Raman spectroscopy could be used for automatic in vivo skin cancer diagnosis with good accuracy.

Receiver Operating Characteristic Curve Analysis of Beach Water Quality Indicator Variables

PubMed Central

Morrison, Ann Michelle; Coughlin, Kelly; Shine, James P.; Coull, Brent A.; Rex, Andrea C.

2003-01-01

Receiver operating characteristic (ROC) curve analysis is a simple and effective means to compare the accuracies of indicator variables of bacterial beach water quality. The indicator variables examined in this study were previous day's Enterococcus density and antecedent rainfall at 24, 48, and 96 h. Daily Enterococcus densities and 15-min rainfall values were collected during a 5-year (1996 to 2000) study of four Boston Harbor beaches. The indicator variables were assessed for their ability to correctly classify water as suitable or unsuitable for swimming at a maximum threshold Enterococcus density of 104 CFU/100 ml. Sensitivity and specificity values were determined for each unique previous day's Enterococcus density and antecedent rainfall volume and used to construct ROC curves. The area under the ROC curve was used to compare the accuracies of the indicator variables. Twenty-four-hour antecedent rainfall classified elevated Enterococcus densities more accurately than previous day's Enterococcus density (P = 0.079). An empirically derived threshold for 48-h antecedent rainfall, corresponding to a sensitivity of 0.75, was determined from the 1996 to 2000 data and evaluated to ascertain if the threshold would produce a 0.75 sensitivity with independent water quality data collected in 2001 from the same beaches. PMID:14602593

Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China

NASA Astrophysics Data System (ADS)

Zhang, Nannan; Zhou, Kefa; Du, Xishihui

2017-04-01

Mineral prospectivity mapping (MPM) is a multi-step process that ranks promising target areas for further exploration. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are knowledge-driven MPM approaches. In this study, both approaches were used for data processing, based on which MPM was performed for porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang. The results of the two methods were then compared. The two methods combined expert experience and the Studentized contrast (S(C)) values of the weights-of-evidence approach to calculate the weights of 15 layers, and these layers were then integrated by the gamma operator (γ). Through prediction-area (P-A) plot analysis, the optimal γ for fuzzy logic and fuzzy AHP was determined as 0.95 and 0.93, respectively. The thresholds corresponding to different levels of metallogenic probability were defined via concentration-area (C-A) fractal analysis. The prediction performances of the two methods were compared on this basis. The results showed that in MPM based on fuzzy logic, the area under the receiver operating characteristic (ROC) curve was 0.806 and 81.48% of the known deposits were predicted, whereas in MPM based on fuzzy AHP, the area under the ROC curve was 0.862 and 92.59% of the known deposits were predicted. Therefore, prediction based on fuzzy AHP is more accurate and can provide directions for future prospecting.

Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

NASA Astrophysics Data System (ADS)

Lederman, Dror; Zheng, Bin; Wang, Xingwei; Wang, Xiao Hui; Gur, David

2011-03-01

We have developed a multi-probe resonance-frequency electrical impedance spectroscope (REIS) system to detect breast abnormalities. Based on assessing asymmetry in REIS signals acquired between left and right breasts, we developed several machine learning classifiers to classify younger women (i.e., under 50YO) into two groups of having high and low risk for developing breast cancer. In this study, we investigated a new method to optimize performance based on the area under a selected partial receiver operating characteristic (ROC) curve when optimizing an artificial neural network (ANN), and tested whether it could improve classification performance. From an ongoing prospective study, we selected a dataset of 174 cases for whom we have both REIS signals and diagnostic status verification. The dataset includes 66 "positive" cases recommended for biopsy due to detection of highly suspicious breast lesions and 108 "negative" cases determined by imaging based examinations. A set of REIS-based feature differences, extracted from the two breasts using a mirror-matched approach, was computed and constituted an initial feature pool. Using a leave-one-case-out cross-validation method, we applied a genetic algorithm (GA) to train the ANN with an optimal subset of features. Two optimization criteria were separately used in GA optimization, namely the area under the entire ROC curve (AUC) and the partial area under the ROC curve, up to a predetermined threshold (i.e., 90% specificity). The results showed that although the ANN optimized using the entire AUC yielded higher overall performance (AUC = 0.83 versus 0.76), the ANN optimized using the partial ROC area criterion achieved substantially higher operational performance (i.e., increasing sensitivity level from 28% to 48% at 95% specificity and/ or from 48% to 58% at 90% specificity).

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

PubMed

Karaismailoğlu, Eda; Dikmen, Zeliha Günnur; Akbıyık, Filiz; Karaağaoğlu, Ahmet Ergun

2018-04-30

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB) are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, time- dependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where disease status can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. We also obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A web tool was developed to estimate time intervals in risk. Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath were included. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac death and to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significant biomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youden index was used to determine optimal cut-off values. Results : Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves of myoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 during the first 180 h. Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to be statistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web tool can be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

Local variance for multi-scale analysis in geomorphometry.

PubMed

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-07-15

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements.

Local variance for multi-scale analysis in geomorphometry

PubMed Central

Drăguţ, Lucian; Eisank, Clemens; Strasser, Thomas

2011-01-01

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters derived from a DEM; 2) calculating LV as the average standard deviation (SD) within a 3 × 3 moving window for each scale level; 3) calculating the rate of change of LV (ROC-LV) from one level to another, and 4) plotting values so obtained against scale levels. We interpreted peaks in the ROC-LV graphs as markers of scale levels where cells or segments match types of pattern elements characterized by (relatively) equal degrees of homogeneity. The proposed method has been applied to LiDAR DEMs in two test areas different in terms of roughness: low relief and mountainous, respectively. For each test area, scale levels for slope gradient, plan, and profile curvatures were produced at constant increments with either resampling (cell-based) or image segmentation (object-based). Visual assessment revealed homogeneous areas that convincingly associate into patterns of land-surface parameters well differentiated across scales. We found that the LV method performed better on scale levels generated through segmentation as compared to up-scaling through resampling. The results indicate that coupling multi-scale pattern analysis with delineation of morphometric primitives is possible. This approach could be further used for developing hierarchical classifications of landform elements. PMID:21779138

A comparative study of fire weather indices in a semiarid south-eastern Europe region. Case of study: Murcia (Spain).

PubMed

Pérez-Sánchez, Julio; Senent-Aparicio, Javier; Díaz-Palmero, José María; Cabezas-Cerezo, Juan de Dios

2017-07-15

Forest fires are an important distortion in forest ecosystems, linked to their development and whose effects proceed beyond the destruction of ecosystems and material properties, especially in semiarid regions. Prevention of forest fires has to lean on indices based on available parameters that quantify fire risk ignition and spreading. The present study was conducted to compare four fire weather indices in a semiarid region of 11,314km 2 located in southern Spain, characterised as being part of the most damaged area by fire in the Iberian Peninsula. The studied period comprises 3033 wildfires in the region during 15years (2000-2014), of which 80% are >100m 2 and 14% >1000m 2 , resulting around 40km 2 of burnt area in this period. The indices selected have been Angström Index, Forest Fire Drought Index, Forest Moisture Index and Fire Weather Index. Likewise, four selection methods have been applied to compare the results of the studied indices: Mahalanobis distance, percentile method, ranked percentile method and Relative Operating Characteristic curves (ROC). Angström index gives good results in the coastal areas with higher temperatures, low rainfall and wider range of variations while Fire Weather Index has better results in inland areas with higher rainfall, dense forest mass and fewer changes in meteorological conditions throughout the year. ROC space rejects all the indices except Fire Weather Index with good performance all over the region. ROC analysis ratios can be used to assess the success (or lack thereof) of fire indices; thus, it benefits operational wildfire predictions in semiarid regions similar to that of the case study. Copyright © 2017 Elsevier B.V. All rights reserved.

Diagnostic performance of FDG PET or PET/CT in prosthetic infection after arthroplasty: a meta-analysis.

PubMed

Jin, H; Yuan, L; Li, C; Kan, Y; Hao, R; Yang, J

2014-03-01

The purpose of this study was to systematically review and perform a meta-analysis of published data regarding the diagnostic performance of positron emission tomography (PET) or PET/computed tomography (PET/CT) in prosthetic infection after arthroplasty. A comprehensive computer literature search of studies published through May 31, 2012 regarding PET or PET/CT in patients suspicious of prosthetic infection was performed in PubMed/MEDLINE, Embase and Scopus databases. Pooled sensitivity and specificity of PET or PET/CT in patients suspicious of prosthetic infection on a per prosthesis-based analysis were calculated. The area under the receiver-operating characteristic (ROC) curve was calculated to measure the accuracy of PET or PET/CT in patients with suspicious of prosthetic infection. Fourteen studies comprising 838 prosthesis with suspicious of prosthetic infection after arthroplasty were included in this meta-analysis. The pooled sensitivity of PET or PET/CT in detecting prosthetic infection was 86% (95% confidence interval [CI] 82-90%) on a per prosthesis-based analysis. The pooled specificity of PET or PET/CT in detecting prosthetic infection was 86% (95% CI 83-89%) on a per prosthesis-based analysis. The area under the ROC curve was 0.93 on a per prosthesis-based analysis. In patients suspicious of prosthetic infection, FDG PET or PET/CT demonstrated high sensitivity and specificity. FDG PET or PET/CT are accurate methods in this setting. Nevertheless, possible sources of false positive results and influcing factors should kept in mind.

On the meaning of the weighted alternative free-response operating characteristic figure of merit.

PubMed

Chakraborty, Dev P; Zhai, Xuetong

2016-05-01

The free-response receiver operating characteristic (FROC) method is being increasingly used to evaluate observer performance in search tasks. Data analysis requires definition of a figure of merit (FOM) quantifying performance. While a number of FOMs have been proposed, the recommended one, namely, the weighted alternative FROC (wAFROC) FOM, is not well understood. The aim of this work is to clarify the meaning of this FOM by relating it to the empirical area under a proposed wAFROC curve. The weighted wAFROC FOM is defined in terms of a quasi-Wilcoxon statistic that involves weights, coding the clinical importance, assigned to each lesion. A new wAFROC curve is proposed, the y-axis of which incorporates the weights, giving more credit for marking clinically important lesions, while the x-axis is identical to that of the AFROC curve. An expression is derived relating the area under the empirical wAFROC curve to the wAFROC FOM. Examples are presented with small numbers of cases showing how AFROC and wAFROC curves are affected by correct and incorrect decisions and how the corresponding FOMs credit or penalize these decisions. The wAFROC, AFROC, and inferred ROC FOMs were applied to three clinical data sets involving multiple reader FROC interpretations in different modalities. It is shown analytically that the area under the empirical wAFROC curve equals the wAFROC FOM. This theorem is the FROC analog of a well-known theorem developed in 1975 for ROC analysis, which gave meaning to a Wilcoxon statistic based ROC FOM. A similar equivalence applies between the area under the empirical AFROC curve and the AFROC FOM. The examples show explicitly that the wAFROC FOM gives equal importance to all diseased cases, regardless of the number of lesions, a desirable statistical property not shared by the AFROC FOM. Applications to the clinical data sets show that the wAFROC FOM yields results comparable to that using the AFROC FOM. The equivalence theorem gives meaning to the weighted AFROC FOM, namely, it is identical to the empirical area under weighted AFROC curve.

Predictive factors in patients with hepatocellular carcinoma receiving sorafenib therapy using time-dependent receiver operating characteristic analysis.

PubMed

Nishikawa, Hiroki; Nishijima, Norihiro; Enomoto, Hirayuki; Sakamoto, Azusa; Nasu, Akihiro; Komekado, Hideyuki; Nishimura, Takashi; Kita, Ryuichi; Kimura, Toru; Iijima, Hiroko; Nishiguchi, Shuhei; Osaki, Yukio

2017-01-01

To investigate variables before sorafenib therapy on the clinical outcomes in hepatocellular carcinoma (HCC) patients receiving sorafenib and to further assess and compare the predictive performance of continuous parameters using time-dependent receiver operating characteristics (ROC) analysis. A total of 225 HCC patients were analyzed. We retrospectively examined factors related to overall survival (OS) and progression free survival (PFS) using univariate and multivariate analyses. Subsequently, we performed time-dependent ROC analysis of continuous parameters which were significant in the multivariate analysis in terms of OS and PFS. Total sum of area under the ROC in all time points (defined as TAAT score) in each case was calculated. Our cohort included 175 male and 50 female patients (median age, 72 years) and included 158 Child-Pugh A and 67 Child-Pugh B patients. The median OS time was 0.68 years, while the median PFS time was 0.24 years. On multivariate analysis, gender, body mass index (BMI), Child-Pugh classification, extrahepatic metastases, tumor burden, aspartate aminotransferase (AST) and alpha-fetoprotein (AFP) were identified as significant predictors of OS and ECOG-performance status, Child-Pugh classification and extrahepatic metastases were identified as significant predictors of PFS. Among three continuous variables (i.e., BMI, AST and AFP), AFP had the highest TAAT score for the entire cohort. In subgroup analyses, AFP had the highest TAAT score except for Child-Pugh B and female among three continuous variables. In continuous variables, AFP could have higher predictive accuracy for survival in HCC patients undergoing sorafenib therapy.

Does consideration of either psychological or material disadvantage improve coronary risk prediction? Prospective observational study of Scottish men.

PubMed

Macleod, John; Metcalfe, Chris; Smith, George Davey; Hart, Carole

2007-09-01

To assess the value of psychosocial risk factors in discriminating between individuals at higher and lower risk of coronary heart disease, using risk prediction equations. Prospective observational study. Scotland. 5191 employed men aged 35 to 64 years and free of coronary heart disease at study enrollment Area under receiver operating characteristic (ROC) curves for risk prediction equations including different risk factors for coronary heart disease. During the first 10 years of follow up, 203 men died of coronary heart disease and a further 200 were admitted to hospital with this diagnosis. Area under the ROC curve for the standard Framingham coronary risk factors was 74.5%. Addition of "vital exhaustion" and psychological stress led to areas under the ROC curve of 74.5% and 74.6%, respectively. Addition of current social class and lifetime social class to the standard Framingham equation gave areas under the ROC curve of 74.6% and 74.9%, respectively. In no case was there strong evidence for improved discrimination of the model containing the novel risk factor over the standard model. Consideration of psychosocial risk factors, including those that are strong independent predictors of heart disease, does not substantially influence the ability of risk prediction tools to discriminate between individuals at higher and lower risk of coronary heart disease.

ROC Analysis of Chest Radiographs Using Computed Radiography and Conventional Analog Films

NASA Astrophysics Data System (ADS)

Morioka, Craig A.; Brown, Kathy; Hayrapetian, Alek S.; Kangarloo, Hooshang; Balter, Stephen; Huang, H. K.

1989-05-01

Receiver operating characteristic is used to compare the image quality of films obtained digitally using computed radiography (CR) and conventionally using analog film following fluoroscopic examination. Similar radiological views were obtained by both modalities. Twenty-four cases, some with a solitary noncalcified nodule and/or pneumothorax, were collected. Ten radiologists have been tested viewing analog and CR digital films separately. Final results indicate that there is no statistically significant difference in the ability to detect either a pneumothorax or a solitary noncalcified nodule when comparing CR digital film with conventional analog film. However, there is a trend that indicated the area under the ROC curves for detection of either a pneumothorax or solitary noncalcified nodule were greater for the analog film than for the digital film.

Analysis of SEER Adenosquamous Carcinoma Data to Identify Cause Specific Survival Predictors and Socioeconomic Disparities.

PubMed

Cheung, Rex

2016-01-01

This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.

Linguistic adaptation, validation and comparison of 3 routinely used neuropathic pain questionnaires.

PubMed

Li, Jun; Feng, Yi; Han, Jisheng; Fan, Bifa; Wu, Dasheng; Zhang, Daying; Du, Dongping; Li, Hui; Lim, Jian; Wang, Jiashuang; Jin, Yi; Fu, Zhijian

2012-01-01

Neuropathic pain questionnaires are efficient diagnostic tools for neuropathic pain and play an important role in neuropathic pain epidemiologic studies in China. No comparison data was available in regards to the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), the Neuropathic Pain Questionnaire (NPQ) and ID Pain within and among the same population. To achieve a linguistic adaptation, validation, and comparison of Chinese versions of the 3 neuropathic pain questionnaires (LANSS, NPQ and ID Pain). A nonrandomized, controlled, prospective, multicenter trial. Ten pain centers in China. Two forward translations followed by comparison and reconciliation of the translations. Comparison of the 2 backward translations with the original version was made to establish consistency and accuracy of the translations. Pilot testing and pain specialists' evaluations were also required. A total of 140 patients were enrolled in 10 centers throughout China: 70 neuropathic pain patients and 70 nociceptive pain patients. Reliability (Cronbach's alpha coefficients and Guttman split-half coefficients) and validity (sensitivity, specificity, positive and negative predictive values, receiver operating characteristic [ROC] curves and the area under the ROC curves) of the 3 questionnaires were determined. ROC curves and the area under the ROC curves of the 3 questionnaires were also compared. Chinese versions of LANSS, NPQ and ID Pain had a good reliability (Cronbach's alpha coefficients and Guttman split-half coefficients were greater than 0.7). Sensitivity, specificity, positive and negative predictive values of the Chinese versions of LANSS and ID Pain were considerably high ( > 80%). The area under the ROC curves of LANSS and ID Pain was significantly higher than that of NPQ (P < 0.05). There was no statistically significant difference between the area under the ROC curves of LANSS and ID Pain (P > 0.05). The study was based on patients with a high school degree or above, which limited the application of the 3 neuropathic pain questionnaires to patients with lower educational levels. The Chinese versions of LANSS and ID Pain developed and validated by this study can be used as a diagnostic tool in differentiating neuropathic pain in patients whose native language is Chinese (Mandarin).

Survey on Tuberculosis Patients in Rural Areas in China: Tracing the Role of Stigma in Psychological Distress.

PubMed

Xu, Minlan; Markström, Urban; Lyu, Juncheng; Xu, Lingzhong

2017-10-04

Depressed patients had risks of non-adherence to medication, which brought a big challenge for the control of tuberculosis (TB). The stigma associated with TB may be the reason for distress. This study aimed to assess the psychological distress among TB patients living in rural areas in China and to further explore the relation of experienced stigma to distress. This study was a cross-sectional study with multi-stage randomized sampling for recruiting TB patients. Data was collected by the use of interviewer-led questionnaires. A total of 342 eligible and accessible TB patients being treated at home were included in the survey. Psychological distress was measured using the Kessler Psychological Distress Scale (K10). Experienced stigma was measured using a developed nine-item stigma questionnaire. Univariate analysis and multiple logistic regression were used to analyze the variables related to distress, respectively. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to present the strength of the associations. Finally, the prediction of logistic model was assessed in form of the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). According to the referred cut-off point from K10, this study revealed that 65.2% (223/342) of the participants were categorized as having psychological distress. Both the stigma questionnaire and the K10 were proven to be reliable and valid in measurement. Further analysis found that experienced stigma and illness severity were significant variables to psychological distress in the model of logistic regression. The model was assessed well in predicting distress by use of experienced stigma and illness severity in form of ROC and AUC. Rural TB patients had a high prevalence of psychological distress. Experience of stigma played a significant role in psychological distress. To move the barrier of stigma from the surroundings could be a good strategy in reducing distress for the patients and TB controlling for public health management.

Clinical diagnostic accuracy of acute colonic diverticulitis in patients admitted with acute abdominal pain, a receiver operating characteristic curve analysis.

PubMed

Jamal Talabani, A; Endreseth, B H; Lydersen, S; Edna, T-H

2017-01-01

The study investigated the capability of clinical findings, temperature, C-reactive protein (CRP), and white blood cell (WBC) count to discern patients with acute colonic diverticulitis from all other patients admitted with acute abdominal pain. The probability of acute diverticulitis was assessed by the examining doctor, using a scale from 0 (zero probability) to 10 (100 % probability). Receiver operating characteristic (ROC) curves were used to assess the clinical diagnostic accuracy of acute colonic diverticulitis in patients admitted with acute abdominal pain. Of 833 patients admitted with acute abdominal pain, 95 had acute colonic diverticulitis. ROC curve analysis gave an area under the ROC curve (AUC) of 0.95 (CI 0.92 to 0.97) for ages <65 years, AUC = 0.86 (CI 0.78 to 0.93) in older patients. Separate analysis showed an AUC = 0.83 (CI 0.80 to 0.86) of CRP alone. White blood cell count and temperature were almost useless to discriminate acute colonic diverticulitis from other types of acute abdominal pain, AUC = 0.59 (CI 0.53 to 0.65) for white blood cell count and AUC = 0.57 (0.50 to 0.63) for temperature, respectively. This prospective study demonstrates that standard clinical evaluation by non-specialist doctors based on history, physical examination, and initial blood tests on admission provides a high degree of diagnostic precision in patients with acute colonic diverticulitis.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

PubMed

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

Preoperative vitamin D deficiency and postoperative hypocalcemia in thyroid cancer patients undergoing total thyroidectomy plus central compartment neck dissection

PubMed Central

Wang, Xiaofei; Zhu, Jingqiang; Liu, Feng; Gong, Yanping; Li, Zhihui

2017-01-01

Background There appears to be a lack of consensus whether preoperative vitamin D deficiency (VDD) increases the risk of postoperative hypocalcemia and decreases the accuracy of postoperative parathyroid hormone (PTH) in predicting hypocalcemia in thyroid cancer patients undergoing total thyroidectomy (TT) plus central compartment neck dissection (CCND). This study aims to address these issues. Method All consecutive thyroid cancer patients who underwent TT plus CCND were retrospectively reviewed through a prospectively collected database between October 2015 and April 2016 in a tertiary referral hospital. The multivariate analysis was performed to identify the significant predictors for hypocalcemia. Receiver operator characteristic curve (ROC) was created and the area under the ROC was used to evaluate the predictive accuracy of postoperative PTH and compared between patients with or without VDD. Results A total of 186 patients were included. The incidence of VDD was 73.7% (137 patients). The incidence of biochemical and symptomatic hypocalcemia was similar in patients with or without VDD (P = 0.304 and 0.657, respectively). Multivariate analysis showed that only postoperative PTH was an independent predictor of symptomatic hypocalcemia (OR = 8.05, 95%CI = 3.99-16.22; P = 0.000). The area under the ROC was similar between patients with preoperative vitamin D level < 20 and ≥20 ng/mL (0.809 versus 0.845, P = 0.592). Conclusion VDD was not a significant risk factor for hypocalcemia following TT+CCND, and did not affect the accuracy of postoperative PTH as a predictor of postoperative hypocalcemia. Thus, routine preoperative screening for vitamin D seems to be unnecessary. PMID:29100453

Prognostic Significance of the Short Physical Performance Battery in Older Patients Discharged from Acute Care Hospitals

PubMed Central

Lattanzio, Fabrizia; Pedone, Claudio; Garasto, Sabrina; Laino, Irma; Bustacchini, Silvia; Pranno, Luigi; Mazzei, Bruno; Passarino, Giuseppe; Incalzi, Raffaele Antonelli

2012-01-01

Abstract We investigated the prognostic role of the Short Physical Performance Battery (SPPB) in elderly patients discharged from the acute care hospital. Our series consisted of 506 patients aged 70 years or more enrolled in a multicenter collaborative observational study. We considered three main outcomes: 1-year survival after discharge, functional decline, and hospitalization during follow-up. Independent predictors/correlates of the outcomes were investigated by Cox regression or logistic regression analysis when appropriate. The diagnostic accuracy of SPPB in relation to study outcomes was investigated by receiver operating characteristic (ROC) curve. SPPB score was associated with reduced mortality (hazard ratio [HR]=0.86, 95% confidence interval [CI] 0.78–0.95). When the analysis was adjusted for functional status at discharge, such an association was still near significant only for SPPB values >8 (HR=0.51; 95% CI 0.30–1.05). An SPPB score<5 could identify patients who died during follow-up with fair sensitivity (0.66), specificity (0.62), and area under the ROC curve (0.66). SPPB also qualified as independent correlate of functional decline (odds ratio [OR]=0.82; 95% CI 0.70–0.96), but not of rehospitalization or combined end-point death or rehospitalization. An SPPB score <5 could identify patients experiencing functional decline during follow-up with lower sensitivity (0.60), but higher specificity (0.69), and area under the ROC curve (0.69) with respect to mortality. In conclusion, SPPB can be considered a valid instrument to identify patients at major risk of functional decline and death after discharge from acute care hospital. However, it could more efficiently target patients at risk of functional decline than those at risk of death. PMID:22004280

Corrected ROC analysis for misclassified binary outcomes.

PubMed

Zawistowski, Matthew; Sussman, Jeremy B; Hofer, Timothy P; Bentley, Douglas; Hayward, Rodney A; Wiitala, Wyndy L

2017-06-15

Creating accurate risk prediction models from Big Data resources such as Electronic Health Records (EHRs) is a critical step toward achieving precision medicine. A major challenge in developing these tools is accounting for imperfect aspects of EHR data, particularly the potential for misclassified outcomes. Misclassification, the swapping of case and control outcome labels, is well known to bias effect size estimates for regression prediction models. In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the curve (AUC) metric from standard ROC analysis. The extent of the bias is determined by the false positive and false negative misclassification rates as well as disease prevalence. Notably, we show that simply correcting for misclassification while building the prediction model is not sufficient to remove the bias in AUC. We therefore introduce an intuitive misclassification-adjusted ROC procedure that accounts for uncertainty in observed outcomes and produces bias-corrected estimates of the true AUC. The method requires that misclassification rates are either known or can be estimated, quantities typically required for the modeling step. The computational simplicity of our method is a key advantage, making it ideal for efficiently comparing multiple prediction models on very large datasets. Finally, we apply the correction method to a hospitalization prediction model from a cohort of over 1 million patients from the Veterans Health Administrations EHR. Implementations of the ROC correction are provided for Stata and R. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

ROC-king onwards: intraepithelial lymphocyte counts, distribution & role in coeliac disease mucosal interpretation

PubMed Central

Rostami, Kamran; Marsh, Michael N; Johnson, Matt W; Mohaghegh, Hamid; Heal, Calvin; Holmes, Geoffrey; Ensari, Arzu; Aldulaimi, David; Bancel, Brigitte; Bassotti, Gabrio; Bateman, Adrian; Becheanu, Gabriel; Bozzola, Anna; Carroccio, Antonio; Catassi, Carlo; Ciacci, Carolina; Ciobanu, Alexandra; Danciu, Mihai; Derakhshan, Mohammad H; Elli, Luca; Ferrero, Stefano; Fiorentino, Michelangelo; Fiorino, Marilena; Ganji, Azita; Ghaffarzadehgan, Kamran; Going, James J; Ishaq, Sauid; Mandolesi, Alessandra; Mathews, Sherly; Maxim, Roxana; Mulder, Chris J; Neefjes-Borst, Andra; Robert, Marie; Russo, Ilaria; Rostami-Nejad, Mohammad; Sidoni, Angelo; Sotoudeh, Masoud; Villanacci, Vincenzo; Volta, Umberto; Zali, Mohammad R; Srivastava, Amitabh

2017-01-01

Objectives Counting intraepithelial lymphocytes (IEL) is central to the histological diagnosis of coeliac disease (CD), but no definitive ‘normal’ IEL range has ever been published. In this multicentre study, receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off between normal and CD (Marsh III lesion) duodenal mucosa, based on IEL counts on >400 mucosal biopsy specimens. Design The study was designed at the International Meeting on Digestive Pathology, Bucharest 2015. Investigators from 19 centres, eight countries of three continents, recruited 198 patients with Marsh III histology and 203 controls and used one agreed protocol to count IEL/100 enterocytes in well-oriented duodenal biopsies. Demographic and serological data were also collected. Results The mean ages of CD and control groups were 45.5 (neonate to 82) and 38.3 (2–88) years. Mean IEL count was 54±18/100 enterocytes in CD and 13±8 in normal controls (p=0.0001). ROC analysis indicated an optimal cut-off point of 25 IEL/100 enterocytes, with 99% sensitivity, 92% specificity and 99.5% area under the curve. Other cut-offs between 20 and 40 IEL were less discriminatory. Additionally, there was a sufficiently high number of biopsies to explore IEL counts across the subclassification of the Marsh III lesion. Conclusion Our ROC curve analyses demonstrate that for Marsh III lesions, a cut-off of 25 IEL/100 enterocytes optimises discrimination between normal control and CD biopsies. No differences in IEL counts were found between Marsh III a, b and c lesions. There was an indication of a continuously graded dose–response by IEL to environmental (gluten) antigenic influence. PMID:28893865

Blood-based biomarkers used to predict disease activity in Crohn's disease and ulcerative colitis.

PubMed

Burakoff, Robert; Pabby, Vikas; Onyewadume, Louisa; Odze, Robert; Adackapara, Cheryl; Wang, Wei; Friedman, Sonia; Hamilton, Matthew; Korzenik, Joshua; Levine, Jonathan; Makrauer, Frederick; Cheng, Changming; Smith, Hai Choo; Liew, Choong-Chin; Chao, Samuel

2015-05-01

Identifying specific genes that are differentially expressed during inflammatory bowel disease flares may help stratify disease activity. The aim of this study was to identify panels of genes to be able to distinguish disease activity in Crohn's disease (CD) and ulcerative colitis (UC). Patients were grouped into categories based on disease and severity determined by histological grading. Whole blood was collected by PAXgene Blood RNA collection tubes, (PreAnalytiX) and gene expression analysis using messenger RNA was conducted. Logistic regression was performed on multiple combinations of common probe sets, and data were evaluated in terms of discrimination by computing the area under the receiving operator characteristic curve (ROC-AUC). Nine inactive CD, 8 mild CD, 10 moderate-to-severe CD, 9 inactive UC, 8 mild UC, 10 moderate-to-severe UC, and 120 controls were hybridized to Affymetrix U133 Plus 2 microarrays. Panels of 6 individual genes discriminated the stages of disease activity: CD with mild severity {ROC-AUC, 0.89 (95% confidence interval [CI], 0.84%-0.95%)}, CD with moderate-to-severe severity (ROC-AUC 0.98 [95% CI, 0.97-1.0]), UC with mild severity (ROC-AUC 0.92 [95% CI, 0.87-0.96]), and UC with moderate-to-severe severity (ROC-AUC 0.99 [95% CI, 0.97-1.0]). Validation by real-time reverse transcription-PCR confirmed the Affymetrix microarray data. The specific whole blood gene panels reliably distinguished CD and UC and determined the activity of disease, with high sensitivity and specificity in our cohorts of patients. This simple serological test has the potential to become a biomarker to determine the activity of disease.

Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.

PubMed

Chai, Rifai; Naik, Ganesh R; Nguyen, Tuan Nghia; Ling, Sai Ho; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-05-01

This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p-value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.

A novel multi-epitope recombined protein for diagnosis of human brucellosis.

PubMed

Yin, Dehui; Li, Li; Song, Xiuling; Li, Han; Wang, Juan; Ju, Wen; Qu, Xiaofeng; Song, Dandan; Liu, Yushen; Meng, Xiangjun; Cao, Hongqian; Song, Weiyi; Meng, Rizeng; Liu, Jinhua; Li, Juan; Xu, Kun

2016-05-21

In epidemic regions of the world, brucellosis is a reemerging zoonosis with minimal mortality but is a serious public hygiene problem. Currently, there are various methods for brucellosis diagnosis, however few of them are available to be used to diagnose, especially for serious cross-reaction with other bacteria. To overcome this disadvantage, we explored a novel multi-epitope recombinant protein as human brucellosis diagnostic antigen. We established an indirect enzyme-linked immunosorbent assay (ELISA) based on this recombinant protein. 248 sera obtained from three different groups including patients with brucellosis (146 samples), non-brucellosis patients (82 samples), and healthy individuals (20 samples) were tested by indirect ELISA. To evaluate the assay, a receiver-operating characteristic (ROC) analysis and immunoblotting were carried out using these characterized serum samples. For this test, the area under the ROC curve was 0.9409 (95 % confidence interval, 0.9108 to 0.9709), and a sensitivity of 88.89 % and a specificity of 85.54 % was given with a cutoff value of 0.3865 from this ROC analysis. The Western blot results indicate that it is feasible to differentiate human brucellosis and non-brucellosis with the newly established method based on this recombinant protein. Our results obtained high diagnostic accuracy of the ELISA assay which encourage the use of this novel recombinant protein as diagnostic antigen to implement serological diagnosis of brucellosis.

Electrochemical Skin Conductance May Be Used to Screen for Diabetic Cardiac Autonomic Neuropathy in a Chinese Population with Diabetes

PubMed Central

He, Tianyi; Wang, Chuan; Zuo, Anju; Liu, Pan; Li, Wenjuan

2017-01-01

Aims. This study aimed to assess whether the electrochemical skin conductance (ESC) could be used to screen for diabetic cardiac autonomic neuropathy (DCAN) in a Chinese population with diabetes. Methods. We recruited 75 patients with type 2 diabetes mellitus (T2DM) and 45 controls without diabetes. DCAN was diagnosed by the cardiovascular autonomic reflex tests (CARTs) as gold standard. In all subjects ESCs of hands and feet were also detected by SUDOSCAN™ as a new screening method. The efficacy was assessed by receiver operating characteristic (ROC) curve analysis. Results. The ESCs of both hands and feet were significantly lower in T2DM patients with DCAN than those without DCAN (67.33 ± 15.37 versus 78.03 ± 13.73, P = 0.002, and 57.77 ± 20.99 versus 75.03 ± 11.41, P < 0.001). The ROC curve analysis showed the areas under the ROC curve were both 0.75 for ESCs of hands and feet in screening DCAN. And the optimal cut-off values of ESCs, sensitivities, and specificities were 76 μS, 76.7%, and 75.6% for hands and 75 μS, 80.0%, and 60.0% for feet, respectively. Conclusions. ESC measurement is a reliable and feasible method to screen DCAN in the Chinese population with diabetes before further diagnosis with CARTs. PMID:28280746

Development and testing of new candidate psoriatic arthritis screening questionnaires combining optimal questions from existing tools.

PubMed

Coates, Laura C; Walsh, Jessica; Haroon, Muhammad; FitzGerald, Oliver; Aslam, Tariq; Al Balushi, Farida; Burden, A D; Burden-Teh, Esther; Caperon, Anna R; Cerio, Rino; Chattopadhyay, Chandrabhusan; Chinoy, Hector; Goodfield, Mark J D; Kay, Lesley; Kelly, Stephen; Kirkham, Bruce W; Lovell, Christopher R; Marzo-Ortega, Helena; McHugh, Neil; Murphy, Ruth; Reynolds, Nick J; Smith, Catherine H; Stewart, Elizabeth J C; Warren, Richard B; Waxman, Robin; Wilson, Hilary E; Helliwell, Philip S

2014-09-01

Several questionnaires have been developed to screen for psoriatic arthritis (PsA), but head-to-head studies have found limitations. This study aimed to develop new questionnaires encompassing the most discriminative questions from existing instruments. Data from the CONTEST study, a head-to-head comparison of 3 existing questionnaires, were used to identify items with a Youden index score of ≥0.1. These were combined using 4 approaches: CONTEST (simple additions of questions), CONTESTw (weighting using logistic regression), CONTESTjt (addition of a joint manikin), and CONTESTtree (additional questions identified by classification and regression tree [CART] analysis). These candidate questionnaires were tested in independent data sets. Twelve individual questions with a Youden index score of ≥0.1 were identified, but 4 of these were excluded due to duplication and redundancy. Weighting for 2 of these questions was included in CONTESTw. Receiver operating characteristic (ROC) curve analysis showed that involvement in 6 joint areas on the manikin was predictive of PsA for inclusion in CONTESTjt. CART analysis identified a further 5 questions for inclusion in CONTESTtree. CONTESTtree was not significant on ROC curve analysis and discarded. The other 3 questionnaires were significant in all data sets, although CONTESTw was slightly inferior to the others in the validation data sets. Potential cut points for referral were also discussed. Of 4 candidate questionnaires combining existing discriminatory items to identify PsA in people with psoriasis, 3 were found to be significant on ROC curve analysis. Testing in independent data sets identified 2 questionnaires (CONTEST and CONTESTjt) that should be pursued for further prospective testing. Copyright © 2014 by the American College of Rheumatology.

Differentiating benign from malignant mediastinal lymph nodes visible at EBUS using grey-scale textural analysis.

PubMed

Edey, Anthony J; Pollentine, Adrian; Doody, Claire; Medford, Andrew R L

2015-04-01

Recent data suggest that grey-scale textural analysis on endobronchial ultrasound (EBUS) imaging can differentiate benign from malignant lymphadenopathy. The objective of studies was to evaluate grey-scale textural analysis and examine its clinical utility. Images from 135 consecutive clinically indicated EBUS procedures were evaluated retrospectively using MATLAB software (MathWorks, Natick, MA, USA). Manual node mapping was performed to obtain a region of interest and grey-scale textural features (range of pixel values and entropy) were analysed. The initial analysis involved 94 subjects and receiver operating characteristic (ROC) curves were generated. The ROC thresholds were then applied on a second cohort (41 subjects) to validate the earlier findings. A total of 371 images were evaluated. There was no difference in proportions of malignant disease (56% vs 53%, P = 0.66) in the prediction (group 1) and validation (group 2) sets. There was no difference in range of pixel values in group 1 but entropy was significantly higher in the malignant group (5.95 vs 5.77, P = 0.03). Higher entropy was seen in adenocarcinoma versus lymphoma (6.00 vs 5.50, P < 0.05). An ROC curve for entropy gave an area under the curve of 0.58 with 51% sensitivity and 71% specificity for entropy greater than 5.94 for malignancy. In group 2, the entropy threshold phenotyped only 47% of benign cases and 20% of malignant cases correctly. These findings suggest that use of EBUS grey-scale textural analysis for differentiation of malignant from benign lymphadenopathy may not be accurate. Further studies are required. © 2015 Asian Pacific Society of Respirology.

Verification of a model for the detection of intrauterine growth restriction (IUGR) by receiver operating characteristics (ROC)

NASA Astrophysics Data System (ADS)

Liu, Pengbo; Mongelli, Max; Mondry, Adrian

2004-07-01

The purpose of this study is to verify by Receiver Operating Characteristics (ROC) a mathematical model supporting the hypothesis that IUGR can be diagnosed by estimating growth velocity. The ROC compare computerized simulation results with clinical data from 325 pregnant British women. Each patient had 6 consecutive ultrasound examinations for fetal abdominal circumference (fac). Customized and un-customized fetal weights were calculated according to Hadlock"s formula. IUGR was diagnosed by the clinical standard, i.e. estimated weight below the tenth percentile. Growth velocity was estimated by calculating the changes of fac (Dzfac/dt) at various time intervals from 3 to 10 weeks. Finally, ROC was used to compare the methods. At 3~4 weeks scan interval, the area under the ROC curve is 0.68 for customized data and 0.66 for the uncustomized data with 95% confidence interval. Comparison between simulation data and real pregnancies verified that the model is clinically acceptable.

Determination of glucose-6-phosphate dehydrogenase cut-off values in a Tunisian population.

PubMed

Laouini, Naouel; Sahli, Chaima Abdelhafidh; Jouini, Latifa; Haloui, Sabrine; Fredj, Sondes Hadj; Daboubi, Rym; Siala, Hajer; Ouali, Faida; Becher, Meriam; Toumi, Nourelhouda; Bibi, Amina; Messsaoud, Taieb

2017-07-26

Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the commonest enzymopathy worldwide. The incidence depends essentially on the methods used for the assessment. In this respect, we attempted in this study to set cut-off values of G6PD activity to discriminate among normal, heterozygous, and deficient individuals using the World Health Organization (WHO) classification and the receiver operating characteristics (ROC) curve analysis. Blood samples from 250 female and 302 male subjects were enrolled in this study. The G6PD activity was determined using a quantitative assay. The common G6PD mutations in Tunisia were determined using the amplification refractory mutation system (ARMS-PCR) method. The ROC curve was used to choice the best cut-off. Normal G6PD values were 7.69±2.37, 7.86±2.39, and 7.51±2.35 U/g Hb for the entire, male, and female groups, respectively. Cut-off values for the total, male, and female were determined using the WHO classification and ROC curves analysis. In the male population, both cut-offs established using ROC curve analysis (4.00 U/g Hb) and the 60% level (3.82 U/g Hb), respectively are sensitive and specific resulting in a good efficiency of discrimination between deficient and normal males. For the female group the ROC cut-off (5.84 U/g Hb) seems better than the 60% level cut-off (3.88 U/g Hb) to discriminate between normal and heterozygote or homozygote women with higher Youden Index. The establishment of the normal values for a population is important for a better evaluation of the assay result. The ROC curve analysis is an alternative method to determine the status of patients since it correlates DNA analysis and G6PD activity.

A Quantitative Approach to Distinguish Pneumonia From Atelectasis Using Computed Tomography Attenuation.

PubMed

Edwards, Rachael M; Godwin, J David; Hippe, Dan S; Kicska, Gregory

2016-01-01

It is known that atelectasis demonstrates greater contrast enhancement than pneumonia on computed tomography (CT). However, the effectiveness of using a Hounsfield unit (HU) threshold to distinguish pneumonia from atelectasis has never been shown. The objective of the study is to demonstrate that an HU threshold can be quantitatively used to effectively distinguish pneumonia from atelectasis. Retrospectively identified CT pulmonary angiogram examinations that did not show pulmonary embolism but contained nonaerated lungs were classified as atelectasis or pneumonia based on established clinical criteria. The HU attenuation was measured in these nonaerated lungs. Receiver operating characteristic (ROC) analysis was performed to determine the area under the ROC curve, sensitivity, and specificity of using the attenuation to distinguish pneumonia from atelectasis. Sixty-eight nonaerated lungs were measured in 55 patients. The mean (SD) enhancement was 62 (18) HU in pneumonia and 119 (24) HU in atelectasis (P < 0.001). A threshold of 92 HU diagnosed pneumonia with 97% sensitivity (confidence interval [CI], 80%-99%) and 85% specificity (CI, 70-93). Accuracy, measured as area under the ROC curve, was 0.97 (CI, 0.89-0.99). We have established that a threshold HU value can be used to confidently distinguish pneumonia from atelectasis with our standard CT pulmonary angiogram imaging protocol and patient population. This suggests that a similar threshold HU value may be determined for other scanning protocols, and application of this threshold may facilitate a more confident diagnosis of pneumonia and thus speed treatment.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Accuracy of optical spectroscopy for the detection of cervical intraepithelial neoplasia: Testing a device as an adjunct to colposcopy.

PubMed

Cantor, Scott B; Yamal, Jose-Miguel; Guillaud, Martial; Cox, Dennis D; Atkinson, E Neely; Benedet, John L; Miller, Dianne; Ehlen, Thomas; Matisic, Jasenka; van Niekerk, Dirk; Bertrand, Monique; Milbourne, Andrea; Rhodes, Helen; Malpica, Anais; Staerkel, Gregg; Nader-Eftekhari, Shahla; Adler-Storthz, Karen; Scheurer, Michael E; Basen-Engquist, Karen; Shinn, Eileen; West, Loyd A; Vlastos, Anne-Therese; Tao, Xia; Beck, J Robert; Macaulay, Calum; Follen, Michele

2011-03-01

Testing emerging technologies involves the evaluation of biologic plausibility, technical efficacy, clinical effectiveness, patient satisfaction, and cost-effectiveness. The objective of this study was to select an effective classification algorithm for optical spectroscopy as an adjunct to colposcopy and obtain preliminary estimates of its accuracy for the detection of CIN 2 or worse. We recruited 1,000 patients from screening and prevention clinics and 850 patients from colposcopy clinics at two comprehensive cancer centers and a community hospital. Optical spectroscopy was performed, and 4,864 biopsies were obtained from the sites measured, including abnormal and normal colposcopic areas. The gold standard was the histologic report of biopsies, read 2 to 3 times by histopathologists blinded to the cytologic, histopathologic, and spectroscopic results. We calculated sensitivities, specificities, receiver operating characteristic (ROC) curves, and areas under the ROC curves. We identified a cutpoint for an algorithm based on optical spectroscopy that yielded an estimated sensitivity of 1.00 [95% confidence interval (CI) = 0.92-1.00] and an estimated specificity of 0.71 [95% CI = 0.62-0.79] in a combined screening and diagnostic population. The positive and negative predictive values were 0.58 and 1.00, respectively. The area under the ROC curve was 0.85 (95% CI = 0.81-0.89). The per-patient and per-site performance were similar in the diagnostic and poorer in the screening settings. Like colposcopy, the device performs best in a diagnostic population. Alternative statistical approaches demonstrate that the analysis is robust and that spectroscopy works as well as or slightly better than colposcopy for the detection of CIN 2 to cancer. Copyright © 2010 UICC.

Comparison of two correlated ROC curves at a given specificity or sensitivity level.

PubMed

Bantis, Leonidas E; Feng, Ziding

2016-10-30

The receiver operating characteristic (ROC) curve is the most popular statistical tool for evaluating the discriminatory capability of a given continuous biomarker. The need to compare two correlated ROC curves arises when individuals are measured with two biomarkers, which induces paired and thus correlated measurements. Many researchers have focused on comparing two correlated ROC curves in terms of the area under the curve (AUC), which summarizes the overall performance of the marker. However, particular values of specificity may be of interest. We focus on comparing two correlated ROC curves at a given specificity level. We propose parametric approaches, transformations to normality, and nonparametric kernel-based approaches. Our methods can be straightforwardly extended for inference in terms of ROC -1 (t). This is of particular interest for comparing the accuracy of two correlated biomarkers at a given sensitivity level. Extensions also involve inference for the AUC and accommodating covariates. We evaluate the robustness of our techniques through simulations, compare them with other known approaches, and present a real-data application involving prostate cancer screening. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data.

PubMed

Mazzara, Saveria; Rossi, Riccardo L; Grifantini, Renata; Donizetti, Simone; Abrignani, Sergio; Bombaci, Mauro

2017-03-30

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

An extension of the receiver operating characteristic curve and AUC-optimal classification.

PubMed

Takenouchi, Takashi; Komori, Osamu; Eguchi, Shinto

2012-10-01

While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimum function are discussed. A procedure derived by maximizing a specific optimum function has high robustness, based on gross error sensitivity. Additionally, we focus on the partial AUC, which is the partial area under the ROC curve. For example, in medical screening, a high true-positive rate to the fixed lower false-positive rate is preferable and thus the partial AUC corresponding to lower false-positive rates is much more important than the remaining AUC. We extend the class of concave optimum functions for partial AUC optimality with the boosting algorithm. We investigated the validity of the proposed method through several experiments with data sets in the UCI repository.

A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2011-01-01

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent of the applied detection threshold. While the standard ROC curve is well suited for quantifying binary fault detection performance, it is not suitable for quantifying the classification performance of multi-fault classification problems. Furthermore, it does not provide a measure of diagnostic latency. To address these shortcomings, a novel three-dimensional receiver operator characteristic (3D ROC) surface metric has been developed. This is done by generating and applying two separate curves: the standard ROC curve reflecting fault detection performance, and a second curve reflecting fault classification performance. A third dimension, diagnostic latency, is added giving rise to 3D ROC surfaces. Applying numerical integration techniques, the volumes under and between the surfaces are calculated to produce metrics of the diagnostic system s detection and classification performance. This paper will describe the 3D ROC surface metric in detail, and present an example of its application for quantifying the performance of aircraft engine gas path diagnostic methods. Metric limitations and potential enhancements are also discussed

Tumor invasiveness defined by IASLC/ATS/ERS classification of ground-glass nodules can be predicted by quantitative CT parameters.

PubMed

Zhou, Qian-Jun; Zheng, Zhi-Chun; Zhu, Yong-Qiao; Lu, Pei-Ji; Huang, Jia; Ye, Jian-Ding; Zhang, Jie; Lu, Shun; Luo, Qing-Quan

2017-05-01

To investigate the potential value of CT parameters to differentiate ground-glass nodules between noninvasive adenocarcinoma and invasive pulmonary adenocarcinoma (IPA) as defined by IASLC/ATS/ERS classification. We retrospectively reviewed 211 patients with pathologically proved stage 0-IA lung adenocarcinoma which appeared as subsolid nodules, from January 2012 to January 2013 including 137 pure ground glass nodules (pGGNs) and 74 part-solid nodules (PSNs). Pathological data was classified under the 2011 IASLC/ATS/ERS classification. Both quantitative and qualitative CT parameters were used to determine the tumor invasiveness between noninvasive adenocarcinomas and IPAs. There were 154 noninvasive adenocarcinomas and 57 IPAs. In pGGNs, CT size and area, one-dimensional mean CT value and bubble lucency were significantly different between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate regression and ROC analysis revealed that CT size and one-dimensional mean CT value were predictive of noninvasive adenocarcinomas compared to IPAs. Optimal cutoff value was 13.60 mm (sensitivity, 75.0%; specificity, 99.6%), and -583.60 HU (sensitivity, 68.8%; specificity, 66.9%). In PSNs, there were significant differences in CT size and area, solid component area, solid proportion, one-dimensional mean and maximum CT value, three-dimensional (3D) mean CT value between noninvasive adenocarcinomas and IPAs on univariate analysis. Multivariate and ROC analysis showed that CT size and 3D mean CT value were significantly differentiators. Optimal cutoff value was 19.64 mm (sensitivity, 53.7%; specificity, 93.9%), -571.63 HU (sensitivity, 85.4%; specificity, 75.8%). For pGGNs, CT size and one-dimensional mean CT value are determinants for tumor invasiveness. For PSNs, tumor invasiveness can be predicted by CT size and 3D mean CT value.

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Reliability of cone beam computed tomography as a biopsy-independent tool in differential diagnosis of periapical cysts and granulomas: An In vivo Study

PubMed Central

Chanani, Ankit; Adhikari, Haridas Das

2017-01-01

Background: Differential diagnosis of periapical cysts and granulomas is required as their treatment modalities are different. Aim: The aim of this study was to evaluate the efficacy of cone beam computed tomography (CBCT) in the differential diagnosis of periapical cysts from granulomas. Settings and Design: A single-centered observational study was carried out in the Department of Conservative Dentistry and Endodontics, Dr. R. Ahmed Dental College and Hospital, using CBCT and dental operating microscope. Methods: Forty-five lesions were analyzed using CBCT scans. One evaluator analyzed each CBCT scan for the presence of the following six characteristic radiological features: cyst like-location, shape, periphery, internal structure, effect on the surrounding structures, and cortical plate perforation. Another independent evaluator analyzed the CBCT scans. This process was repeated after 6 months, and inter- and intrarater reliability of CBCT diagnoses was evaluated. Periapical surgeries were performed and tissue samples were obtained for histopathological analysis. To evaluate the efficacy, CBCT diagnoses were compared with histopathological diagnoses, and six receiver operating characteristic (ROC) curve analyses were conducted. Statistical Analysis Used: ROC curve, Cronbach's alpha (α) test, and Cohen Kappa (κ) test were used for statistical analysis. Results: Both inter- and intrarater reliability were excellent (α = 0.94, κ = 0.75 and 0.77, respectively). ROC curve with regard to ≥4 positive findings revealed the highest area under curve (0.66). Conclusion: CBCT is moderately accurate in the differential diagnosis of periapical cysts and granulomas. PMID:29386780

[Predicting very early rebleeding after acute variceal bleeding based in classification and regression tree analysis (CRTA).].

PubMed

Altamirano, J; Augustin, S; Muntaner, L; Zapata, L; González-Angulo, A; Martínez, B; Flores-Arroyo, A; Camargo, L; Genescá, J

2010-01-01

Variceal bleeding (VB) is the main cause of death among cirrhotic patients. About 30-50% of early rebleeding is encountered few days after the acute episode of VB. It is necessary to stratify patients with high risk of very early rebleeding (VER) for more aggressive therapies. However, there are few and incompletely understood prognostic models for this purpose. To determine the risk factors associated with VER after an acute VB. Assessment and comparison of a novel prognostic model generated by Classification and Regression Tree Analysis (CART) with classic-used models (MELD and Child-Pugh [CP]). Sixty consecutive cirrhotic patients with acute variceal bleeding. CART analysis, MELD and Child-Pugh scores were performed at admission. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive performance of the models. Very early rebleeding rate was 13%. Variables associated with VER were: serum albumin (p = 0.027), creatinine (p = 0.021) and transfused blood units in the first 24 hrs (p = 0.05). The area under the ROC for MELD, CHILD-Pugh and CART were 0.46, 0.50 and 0.82, respectively. The value of cut analyzed by CART for the significant variables were: 1) Albumin 2.85 mg/dL, 2) Packed red cells 2 units and 3) Creatinine 1.65 mg/dL the ABC-ROC. Serum albumin, creatinine and number of transfused blood units were associated with VER. A simple CART algorithm combining these variables allows an accurate predictive assessment of VER after acute variceal bleeding. Key words: cirrhosis, variceal bleeding, esophageal varices, prognosis, portal hypertension.

Psychometric properties of the Medical Student Well-Being Index among medical students in a Malaysian medical school.

PubMed

Yusoff, Muhamad Saiful Bahri; Yaacob, Mohd Jamil; Naing, Nyi Nyi; Esa, Ab Rahman

2013-02-01

This study evaluated the convergent, discriminant, construct, concurrent and discriminative validity of the Medical Student Wellbeing Index (MSWBI) as well as to evaluate its internal consistency and optimal cut-off total scores to detect at least moderate levels of general psychological distress, stress, anxiety and depression symptoms. A cross sectional study was done on 171 medical students. The MSWBI and DASS-21 were administered and returned immediately upon completion. Confirmatory factor analysis, reliability analysis, ROC analysis and Pearson correlation test were applied to assess psychometric properties of the MSWBI. A total of 168 (98.2%) medical students responded. The goodness of fit indices showed the MSWBI had a good construct (χ(2)=6.14, p=0.803, RMSEA<0.001, RMR=0.004, GFI=0.99, AGFI=0.97, CFI=1.00, IFI=1.02, TLI=1.04). The Cronbach's alpha value was 0.69 indicating an acceptable level of internal consistency. Pearson correlation coefficients and ROC analysis suggested each MSWBI's item showed adequate convergent and discriminant validity. Its optimal cut-off scores to detect at least moderate levels of general psychological distress, stress, anxiety, and depression were 1.5, 2.5, 1.5 and 2.5 respectively with sensitivity and specificity ranged from 62 to 80% and the areas under ROC curve ranged from 0.71 to 0.83. This study showed that the MSWBI had good level of psychometric properties. The MSWBI score more than 2 can be considered as having significant psychological distress. The MSWBI is a valid and reliable screening instrument to assess psychological distress of medical students. Copyright © 2012 Elsevier B.V. All rights reserved.

Magnetic Resonance Lymphography Findings in Patients With Biochemical Recurrence After Prostatectomy and the Relation With the Stephenson Nomogram

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meijer, Hanneke J.M., E-mail: H.Meijer@rther.umcn.nl; Debats, Oscar A.; Roach, Mack

2012-12-01

Purpose: To estimate the occurrence of positive lymph nodes on magnetic resonance lymphography (MRL) in patients with a prostate-specific antigen (PSA) recurrence after prostatectomy and to investigate the relation between score on the Stephenson nomogram and lymph node involvement on MRL. Methods and Materials: Sixty-five candidates for salvage radiation therapy were referred for an MRL to determine their lymph node status. Clinical and histopathologic features were recorded. For 49 patients, data were complete to calculate the Stephenson nomogram score. Receiver operating characteristic (ROC) analysis was performed to determine how well this nomogram related to the MRL result. Analysis was donemore » for the whole group and separately for patients with a PSA <1.0 ng/mL to determine the situation in candidates for early salvage radiation therapy, and for patients without pathologic lymph nodes at initial lymph node dissection. Results: MRL detected positive lymph nodes in 47 patients. ROC analysis for the Stephenson nomogram yielded an area under the curve (AUC) of 0.78 (95% confidence interval, 0.61-0.93). Of 29 patients with a PSA <1.0 ng/mL, 18 had a positive MRL. Of 37 patients without lymph node involvement at initial lymph node dissection, 25 had a positive MRL. ROC analysis for the Stephenson nomogram showed AUCs of 0.84 and 0.74, respectively, for these latter groups. Conclusion: MRL detected positive lymph nodes in 72% of candidates for salvage radiation therapy, in 62% of candidates for early salvage radiation therapy, and in 68% of initially node-negative patients. The Stephenson nomogram showed a good correlation with the MRL result and may thus be useful for identifying patients with a PSA recurrence who are at high risk for lymph node involvement.« less

Discrimination of dementia with Lewy bodies from Alzheimer's disease using voxel-based morphometry of white matter by statistical parametric mapping 8 plus diffeomorphic anatomic registration through exponentiated Lie algebra.

PubMed

Nakatsuka, Tomoya; Imabayashi, Etsuko; Matsuda, Hiroshi; Sakakibara, Ryuji; Inaoka, Tsutomu; Terada, Hitoshi

2013-05-01

The purpose of this study was to identify brain atrophy specific for dementia with Lewy bodies (DLB) and to evaluate the discriminatory performance of this specific atrophy between DLB and Alzheimer's disease (AD). We retrospectively reviewed 60 DLB and 30 AD patients who had undergone 3D T1-weighted MRI. We randomly divided the DLB patients into two equal groups (A and B). First, we obtained a target volume of interest (VOI) for DLB-specific atrophy using correlation analysis of the percentage rate of significant whole white matter (WM) atrophy calculated using the Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD) based on statistical parametric mapping 8 (SPM8) plus diffeomorphic anatomic registration through exponentiated Lie algebra, with segmented WM images in group A. We then evaluated the usefulness of this target VOI for discriminating the remaining 30 DLB patients in group B from the 30 AD patients. Z score values in this target VOI obtained from VSRAD were used as the determinant in receiver operating characteristic (ROC) analysis. Specific target VOIs for DLB were determined in the right-side dominant dorsal midbrain, right-side dominant dorsal pons, and bilateral cerebellum. ROC analysis revealed that the target VOI limited to the midbrain exhibited the highest area under the ROC curves of 0.75. DLB patients showed specific atrophy in the midbrain, pons, and cerebellum. Midbrain atrophy demonstrated the highest power for discriminating DLB and AD. This approach may be useful for determining the contributions of DLB and AD pathologies to the dementia syndrome.

Three-class ROC analysis--the equal error utility assumption and the optimality of three-class ROC surface using the ideal observer.

PubMed

He, Xin; Frey, Eric C

2006-08-01

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the "general" three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman-Pearson (N-P) criteria. We found that by making assumptions for both MEU and N-P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N-P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.

Experimental Design and Data Analysis in Receiver Operating Characteristic Studies: Lessons Learned from Reports in Radiology from 1997 to 20061

PubMed Central

Shiraishi, Junji; Pesce, Lorenzo L.; Metz, Charles E.; Doi, Kunio

2009-01-01

Purpose: To provide a broad perspective concerning the recent use of receiver operating characteristic (ROC) analysis in medical imaging by reviewing ROC studies published in Radiology between 1997 and 2006 for experimental design, imaging modality, medical condition, and ROC paradigm. Materials and Methods: Two hundred ninety-five studies were obtained by conducting a literature search with PubMed with two criteria: publication in Radiology between 1997 and 2006 and occurrence of the phrase “receiver operating characteristic.” Studies returned by the query that were not diagnostic imaging procedure performance evaluations were excluded. Characteristics of the remaining studies were tabulated. Results: Two hundred thirty-three (79.0%) of the 295 studies reported findings based on observers' diagnostic judgments or objective measurements. Forty-three (14.6%) did not include human observers, with most of these reporting an evaluation of a computer-aided diagnosis system or functional data obtained with computed tomography (CT) or magnetic resonance (MR) imaging. The remaining 19 (6.4%) studies were classified as reviews or meta-analyses and were excluded from our subsequent analysis. Among the various imaging modalities, MR imaging (46.0%) and CT (25.7%) were investigated most frequently. Approximately 60% (144 of 233) of ROC studies with human observers published in Radiology included three or fewer observers. Conclusion: ROC analysis is widely used in radiologic research, confirming its fundamental role in assessing diagnostic performance. However, the ROC studies reported in Radiology were not always adequate to support clear and clinically relevant conclusions. © RSNA, 2009 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2533081632/-/DC1 PMID:19864510

Rationale, development and implementation of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest.

PubMed

Morrison, Laurie J; Nichol, Graham; Rea, Thomas D; Christenson, Jim; Callaway, Clifton W; Stephens, Shannon; Pirrallo, Ronald G; Atkins, Dianne L; Davis, Daniel P; Idris, Ahamed H; Newgard, Craig

2008-08-01

To describe the development, design and consequent scientific implications of the Resuscitation Outcomes Consortium (ROC) population-based registry; ROC Epistry-Cardiac Arrest. The ROC Epistry--Cardiac Arrest is designed as a prospective population-based registry of all Emergency Medical Services (EMSs)-attended 9-1-1 calls for patients with out-of-hospital cardiac arrest occurring in the geographical area described by the eight US and three Canadian regions. The dataset was derived by an North American interdisciplinary steering committee. Enrolled cases include individuals of all ages who experience cardiac arrest outside the hospital, with evaluation by organized EMS personnel and: (a) attempts at external defibrillation (by lay responders or emergency personnel), or chest compressions by organized EMS personnel; (b) were pulseless but did not receive attempts to defibrillate or CPR by EMS personnel. Selected data items are categorized as mandatory or optional and undergo revisions approximately every 12 months. Where possible all definitions are referenced to existing literature. Where a common definition did not exist one was developed. Optional items include standardized CPR process data elements. It is anticipated the ROC Epistry--Cardiac Arrest will enroll between approximately 9000 and 13,500 treated all rhythm arrests and 4000 and 5000 ventricular fibrillation arrests annually and approximately 8000 EMS-attended but untreated arrests. We describe the rationale, development, design and future implications of the ROC Epistry--Cardiac Arrest. This paper will serve as the reference for subsequent ROC manuscripts and for the common data elements captured in both ROC Epistry--Cardiac Arrest and the ROC trials.

Design of a receiver operating characteristic (ROC) study of 10:1 lossy image compression

NASA Astrophysics Data System (ADS)

Collins, Cary A.; Lane, David; Frank, Mark S.; Hardy, Michael E.; Haynor, David R.; Smith, Donald V.; Parker, James E.; Bender, Gregory N.; Kim, Yongmin

1994-04-01

The digital archiving system at Madigan Army Medical Center (MAMC) uses a 10:1 lossy data compression algorithm for most forms of computed radiography. A systematic study on the potential effect of lossy image compression on patient care has been initiated with a series of studies focused on specific diagnostic tasks. The studies are based upon the receiver operating characteristic (ROC) method of analysis for diagnostic systems. The null hypothesis is that observer performance with approximately 10:1 compressed and decompressed images is not different from using original, uncompressed images for detecting subtle pathologic findings seen on computed radiographs of bone, chest, or abdomen, when viewed on a high-resolution monitor. Our design involves collecting cases from eight pathologic categories. Truth is determined by committee using confirmatory studies performed during routine clinical practice whenever possible. Software has been developed to aid in case collection and to allow reading of the cases for the study using stand-alone Siemens Litebox workstations. Data analysis uses two methods, ROC analysis and free-response ROC (FROC) methods. This study will be one of the largest ROC/FROC studies of its kind and could benefit clinical radiology practice using PACS technology. The study design and results from a pilot FROC study are presented.

Diagnosis of focal liver lesions suspected of metastases by diffusion-weighted imaging (DWI): systematic comparison favors free-breathing technique.

PubMed

Baltzer, Pascal A T; Schelhorn, Juliane; Benndorf, Matthias; Dietzel, Matthias; Kaiser, Werner A

2013-01-01

Two echo planar imaging diffusion-weighted imaging (DWI) techniques [one breath hold (DWI(bh)), repetition time/echo time (TR/TE) 2100/62 ms; one at free breathing (DWI(fb)), TR/TE 2000/65 ms] were compared regarding diagnosis of focal liver lesions (FLLs) in 45 patients with suspected liver metastasis without prior treatment. Apparent diffusion coefficient values of 46 benign and 67 malignant FLLs were analyzed by receiver operating characteristics (ROC) analysis. DWI(fb) detected more malignant lesions than DWI(bh) (P=.002). Lesion size ≤10 mm was associated with FLLs missed by DWI(bh) (P=.018). Area under the ROC curve of DWI(fb) (0.801) was higher compared to that of DWI(bh) (0.669, P<.0113), demonstrating the diagnostic superiority of DWI(fb). Copyright © 2013 Elsevier Inc. All rights reserved.

Diffuse reflectance spectroscopy from 400-1600 nm to evaluate tumor resection margins during head and neck surgery (Conference Presentation)

NASA Astrophysics Data System (ADS)

Brouwer de Koning, Susan G.; Baltussen, E. J. M.; Karakullukcu, M. Baris; Smit, L.; van Veen, R. L. P.; Hendriks, Benno H. W.; Sterenborg, H. J. C. M.; Ruers, Theo J. M.

2017-02-01

This ex vivo study evaluates the feasibility of diffuse reflectance spectroscopy (DRS) for discriminating tumor from healthy oral tissue, with the aim to develop a technique that can be used to determine a complete excision of tumor through intraoperative margin assessment. DRS spectra were acquired on fresh surgical specimens from patients with an oral squamous cell carcinoma. The spectra represent a measure of diffuse light reflectance (wavelength range of 400-1600 nm), detected after illuminating tissue with a source fiber at 1.0 and 2.0 mm distances from a detection fiber. Spectra were obtained from 23 locations of tumor tissue and 16 locations of healthy muscle tissue. Biopsies were taken from all measured locations to facilitate an optimal correlation between spectra and pathological information. The area under the spectrum was used as a parameter to classify spectra of tumor and healthy tissue. Next, a receiver operating characteristics (ROC) analysis was performed to provide the area under the receiver operating curve (AUROC) as a measure for discriminative power. The area under the spectrum between 650 and 750 nm was used in the ROC analysis and provided AUROC values of 0.99 and 0.97, for distances of 1 mm and 2 mm between source and detector fiber, respectively. DRS can discriminate tumor from healthy oral tissue in an ex vivo setting. More specimens are needed to further evaluate this technique with component analyses and classification methods, prior to in vivo patient measurements.

Diagnostic performance of Fluorine-18-Fluorodeoxyglucose positron emission tomography for the diagnosis of osteomyelitis related to diabetic foot: a systematic review and a meta-analysis.

PubMed

Treglia, Giorgio; Sadeghi, Ramin; Annunziata, Salvatore; Zakavi, Seyed Rasoul; Caldarella, Carmelo; Muoio, Barbara; Bertagna, Francesco; Ceriani, Luca; Giovanella, Luca

2013-12-01

To systematically review and meta-analyse published data about the diagnostic performance of Fluorine-18-Fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) and PET/computed tomography (PET/CT) in osteomyelitis related to diabetic foot. A comprehensive literature search of studies on (18)F-FDG-PET and PET/CT in patients with diabetic foot was performed. Pooled sensitivity, specificity, positive and negative likelihood ratio (LR+ and LR-) and diagnostic odds ratio (DOR) and area under the summary ROC curve of (18)F-FDG-PET and PET/CT in patients with osteomyelitis related to diabetic foot were calculated. Nine studies comprising 299 patients with diabetic foot were included in the qualitative analysis (systematic review) and discussed. The quantitative analysis (meta-analysis) of four selected studies provided the following results on a per patient-based analysis: sensitivity was 74% [95% confidence interval (95%CI): 60-85%], specificity 91% (95%CI: 85-96%), LR+ 5.56 (95%CI: 2.02-15.27), LR- 0.37 (95%CI: 0.10-1.35), and DOR 16.96 (95%CI: 2.06-139.66). The area under the summary ROC curve was 0.874. In patients with suspected osteomyelitis related to diabetic foot (18)F-FDG-PET and PET/CT demonstrated a high specificity, being potentially useful tools if combined with other imaging methods such as MRI. Nevertheless, the literature focusing on the use of (18)F-FDG-PET and PET/CT in this setting remains still limited. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparison of two correlated ROC curves at a given specificity or sensitivity level

PubMed Central

Bantis, Leonidas E.; Feng, Ziding

2017-01-01

The receiver operating characteristic (ROC) curve is the most popular statistical tool for evaluating the discriminatory capability of a given continuous biomarker. The need to compare two correlated ROC curves arises when individuals are measured with two biomarkers, which induces paired and thus correlated measurements. Many researchers have focused on comparing two correlated ROC curves in terms of the area under the curve (AUC), which summarizes the overall performance of the marker. However, particular values of specificity may be of interest. We focus on comparing two correlated ROC curves at a given specificity level. We propose parametric approaches, transformations to normality, and nonparametric kernel-based approaches. Our methods can be straightforwardly extended for inference in terms of ROC−1(t). This is of particular interest for comparing the accuracy of two correlated biomarkers at a given sensitivity level. Extensions also involve inference for the AUC and accommodating covariates. We evaluate the robustness of our techniques through simulations, compare to other known approaches and present a real data application involving prostate cancer screening. PMID:27324068

Diagnostic Performance of Mammographic Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors.

PubMed

Li, Zhiming; Yu, Lan; Wang, Xin; Yu, Haiyang; Gao, Yuanxiang; Ren, Yande; Wang, Gang; Zhou, Xiaoming

2017-11-09

The purpose of this study was to investigate the diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Digital mammography images were obtained from the Picture Archiving and Communication System at our institute. Texture features of mammographic images were calculated. Mann-Whitney U test was used to identify differences between the benign and malignant group. The receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic performance of texture features. Significant differences of texture features of histogram, gray-level co-occurrence matrix (GLCM) and run length matrix (RLM) were found between the benign and malignant breast group (P < .05). The area under the ROC (AUROC) of histogram, GLCM, and RLM were 0.800, 0.787, and 0.761, with no differences between them (P > .05). The AUROCs of imaging-based diagnosis, texture analysis, and imaging-based diagnosis combined with texture analysis were 0.873, 0.863, and 0.961, respectively. When imaging-based diagnosis was combined with texture analysis, the AUROC was higher than that of imaging-based diagnosis or texture analysis (P < .05). Mammographic texture analysis is a reliable technique for differential diagnosis of benign and malignant breast tumors. Furthermore, the combination of imaging-based diagnosis and texture analysis can significantly improve diagnostic performance. Copyright © 2017 Elsevier Inc. All rights reserved.

An enhancement of ROC curves made them clinically relevant for diagnostic-test comparison and optimal-threshold determination.

PubMed

Subtil, Fabien; Rabilloud, Muriel

2015-07-01

The receiver operating characteristic curves (ROC curves) are often used to compare continuous diagnostic tests or determine the optimal threshold of a test; however, they do not consider the costs of misclassifications or the disease prevalence. The ROC graph was extended to allow for these aspects. Two new lines are added to the ROC graph: a sensitivity line and a specificity line. Their slopes depend on the disease prevalence and on the ratio of the net benefit of treating a diseased subject to the net cost of treating a nondiseased one. First, these lines help researchers determine the range of specificities within which test comparisons of partial areas under the curves is clinically relevant. Second, the ROC curve point the farthest from the specificity line is shown to be the optimal threshold in terms of expected utility. This method was applied: (1) to determine the optimal threshold of ratio specific immunoglobulin G (IgG)/total IgG for the diagnosis of congenital toxoplasmosis and (2) to select, among two markers, the most accurate for the diagnosis of left ventricular hypertrophy in hypertensive subjects. The two additional lines transform the statistically valid ROC graph into a clinically relevant tool for test selection and threshold determination. Copyright © 2015 Elsevier Inc. All rights reserved.

Reliability of cone beam computed tomography as a biopsy-independent tool in differential diagnosis of periapical cysts and granulomas: An In vivo Study.

PubMed

Chanani, Ankit; Adhikari, Haridas Das

2017-01-01

Differential diagnosis of periapical cysts and granulomas is required as their treatment modalities are different. The aim of this study was to evaluate the efficacy of cone beam computed tomography (CBCT) in the differential diagnosis of periapical cysts from granulomas. A single-centered observational study was carried out in the Department of Conservative Dentistry and Endodontics, Dr. R. Ahmed Dental College and Hospital, using CBCT and dental operating microscope. Forty-five lesions were analyzed using CBCT scans. One evaluator analyzed each CBCT scan for the presence of the following six characteristic radiological features: cyst like-location, shape, periphery, internal structure, effect on the surrounding structures, and cortical plate perforation. Another independent evaluator analyzed the CBCT scans. This process was repeated after 6 months, and inter- and intrarater reliability of CBCT diagnoses was evaluated. Periapical surgeries were performed and tissue samples were obtained for histopathological analysis. To evaluate the efficacy, CBCT diagnoses were compared with histopathological diagnoses, and six receiver operating characteristic (ROC) curve analyses were conducted. ROC curve, Cronbach's alpha (α) test, and Cohen Kappa (κ) test were used for statistical analysis. Both inter- and intrarater reliability were excellent (α = 0.94, κ = 0.75 and 0.77, respectively). ROC curve with regard to ≥4 positive findings revealed the highest area under curve (0.66). CBCT is moderately accurate in the differential diagnosis of periapical cysts and granulomas.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

[Law for the protection of returned overseas Chinese 7 September 1990].

PubMed

1990-09-10

The full text of the Beijing, China law on protection of returned overseas Chinese (ROC) (gui giao 2981 0294) and overseas Chinese families (OCF) (gui giao 0294 4187) is reported as effective on January 1, 1991 and adopted by the 7th National People's Congress Standing Committee on September 7, 1990. There are 22 articles. The 1st 2 articles define the population referred to: ROC are those Chinese who have returned and settled in China. OCF are those who have settled abroad. Family includes parents, children, spouses, brothers, sisters, grandparents, and grandchildren, and other relative receiving longterm support form overseas Chinese (OC). ROC and OCF have the same citizen rights and obligations prescribed in the constitution and other laws. ROC shall be resettled by the state. Concentrations of ROC in an area assures representation in the National People's Congress and people's congresses. ROC and OC have the right to organize social groups; the property of social groups is protected by law. In article 7, the state assures support for ranches and tree farms and school and medical care. Article 8 provides for local government support for investments of ROC, OC and OCF in industry and land commerce. Article 9 indicates government support at all levels for public services; tariffs will be reduced or exempted on donated materials and equipment brought from abroad. Private ownership of houses of ROC and OC is secured in article 10, and compensation is provided if the state appropriates the housing. ROC students and children of ROC and OC children in China are assured of support for education and employment assistance in article 11. Remittances of ROC and OCF received from abroad are protected in article 12. Article 13 secures the right of ROC and OCF to inheritance and gifts from relatives living abroad. ROC and OCF may dispose of overseas property. Article 15 requires examination of departure applications by relevant authorities. Emergency situations are accounted for. Article 16 protects the rights of visitation of a family still overseas. Article 17 assures the rights of ROC and OCF to settle abroad, and shall upon retirement continue to receive pensions, for instance. Article 18 assures assistance for study abroad. Article 19 assures rights of OC and OCF under international treaties. Article 10 provides the right to address grievances when laws have been violated. The State Council has the responsibility to prepare implementation measures pertinent to this law.

Validating the Injury Severity Score (ISS) in different populations: ISS predicts mortality better among Hispanics and females.

PubMed

Bolorunduro, O B; Villegas, C; Oyetunji, T A; Haut, E R; Stevens, K A; Chang, D C; Cornwell, E E; Efron, D T; Haider, A H

2011-03-01

The Injury Severity Score (ISS) is the most commonly used measure of injury severity. The score has been shown to have excellent predictive capability for trauma mortality and has been validated in multiple data sets. However, the score has never been tested to see if its discriminatory ability is affected by differences in race and gender. This study is aimed at validating the ISS in men and women and in three different race/ethnic groups using a nationwide database. Retrospective analysis of patients age 18-64 y in the National Trauma Data Bank 7.0 with blunt trauma was performed. ISS was categorized as mild (<9,) moderate (9-15), severe (16-25), and profound (>25). Logistic regression was done to measure the relative odds of mortality associated with a change in ISS categories. The discriminatory ability was compared using the receiver operating characteristics curves (ROC). A P value testing the equality of the ROC curves was calculated. Age stratified analyses were also conducted. A total of 872,102 patients had complete data for the analysis on ethnicity, while 763,549 patients were included in the gender analysis. The overall mortality rate was 3.7%. ROC in Whites was 0.8617, in Blacks 0.8586, and in Hispanics 0.8869. Hispanics have a statistically significant higher ROC (P value < 0.001). Similar results were observed within each age category. ROC curves were also significantly higher in females than in males. The ISS possesses excellent discriminatory ability in all populations as indicated by the high ROCs. Copyright © 2011 Elsevier Inc. All rights reserved.

[Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer--comparison with film-screen mammography].

PubMed

Kitahama, H

1991-05-25

The aim of this study is to present efficacy of storage phosphor-based digital mammography (CR-mammography) in diagnosis of breast cancer. Ninety-seven cases with breast cancer including 44 cases less than 2 cm in macroscopic size (t1 cases) were evaluated using storage phosphor-based digital mammography (2000 x 2510 pixels by 10 bits). Abnormal findings on CR-mammography were detected in 86 cases (88.7%) of 97 women with breast cancer. Sensitivity of CR-mammography was 88.7%. It was superior to that of film-screen mammography. On t1 breast cancer cases, sensitivity on CR-mammography was 88.6%. False negative rate in t1 breast cancer cases was reduced by image processing using CR-mammography. To evaluate microcalcifications, CR-mammograms and film-screen mammograms were investigated in 22 cases of breast cancer proven pathologically the existence of microcalcifications and 11 paraffin tissue blocks of breast cancer. CR-mammography was superior to film-screen mammography in recognizing of microcalcifications. As regards the detectability for the number and the shape of microcalcifications, CR-mammography was equivalent to film-screen mammography. Receiver operating characteristic (ROC) analysis by eight observers was performed for CR-mammography and film-screen mammography with 54 breast cancer patients and 54 normal cases. The detectability of abnormal findings of breast cancer on CR-mammography (ROC area = 0.91) was better than that on film-screen mammography (ROC area = 0.88) (p less than 0.05). Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer was discussed and demonstrated in this study.

Accuracy of specific BIVA for the assessment of body composition in the United States population.

PubMed

Buffa, Roberto; Saragat, Bruno; Cabras, Stefano; Rinaldi, Andrea C; Marini, Elisabetta

2013-01-01

Bioelectrical impedance vector analysis (BIVA) is a technique for the assessment of hydration and nutritional status, used in the clinical practice. Specific BIVA is an analytical variant, recently proposed for the Italian elderly population, that adjusts bioelectrical values for body geometry. Evaluating the accuracy of specific BIVA in the adult U.S. population, compared to the 'classic' BIVA procedure, using DXA as the reference technique, in order to obtain an interpretative model of body composition. A cross-sectional sample of 1590 adult individuals (836 men and 754 women, 21-49 years old) derived from the NHANES 2003-2004 was considered. Classic and specific BIVA were applied. The sensitivity and specificity in recognizing individuals below the 5(th) and above the 95(th) percentiles of percent fat (FMDXA%) and extracellular/intracellular water (ECW/ICW) ratio were evaluated by receiver operating characteristic (ROC) curves. Classic and specific BIVA results were compared by a probit multiple-regression. Specific BIVA was significantly more accurate than classic BIVA in evaluating FMDXA% (ROC areas: 0.84-0.92 and 0.49-0.61 respectively; p = 0.002). The evaluation of ECW/ICW was accurate (ROC areas between 0.83 and 0.96) and similarly performed by the two procedures (p = 0.829). The accuracy of specific BIVA was similar in the two sexes (p = 0.144) and in FMDXA% and ECW/ICW (p = 0.869). Specific BIVA showed to be an accurate technique. The tolerance ellipses of specific BIVA can be used for evaluating FM% and ECW/ICW in the U.S. adult population.

Using patient data similarities to predict radiation pneumonitis via a self-organizing map

NASA Astrophysics Data System (ADS)

Chen, Shifeng; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.; Das, Shiva K.

2008-01-01

This work investigates the use of the self-organizing map (SOM) technique for predicting lung radiation pneumonitis (RP) risk. SOM is an effective method for projecting and visualizing high-dimensional data in a low-dimensional space (map). By projecting patients with similar data (dose and non-dose factors) onto the same region of the map, commonalities in their outcomes can be visualized and categorized. Once built, the SOM may be used to predict pneumonitis risk by identifying the region of the map that is most similar to a patient's characteristics. Two SOM models were developed from a database of 219 lung cancer patients treated with radiation therapy (34 clinically diagnosed with Grade 2+ pneumonitis). The models were: SOMall built from all dose and non-dose factors and, for comparison, SOMdose built from dose factors alone. Both models were tested using ten-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Models SOMall and SOMdose yielded ten-fold cross-validated ROC areas of 0.73 (sensitivity/specificity = 71%/68%) and 0.67 (sensitivity/specificity = 63%/66%), respectively. The significant difference between the cross-validated ROC areas of these two models (p < 0.05) implies that non-dose features add important information toward predicting RP risk. Among the input features selected by model SOMall, the two with highest impact for increasing RP risk were: (a) higher mean lung dose and (b) chemotherapy prior to radiation therapy. The SOM model developed here may not be extrapolated to treatment techniques outside that used in our database, such as several-field lung intensity modulated radiation therapy or gated radiation therapy.

Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa

PubMed Central

2014-01-01

Background African swine fever (ASF) is endemic in several countries of Africa and may pose a risk to all pig producing areas on the continent. Official ASF reporting is often rare and there remains limited awareness of the continent-wide distribution of the disease. In the absence of accurate ASF outbreak data and few quantitative studies on the epidemiology of the disease in Africa, we used spatial multi-criteria decision analysis (MCDA) to derive predictions of the continental distribution of suitability for ASF persistence in domestic pig populations as part of sylvatic or domestic transmission cycles. In order to incorporate the uncertainty in the relative importance of different criteria in defining suitability, we modelled decisions within the MCDA framework using a stochastic approach. The predictive performance of suitability estimates was assessed via a partial ROC analysis using ASF outbreak data reported to the OIE since 2005. Results Outputs from the spatial MCDA indicate that large areas of sub-Saharan Africa may be suitable for ASF persistence as part of either domestic or sylvatic transmission cycles. Areas with high suitability for pig to pig transmission (‘domestic cycles’) were estimated to occur throughout sub-Saharan Africa, whilst areas with high suitability for introduction from wildlife reservoirs (‘sylvatic cycles’) were found predominantly in East, Central and Southern Africa. Based on average AUC ratios from the partial ROC analysis, the predictive ability of suitability estimates for domestic cycles alone was considerably higher than suitability estimates for sylvatic cycles alone, or domestic and sylvatic cycles in combination. Conclusions This study provides the first standardised estimates of the distribution of suitability for ASF transmission associated with domestic and sylvatic cycles in Africa. We provide further evidence for the utility of knowledge-driven risk mapping in animal health, particularly in data-sparse environments. PMID:24406022

Competitive Enzyme-Linked Immunosorbent Assay for Detection of Leptospira interrogans Serovar pomona Antibodies in Bovine Sera

PubMed Central

Surujballi, Om; Mallory, Maria

2001-01-01

A competitive enzyme-linked immunosorbent assay (ELISA) using a specific monoclonal antibody (M898) was developed for detection of bovine antibodies to Leptospira interrogans serovar pomona. This assay was evaluated using field sera (n = 190) with serovar pomona microscopic agglutination test (MAT) titers of ≥100 as the positive population (group A); field sera (n = 1,445) which were negative in the MAT (1:100 dilution) for serovar pomona (group B); and sera (from a specific-pathogen-free cattle herd [n = 210]) which were negative in the MAT (1:100 dilution) for serovars canicola, copenhageni, grippotyphosa, hardjo, pomona, and sejroe (group C). At the cutoff point recommended by receiver operating characteristic (ROC) curve analysis of the combined ELISA results of serum groups A, B, and C, the sensitivity and specificity values were 93.7 and 96.3%, respectively. The value for the area under this ROC curve was 0.977, indicating a high level of accuracy for the ELISA. Similar results were obtained from the analysis of the combined results of serum groups A and B and from the analysis of the combined results of serum groups A and C. PMID:11139193

Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures.

PubMed

Doebler, Philipp; Holling, Heinz

2015-12-01

Many screening tests dichotomize a measurement to classify subjects. Typically a cut-off value is chosen in a way that allows identification of an acceptable number of cases relative to a reference procedure, but does not produce too many false positives at the same time. Thus for the same sample many pairs of sensitivities and false positive rates result as the cut-off is varied. The curve of these points is called the receiver operating characteristic (ROC) curve. One goal of diagnostic meta-analysis is to integrate ROC curves and arrive at a summary ROC (SROC) curve. Holling, Böhning, and Böhning (Psychometrika 77:106-126, 2012a) demonstrated that finite semiparametric mixtures can describe the heterogeneity in a sample of Lehmann ROC curves well; this approach leads to clusters of SROC curves of a particular shape. We extend this work with the help of the [Formula: see text] transformation, a flexible family of transformations for proportions. A collection of SROC curves is constructed that approximately contains the Lehmann family but in addition allows the modeling of shapes beyond the Lehmann ROC curves. We introduce two rationales for determining the shape from the data. Using the fact that each curve corresponds to a natural univariate measure of diagnostic accuracy, we show how covariate adjusted mixtures lead to a meta-regression on SROC curves. Three worked examples illustrate the method.

Predicting extracapsular involvement in prostate cancer through the tumor contact length and the apparent diffusion coefficient.

PubMed

Granja, M F; Pedraza, C M; Flórez, D C; Romero, J A; Palau, M A; Aguirre, D A

To evaluate the diagnostic performance of the length of the tumor contact with the capsule (LTC) and the apparent diffusion coefficient (ADC) map in the prediction of microscopic extracapsular extension in patients with prostate cancer who are candidates for radical prostatectomy. We used receiver operating curves to retrospectively study the diagnostic performance of the ADC map and the LTC as predictors of microscopic extracapsular extension in 92 patients with prostate cancer and moderate to high risk who were examined between May 2011 and December 2013. The optimal cutoff for the ADC map was 0.87× 10 -3 mm 2 /s, which yielded an area under the ROC curve of 72% (95% CI: 57%-86%), corresponding to a sensitivity of 83% and a specificity of 61%. The optimal cutoff for the LTC was 17.5mm, which yielded an area under the ROC curve of 74% (95% CI: 61%-87%), corresponding to a sensitivity of 91% and a specificity of 57%. Combining the two criteria improved the diagnostic performance, yielding an area under the ROC curve of 77% (95% CI: 62%-92%), corresponding to a sensitivity of 77% and a specificity of 61%. We elaborated a logistic regression model, obtaining an area under the ROC curve of 82% (95% CI: 73%-93%). Using quantitative measures improves the diagnostic accuracy of multiparametric magnetic resonance imaging in the staging of prostate cancer. The values of the ADC and LTC were predictors of microscopic extracapsular extension, and the best results were obtained when both values were used in combination. Copyright © 2017 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for continuous diagnostic tests.

PubMed

To Duc, Khanh

2017-11-18

Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias-corrected inference tools are required. This paper introduce an R package, named bcROCsurface, which provides utility functions for verification bias-corrected ROC surface analysis. The shiny web application of the correction for verification bias in estimation of the ROC surface analysis is also developed. bcROCsurface may become an important tool for the statistical evaluation of three-class diagnostic markers in presence of verification bias. The R package, readme and example data are available on CRAN. The web interface enables users less familiar with R to evaluate the accuracy of diagnostic tests, and can be found at http://khanhtoduc.shinyapps.io/bcROCsurface_shiny/ .

What is an ROC curve?

PubMed

Hoo, Zhe Hui; Candlish, Jane; Teare, Dawn

2017-06-01

The paper by Body et al is concerned with the evaluation of decision aids, which can be used to identify potential acute coronary syndromes (ACS) in the ED. The authors previously developed the Manchester Acute Coronary Syndromes model (MACS) decision aid, which uses several clinical variables and two biomarkers to 'rule in' and 'rule out' ACS. However, one of the two biomarkers (heart-type fatty acid bindingprotein, H-FABP) is not widely used so a revised decision aid has been developed (Troponin-only Manchester Acute Coronary Syndromes, T-MACS), which include a single biomarker hs-cTnT. In this issue, the authors show how they derive a revised decision aid and describe its performance in a number of independent diagnostic cohort studies. Decision aids (as well as other types of 'diagnostic tests') are often evaluated in terms of diagnostic testing parameters such as the area under the receiver operating characteristic (ROC) curve, sensitivity and specificity. In this article, we explain how the ROC analysis is conducted and why it is an essential step towards developing a test with the desirable levels of sensitivity and specificity. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Reader strategies: variability and error- methodology, findings, and health policy implications from a study of the U.S. population of mammographers

NASA Astrophysics Data System (ADS)

Beam, Craig A.

2002-04-01

Each year, approximately 60% of all US women over the age of 40 utilize mammography. Through the matrix of an imaging technology, this Population of Patients (POP) interacts with a population of approximately 20,000 physicians who interpret mammograms in the US. This latter Population of Diagnosticians (POD) operationally serves as the interface between an image-centric healthcare technology system and patient. Methods: using data collected from a large POD and POP based study, I evaluate the distribution of several ROC curve-related parameters in the POD and explore the health policy implications of a population ROC curve for mammography. Results and Conclusions: Principal Components Analysis suggests that two Binormal parameters are sufficient to explain variation in the POD and implies that the Binormal model is foundational to Health Policy Research in Mammography. A population ROC curve based on percentiles of the POD can be used to set targets to achieve national health policy goals. Medical Image Perception science provides the framework. Alternatively, a restrictive policy can be envisioned using performance criteria based on area. However, the data suggests this sort of policy would be too costly in terms of reduced healthcare service capacity in the US in the face of burgeoning demands.

Initial Validation of a Comprehensive Assessment Instrument for Bereavement-Related Grief Symptoms and Risk of Complications: The Indicator of Bereavement Adaptation—Cruse Scotland (IBACS)

PubMed Central

Schut, Henk; Stroebe, Margaret S.; Wilson, Stewart; Birrell, John

2016-01-01

Objective This study assessed the validity of the Indicator of Bereavement Adaptation Cruse Scotland (IBACS). Designed for use in clinical and non-clinical settings, the IBACS measures severity of grief symptoms and risk of developing complications. Method N = 196 (44 male, 152 female) help-seeking, bereaved Scottish adults participated at two timepoints: T1 (baseline) and T2 (after 18 months). Four validated assessment instruments were administered: CORE-R, ICG-R, IES-R, SCL-90-R. Discriminative ability was assessed using ROC curve analysis. Concurrent validity was tested through correlation analysis at T1. Predictive validity was assessed using correlation analyses and ROC curve analysis. Optimal IBACS cutoff values were obtained by calculating a maximal Youden index J in ROC curve analysis. Clinical implications were compared across instruments. Results ROC curve analysis results (AUC = .84, p < .01, 95% CI between .77 and .90) indicated the IBACS is a good diagnostic instrument for assessing complicated grief. Positive correlations (p < .01, 2-tailed) with all four instruments at T1 demonstrated the IBACS' concurrent validity, strongest with complicated grief measures (r = .82). Predictive validity was shown to be fair in T2 ROC curve analysis results (n = 67, AUC = .78, 95% CI between .65 and .92; p < .01). Predictive validity was also supported by stable positive correlations between IBACS and other instruments at T2. Clinical indications were found not to differ across instruments. Conclusions The IBACS offers effective grief symptom and risk assessment for use by non-clinicians. Indications are sufficient to support intake assessment for a stepped model of bereavement intervention. PMID:27741246

ROC-king onwards: intraepithelial lymphocyte counts, distribution & role in coeliac disease mucosal interpretation.

PubMed

Rostami, Kamran; Marsh, Michael N; Johnson, Matt W; Mohaghegh, Hamid; Heal, Calvin; Holmes, Geoffrey; Ensari, Arzu; Aldulaimi, David; Bancel, Brigitte; Bassotti, Gabrio; Bateman, Adrian; Becheanu, Gabriel; Bozzola, Anna; Carroccio, Antonio; Catassi, Carlo; Ciacci, Carolina; Ciobanu, Alexandra; Danciu, Mihai; Derakhshan, Mohammad H; Elli, Luca; Ferrero, Stefano; Fiorentino, Michelangelo; Fiorino, Marilena; Ganji, Azita; Ghaffarzadehgan, Kamran; Going, James J; Ishaq, Sauid; Mandolesi, Alessandra; Mathews, Sherly; Maxim, Roxana; Mulder, Chris J; Neefjes-Borst, Andra; Robert, Marie; Russo, Ilaria; Rostami-Nejad, Mohammad; Sidoni, Angelo; Sotoudeh, Masoud; Villanacci, Vincenzo; Volta, Umberto; Zali, Mohammad R; Srivastava, Amitabh

2017-12-01

Counting intraepithelial lymphocytes (IEL) is central to the histological diagnosis of coeliac disease (CD), but no definitive 'normal' IEL range has ever been published. In this multicentre study, receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off between normal and CD (Marsh III lesion) duodenal mucosa, based on IEL counts on >400 mucosal biopsy specimens. The study was designed at the International Meeting on Digestive Pathology, Bucharest 2015. Investigators from 19 centres, eight countries of three continents, recruited 198 patients with Marsh III histology and 203 controls and used one agreed protocol to count IEL/100 enterocytes in well-oriented duodenal biopsies. Demographic and serological data were also collected. The mean ages of CD and control groups were 45.5 (neonate to 82) and 38.3 (2-88) years. Mean IEL count was 54±18/100 enterocytes in CD and 13±8 in normal controls (p=0.0001). ROC analysis indicated an optimal cut-off point of 25 IEL/100 enterocytes, with 99% sensitivity, 92% specificity and 99.5% area under the curve. Other cut-offs between 20 and 40 IEL were less discriminatory. Additionally, there was a sufficiently high number of biopsies to explore IEL counts across the subclassification of the Marsh III lesion. Our ROC curve analyses demonstrate that for Marsh III lesions, a cut-off of 25 IEL/100 enterocytes optimises discrimination between normal control and CD biopsies. No differences in IEL counts were found between Marsh III a, b and c lesions. There was an indication of a continuously graded dose-response by IEL to environmental (gluten) antigenic influence. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Application of impulse oscillometry and bronchial dilation test for analysis in patients with asthma and chronic obstructive pulmonary disease

PubMed Central

Li, Yueyue; Chen, Yang; Wang, Ping

2015-01-01

Impulse oscillometry (IOS) is a good method for measuring airway resistance. The aim of this study was to assess the diagnostic contribution of IOS combined with bronchial dilation test (BDT) when distinguishing between patients with asthma and those with chronic obstructive pulmonary disease (COPD). 870 were enrolled in the study including 561 patients with asthma, 100 patients with COPD and 209 patients with chronic coughing or normal subjects. All the participants underwent routine pulmonary function tests, IOS and BDT examination. And IOS examination was before and after BDT. IOS parameters (R5, R20, R25, R35, X5, X20, X25, X35, Fres, Zrs & RP) and forced expiratory volume in one second (FEV1) were recorded. Receiver operating characteristics (ROC) curve analysis was performed to evaluate the diagnostic ability to differentiate asthma and COPD. The discriminative power of the various parameters studied was determined by means of ROC curves: the area under the curve (AUC), sensitivity and specificity. The X5, X20, X25, X35, Fres, Zrs and Rp correlated better with COPD. In particular, X5, Fres and X25 have been found to be significantly correlated with COPD. The diagnostic efficiency of X5, Fres and X25 when diagnosis COPD, expressed by ROC curve parameters, was as follows: AUC (0.725, 0.730, 0.724), sensitivity (67%, 77%, 83%) and specificity (68%, 65%, 58%), respectively. The diagnostic efficiency of Zrs, R5 and X35 when diagnosis asthma, expressed by ROC curve parameters, was as follows: AUC (0.721, 0.710, 0.695), sensitivity (62%, 72%, 53%) and specificity (72%, 61%, 76%), respectively. Our findings show, that X5, X25 and Fres may be useful for predictions and evaluations for COPD. And R5, X35 and Zrs may provide useful IOS parameters for asthma. IOS combined BDT could be useful diagnostic and differential diagnosis between asthma and COPD. PMID:25785124

Application of impulse oscillometry and bronchial dilation test for analysis in patients with asthma and chronic obstructive pulmonary disease.

PubMed

Li, Yueyue; Chen, Yang; Wang, Ping

2015-01-01

Impulse oscillometry (IOS) is a good method for measuring airway resistance. The aim of this study was to assess the diagnostic contribution of IOS combined with bronchial dilation test (BDT) when distinguishing between patients with asthma and those with chronic obstructive pulmonary disease (COPD). 870 were enrolled in the study including 561 patients with asthma, 100 patients with COPD and 209 patients with chronic coughing or normal subjects. All the participants underwent routine pulmonary function tests, IOS and BDT examination. And IOS examination was before and after BDT. IOS parameters (R5, R20, R25, R35, X5, X20, X25, X35, Fres, Zrs & RP) and forced expiratory volume in one second (FEV1) were recorded. Receiver operating characteristics (ROC) curve analysis was performed to evaluate the diagnostic ability to differentiate asthma and COPD. The discriminative power of the various parameters studied was determined by means of ROC curves: the area under the curve (AUC), sensitivity and specificity. The X5, X20, X25, X35, Fres, Zrs and Rp correlated better with COPD. In particular, X5, Fres and X25 have been found to be significantly correlated with COPD. The diagnostic efficiency of X5, Fres and X25 when diagnosis COPD, expressed by ROC curve parameters, was as follows: AUC (0.725, 0.730, 0.724), sensitivity (67%, 77%, 83%) and specificity (68%, 65%, 58%), respectively. The diagnostic efficiency of Zrs, R5 and X35 when diagnosis asthma, expressed by ROC curve parameters, was as follows: AUC (0.721, 0.710, 0.695), sensitivity (62%, 72%, 53%) and specificity (72%, 61%, 76%), respectively. Our findings show, that X5, X25 and Fres may be useful for predictions and evaluations for COPD. And R5, X35 and Zrs may provide useful IOS parameters for asthma. IOS combined BDT could be useful diagnostic and differential diagnosis between asthma and COPD.

Voice-onset time and buzz-onset time identification: A ROC analysis

NASA Astrophysics Data System (ADS)

Lopez-Bascuas, Luis E.; Rosner, Burton S.; Garcia-Albea, Jose E.

2004-05-01

Previous studies have employed signal detection theory to analyze data from speech and nonspeech experiments. Typically, signal distributions were assumed to be Gaussian. Schouten and van Hessen [J. Acoust. Soc. Am. 104, 2980-2990 (1998)] explicitly tested this assumption for an intensity continuum and a speech continuum. They measured response distributions directly and, assuming an interval scale, concluded that the Gaussian assumption held for both continua. However, Pastore and Macmillan [J. Acoust. Soc. Am. 111, 2432 (2002)] applied ROC analysis to Schouten and van Hessen's data, assuming only an ordinal scale. Their ROC curves suppported the Gaussian assumption for the nonspeech signals only. Previously, Lopez-Bascuas [Proc. Audit. Bas. Speech Percept., 158-161 (1997)] found evidence with a rating scale procedure that the Gaussian model was inadequate for a voice-onset time continuum but not for a noise-buzz continuum. Both continua contained ten stimuli with asynchronies ranging from -35 ms to +55 ms. ROC curves (double-probability plots) are now reported for each pair of adjacent stimuli on the two continua. Both speech and nonspeech ROCs often appeared nonlinear, indicating non-Gaussian signal distributions under the usual zero-variance assumption for response criteria.

Accuracy of Seattle Heart Failure Model and HeartMate II Risk Score in Non-Inotrope-Dependent Advanced Heart Failure Patients: Insights From the ROADMAP Study (Risk Assessment and Comparative Effectiveness of Left Ventricular Assist Device and Medical Management in Ambulatory Heart Failure Patients).

PubMed

Lanfear, David E; Levy, Wayne C; Stehlik, Josef; Estep, Jerry D; Rogers, Joseph G; Shah, Keyur B; Boyle, Andrew J; Chuang, Joyce; Farrar, David J; Starling, Randall C

2017-05-01

Timing of left ventricular assist device (LVAD) implantation in advanced heart failure patients not on inotropes is unclear. Relevant prediction models exist (SHFM [Seattle Heart Failure Model] and HMRS [HeartMate II Risk Score]), but use in this group is not established. ROADMAP (Risk Assessment and Comparative Effectiveness of Left Ventricular Assist Device and Medical Management in Ambulatory Heart Failure Patients) is a prospective, multicenter, nonrandomized study of 200 advanced heart failure patients not on inotropes who met indications for LVAD implantation, comparing the effectiveness of HeartMate II support versus optimal medical management. We compared SHFM-predicted versus observed survival (overall survival and LVAD-free survival) in the optimal medical management arm (n=103) and HMRS-predicted versus observed survival in all LVAD patients (n=111) using Cox modeling, receiver-operator characteristic (ROC) curves, and calibration plots. In the optimal medical management cohort, the SHFM was a significant predictor of survival (hazard ratio=2.98; P <0.001; ROC area under the curve=0.71; P <0.001) but not LVAD-free survival (hazard ratio=1.41; P =0.097; ROC area under the curve=0.56; P =0.314). SHFM showed adequate calibration for survival but overestimated LVAD-free survival. In the LVAD cohort, the HMRS had marginal discrimination at 3 (Cox P =0.23; ROC area under the curve=0.71; P =0.026) and 12 months (Cox P =0.036; ROC area under the curve=0.62; P =0.122), but calibration was poor, underestimating survival across time and risk subgroups. In non-inotrope-dependent advanced heart failure patients receiving optimal medical management, the SHFM was predictive of overall survival but underestimated the risk of clinical worsening and LVAD implantation. Among LVAD patients, the HMRS had marginal discrimination and underestimated survival post-LVAD implantation. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01452802. © 2017 American Heart Association, Inc.

Recent developments in imaging system assessment methodology, FROC analysis and the search model.

PubMed

Chakraborty, Dev P

2011-08-21

A frequent problem in imaging is assessing whether a new imaging system is an improvement over an existing standard. Observer performance methods, in particular the receiver operating characteristic (ROC) paradigm, are widely used in this context. In ROC analysis lesion location information is not used and consequently scoring ambiguities can arise in tasks, such as nodule detection, involving finding localized lesions. This paper reviews progress in the free-response ROC (FROC) paradigm in which the observer marks and rates suspicious regions and the location information is used to determine whether lesions were correctly localized. Reviewed are FROC data analysis, a search-model for simulating FROC data, predictions of the model and a method for estimating the parameters. The search model parameters are physically meaningful quantities that can guide system optimization.

A local equation for differential diagnosis of β-thalassemia trait and iron deficiency anemia by logistic regression analysis in Southeast Iran.

PubMed

Sargolzaie, Narjes; Miri-Moghaddam, Ebrahim

2014-01-01

The most common differential diagnosis of β-thalassemia (β-thal) trait is iron deficiency anemia. Several red blood cell equations were introduced during different studies for differential diagnosis between β-thal trait and iron deficiency anemia. Due to genetic variations in different regions, these equations cannot be useful in all population. The aim of this study was to determine a native equation with high accuracy for differential diagnosis of β-thal trait and iron deficiency anemia for the Sistan and Baluchestan population by logistic regression analysis. We selected 77 iron deficiency anemia and 100 β-thal trait cases. We used binary logistic regression analysis and determined best equations for probability prediction of β-thal trait against iron deficiency anemia in our population. We compared diagnostic values and receiver operative characteristic (ROC) curve related to this equation and another 10 published equations in discriminating β-thal trait and iron deficiency anemia. The binary logistic regression analysis determined the best equation for best probability prediction of β-thal trait against iron deficiency anemia with area under curve (AUC) 0.998. Based on ROC curves and AUC, Green & King, England & Frazer, and then Sirdah indices, respectively, had the most accuracy after our equation. We suggest that to get the best equation and cut-off in each region, one needs to evaluate specific information of each region, specifically in areas where populations are homogeneous, to provide a specific formula for differentiating between β-thal trait and iron deficiency anemia.

Metabolic tumour volume and total lesion glycolysis, measured using preoperative 18F-FDG PET/CT, predict the recurrence of endometrial cancer.

PubMed

Shim, S-H; Kim, D-Y; Lee, D-Y; Lee, S-W; Park, J-Y; Lee, J J; Kim, J-H; Kim, Y-M; Kim, Y-T; Nam, J-H

2014-08-01

To investigate the prognostic value of metabolic tumour volume (MTV) and total lesion glycolysis (TLG), measured by preoperative positron emission tomography and computerised tomography (PET/CT), in women with endometrial cancer. Retrospective cohort study. A tertiary referral centre. Women with endometrial cancer who underwent preoperative (18)F-FDG PET/CT in the period 2004-2009. Clinicopathological data for 84 women with endometrial cancer were reviewed from medical records. Cox proportional hazards modelling identified recurrence predictors. The receiver operating characteristic (ROC) curve was used to determine the cut-off value for predicting recurrence. Disease-free survival (DFS). The number of patients with International Federation of Gynecology and Obstetrics (FIGO) stages were: I (58); II (11); III (13); and IV (2). The median DFS was 48 (1-85) months. By univariate analysis, DFS was significantly associated with FIGO stage, histology, peritoneal cytology, myometrial invasion, nodal metastasis, serum CA-125, MTV, and TLG. Using multivariate analysis, the MTV (P = 0.010; hazard ratio, HR = 1.010; 95% confidence interval, 95% CI = 1.002-1.018) and TLG (P = 0.024; HR = 1.001; 95% CI = 1.000-1.002) were associated with DFS. The area under the ROC curve was 0.679 (95% CI = 0.505-0.836) after discriminating for recurrence using an MTV cut-off value of 17.15 ml. Regarding TLG, the cut-off value was 56.43 g and the area under the ROC plot was 0.661 (95% CI = 0.501-0.827). Kaplan-Meier survival graphs demonstrated a significant difference in DFS between groups categorised using the cut-off values for MTV and TLG (P < 0.022 for MTV and P < 0.047 for TLG, by log-rank test). Preoperative MTV and TLG could be independent prognostic factors predicting the recurrence of endometrial cancer. © 2014 Royal College of Obstetricians and Gynaecologists.

Clinical application of MRI-respiratory gating technology in the evaluation of children with obstructive sleep apnea hypopnea syndrome.

PubMed

Zeng, Guohui; Teng, Yaoshu; Zhu, Jin; Zhu, Darong; Yang, Bin; Hu, Linping; Chen, Manman; Fu, Xiao

2018-01-01

The objective of the present study was to investigate the clinical application of magnetic resonance imaging (MRI)-respiratory gating technology for assessing illness severity in children with obstructive sleep apnea hypopnea syndrome (OSAHS).MRI-respiratory gating technology was used to scan the nasopharyngeal cavities of 51 children diagnosed with OSAHS during 6 respiratory phases. Correlations between the ratio of the area of the adenoid to the area of the nasopalatine pharyngeal cavity (Sa/Snp), with the main indexes of polysomnography (PSG), were analyzed. Receiver operator characteristic (ROC) curve and Kappa analysis were used to determine the diagnostic accuracy of Sa/Snp in pediatric OSAHS.The Sa/Snp was positively correlated with the apnea hypopnea index (AHI) (P < .001) and negatively correlated with the lowest oxygen saturation of blood during sleep (LaSO2) (P < .001). ROC analysis in the 6 respiratory phases showed that the area under the curve (AUC) of the Sa/Snp in the end-expiratory phase was the largest (0.992, P < .001), providing a threshold of 69.5% for the diagnosis of severe versus slight-moderate OSAHS in children. Consistency analysis with the AHI showed a diagnosis accordance rate of 96.0% in severe pediatric OSAHS and 96.2% in slight-moderate pediatric OSAHS (Kappa = 0.922, P < .001).Stenosis of the nasopalatine pharyngeal cavity in children with adenoidal hypertrophy was greatest at the end-expiration phase during sleep. The end-expiratory Sa/Snp obtained by a combination of MRI and respiratory gating technology has potential as an important imaging index for diagnosing and evaluating severity in pediatric OSAHS.

Use of Cardiac Magnetic Resonance Imaging Based Measurements of Inferior Vena Cava Cross-Sectional Area in the Diagnosis of Pericardial Constriction.

PubMed

Hanneman, Kate; Thavendiranathan, Paaladinesh; Nguyen, Elsie T; Moshonov, Hadas; Wald, Rachel; Connelly, Kim A; Paul, Narinder S; Wintersperger, Bernd J; Crean, Andrew M

2015-08-01

To evaluate the value of cardiac magnetic resonance imaging (MRI)-based measurements of inferior vena cava (IVC) cross-sectional area in the diagnosis of pericardial constriction. Patients who had undergone cardiac MRI for evaluation of clinically suspected pericardial constriction were identified retrospectively. The diagnosis of pericardial constriction was established by clinical history, echocardiography, cardiac catheterization, intraoperative findings, and/or histopathology. Cross-sectional areas of the suprahepatic IVC and descending aorta were measured on a single axial steady-state free-precession (SSFP) image at the level of the esophageal hiatus in end-systole. Logistic regression and receiver-operating curve (ROC) analyses were performed. Thirty-six patients were included; 50% (n = 18) had pericardial constriction. Mean age was 53.9 ± 15.3 years, and 72% (n = 26) were male. IVC area, ratio of IVC to aortic area, pericardial thickness, and presence of respirophasic septal shift were all significantly different between patients with constriction and those without (P < .001 for all). IVC to aortic area ratio had the highest odds ratio for the prediction of constriction (1070, 95% confidence interval [8.0-143051], P = .005). ROC analysis illustrated that IVC to aortic area ratio discriminated between those with and without constriction with an area under the curve of 0.96 (95% confidence interval [0.91-1.00]). In patients referred for cardiac MRI assessment of suspected pericardial constriction, measurement of suprahepatic IVC cross-sectional area may be useful in confirming the diagnosis of constriction when used in combination with other imaging findings, including pericardial thickness and respirophasic septal shift. Copyright © 2015 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Diagnostic performance of traditional hepatobiliary biomarkers of drug-induced liver injury in the rat.

PubMed

Ennulat, Daniela; Magid-Slav, Michal; Rehm, Sabine; Tatsuoka, Kay S

2010-08-01

Nonclinical studies provide the opportunity to anchor biochemical with morphologic findings; however, liver injury is often complex and heterogeneous, confounding the ability to relate biochemical changes with specific patterns of injury. The aim of the current study was to compare diagnostic performance of hepatobiliary markers for specific manifestations of drug-induced liver injury in rat using data collected in a recent hepatic toxicogenomics initiative in which rats (n = 3205) were given 182 different treatments for 4 or 14 days. Diagnostic accuracy of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbili), serum bile acids (SBA), alkaline phosphatase (ALP), gamma glutamyl transferase (GGT), total cholesterol (Chol), and triglycerides (Trig) was evaluated for specific types of liver histopathology by Receiver Operating Characteristic (ROC) analysis. To assess the relationship between biochemical and morphologic changes in the absence of hepatocellular necrosis, a second ROC analysis was performed on a subset of rats (n = 2504) given treatments (n = 152) that did not cause hepatocellular necrosis. In the initial analysis, ALT, AST, Tbili, and SBA had the greatest diagnostic utility for manifestations of hepatocellular necrosis and biliary injury, with comparable magnitude of area under the ROC curve and serum hepatobiliary marker changes for both. In the absence of hepatocellular necrosis, ALT increases were observed with biochemical or morphologic evidence of cholestasis. In both analyses, diagnostic utility of ALP and GGT for biliary injury was limited; however, ALP had modest diagnostic value for peroxisome proliferation, and ALT, AST, and total Chol had moderate diagnostic utility for phospholipidosis. None of the eight markers evaluated had diagnostic value for manifestations of hypertrophy, cytoplasmic rarefaction, inflammation, or lipidosis.

Anti-A2 and anti-A1 domain antibodies are potential predictors of immune tolerance induction outcome in children with hemophilia A.

PubMed

Lapalud, P; Rothschild, C; Mathieu-Dupas, E; Balicchi, J; Gruel, Y; Laune, D; Molina, F; Schved, J F; Granier, C; Lavigne-Lissalde, G

2015-04-01

Hemophilia A (HA) is a congenital bleeding disorder resulting from factor VIII deficiency. The most serious complication of HA management is the appearance of inhibitory antibodies (Abs) against injected FVIII concentrates. To eradicate inhibitors, immune tolerance induction (ITI) is usually attempted, but it fails in up to 30% of cases. Currently, no undisputed predictive marker of ITI outcome is available to facilitate the clinical decision. To identify predictive markers of ITI efficacy. The isotypic and epitopic repertoires of inhibitory Abs were analyzed in plasma samples collected before ITI initiation from 15 children with severe HA and high-titer inhibitors, and their levels were compared in the two outcome groups (ITI success [n = 7] and ITI failure [n = 8]). The predictive value of these candidate biomarkers and of the currently used indicators (inhibitor titer and age at ITI initiation, highest inhibitor titer before ITI, and interval between inhibitor diagnosis and ITI initiation) was then compared by statistical analysis (Wilcoxon test and receiver receiver operating characteristic [ROC] curve analysis). Whereas current indicators seemed to fail in discriminating patients in the two outcome groups (ITI success or failure), anti-A1 and anti-A2 Ab levels before ITI initiation appeared to be good potential predictive markers of ITI outcome (P < 0.018). ROC analysis showed that anti-A1 and anti-A2 Abs were the best at discriminating between outcome groups (area under the ROC curve of > 0.875). Anti-A1 and anti-A2 Abs could represent new promising tools for the development of ITI outcome prediction tests for children with severe HA. © 2015 International Society on Thrombosis and Haemostasis.

Comparison of conventional and automated breast volume ultrasound in the description and characterization of solid breast masses based on BI-RADS features.

PubMed

Kim, Hyunji; Cha, Joo Hee; Oh, Ha-Yeun; Kim, Hak Hee; Shin, Hee Jung; Chae, Eun Young

2014-07-01

To compare the performance of radiologists in the use of conventional ultrasound (US) and automated breast volume ultrasound (ABVU) for the characterization of benign and malignant solid breast masses based on breast imaging and reporting data system (BI-RADS) criteria. Conventional US and ABVU images were obtained in 87 patients with 106 solid breast masses (52 cancers, 54 benign lesions). Three experienced radiologists who were blinded to all examination results independently characterized the lesions and reported a BI-RADS assessment category and a level of suspicion of malignancy. The results were analyzed by calculation of Cohen's κ coefficient and by receiver operating characteristic (ROC) analysis. Assessment of the agreement of conventional US and ABVU indicated that the posterior echo feature was the most discordant feature of seven features (κ = 0.371 ± 0.225) and that orientation had the greatest agreement (κ = 0.608 ± 0.210). The final assessment showed substantial agreement (κ = 0.773 ± 0.104). The areas under the ROC curves (Az) for conventional US and ABVU were not statistically significant for each reader, but the mean Az values of conventional US and ABVU by multi-reader multi-case analysis were significantly different (conventional US 0.991, ABVU 0.963; 95 % CI -0.0471 to -0.0097). The means for sensitivity, specificity, positive predictive value, and negative predictive value of conventional US and ABVU did not differ significantly. There was substantial inter-observer agreement in the final assessment of solid breast masses by conventional US and ABVU. ROC analysis comparing the performance of conventional US and ABVU indicated a marginally significant difference in mean Az, but not in mean sensitivity, specificity, positive predictive value, or negative predictive value.

[Correlation between percentage of body fat and simple anthropometric parameters in children aged 6-9 years in Guangzhou].

PubMed

Yan, H C; Hao, Y T; Guo, Y F; Wei, Y H; Zhang, J H; Huang, G P; Mao, L M; Zhang, Z Q

2017-11-10

Objective: To evaluate the accuracy of simple anthropometric parameters in diagnosing obesity in children in Guangzhou. Methods: A cross-sectional study, including 465 children aged 6-9 years, was carried out in Guangzhou. Their body height and weight, waist circumference (WC) and hip circumference were measured according to standard procedure. Body mass index (BMI), waist to hip ratio (WHR) and waist-to-height ratio (WHtR) were calculated. Body fat percentage (BF%) was determined by dual-energy X-ray absorptiometry. Multiple regression analysis was applied to evaluate the correlations between age-adjusted physical indicators and BF%, after the adjustment for age. Obesity was defined by BF%. Receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic accuracy of the indicators for childhood obesity. Area under-ROC curves (AUCs) were calculated and the best cut-off point that maximizing 'sensitivity + specificity-1' was determined. Results: BMI showed the strongest association with BF% through multiple regression analysis. For 'per-standard deviation increase' of BMI, BF% increased by 5.3% ( t =23.1, P <0.01) in boys and 4.6% ( t =17.5, P <0.01) in girls, respectively. The ROC curve analysis indicated that BMI exhibited the largest AUC in both boys (AUC=0.908) and girls (AUC=0.895). The sensitivity was 80.8% in boys and 81.8% in girls, and the specificity was 88.2% in boys and 87.1% in girls. Both the AUCs for WHtR and WC were less than 0.8 in boys and girls. WHR had the smallest AUCs (<0.8) in both boys and girls. Conclusion: BMI appeared to be a good predicator for BF% in children aged 6-9 years in Guangzhou.

Utility of Shear Wave Elastography for Differentiating Biliary Atresia From Infantile Hepatitis Syndrome.

PubMed

Wang, Xiaoman; Qian, Linxue; Jia, Liqun; Bellah, Richard; Wang, Ning; Xin, Yue; Liu, Qinglin

2016-07-01

The purpose of this study was to investigate the potential utility of shear wave elastography (SWE) for diagnosis of biliary atresia and for differentiating biliary atresia from infantile hepatitis syndrome by measuring liver stiffness. Thirty-eight patients with biliary atresia and 17 patients with infantile hepatitis syndrome were included, along with 31 healthy control infants. The 3 groups underwent SWE. The hepatic tissue of each patient with biliary atresia had been surgically biopsied. Statistical analyses for mean values of the 3 groups were performed. Optimum cutoff values using SWE for differentiation between the biliary atresia and control groups were calculated by a receiver operating characteristic (ROC) analysis. The mean SWE values ± SD for the 3 groups were as follows: biliary atresia group, 20.46 ± 10.19 kPa; infantile hepatitis syndrome group, 6.29 ± 0.99 kPa; and control group, 6.41 ± 1.08 kPa. The mean SWE value for the biliary atresia group was higher than the values for the control and infantile hepatitis syndrome groups (P < .01). The mean SWE values between the control and infantile hepatitis syndrome groups were not statistically different. The ROC analysis showed a cutoff value of 8.68 kPa for differentiation between the biliary atresia and control groups. The area under the ROC curve was 0.997, with sensitivity of 97.4%, specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 96.9%. Correlation analysis suggested a positive correlation between SWE values and age for patients with biliary atresia, with a Pearson correlation coefficient of 0.463 (P < .05). The significant increase in liver SWE values in neonates and infants with biliary atresia supports their application for differentiating biliary atresia from infantile hepatitis syndrome.

Three new potential ovarian cancer biomarkers detected in human urine with equalizer bead technology.

PubMed

Petri, Anette Lykke; Simonsen, Anja Hviid; Yip, Tai-Tung; Hogdall, Estrid; Fung, Eric T; Lundvall, Lene; Hogdall, Claus

2009-01-01

To examine whether urine can be used to measure specific ovarian cancer proteomic profiles and whether one peak alone or in combination with other peaks or CA125 has the sensitivity and specificity to discriminate between ovarian cancer pelvic mass and benign pelvic mass. A total of 209 women were admitted for surgery for pelvic mass at the Gynaecological Department at Rigshospitalet, Copenhagen. Of the women, 156 had benign gynaecological tumors, 13 had borderline tumors and 40 had malignant epithelial ovarian cancer. The prospectively and preoperatively collected urine samples were aliquotted and frozen at -80 degrees until the time of analysis. The urine was fractionated using equalizer bead technology and then analyzed with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Biomarkers were purified and identified using combinations of chromatographic techniques and tandem mass spectrometry. Benign and malignant ovarian cancer cases were compared; 21 significantly different peaks (p<0.001) were visualized using Mann-Whitney analysis, ranging in m/z values from 1,500 to 185,000. The three most significant peaks were purified and identified as fibrinogen alpha fragment (m/z=2570.21), collagen alpha 1 (III) fragment (m/z=2707.32) and fibrinogen beta NT fragment (m/z=4425.09). The area under the receiver operator characteristic curve (ROC AUC) value for these three peaks in combination was 0.88, and their ROC AUC value in combination with CA125 was 0.96. This result supports the feasibility of using urine as a clinical diagnostic medium, and the ROC AUC value for the three most significant peaks in combination with or without CA125 demonstrates the enhanced prediction performance of combined marker analysis.

Dual-time point scanning of integrated FDG PET/CT for the evaluation of mediastinal and hilar lymph nodes in non-small cell lung cancer diagnosed as operable by contrast-enhanced CT.

PubMed

Kasai, Takami; Motoori, Ken; Horikoshi, Takuro; Uchiyama, Katsuhiro; Yasufuku, Kazuhiro; Takiguchi, Yuichi; Takahashi, Fumiaki; Kuniyasu, Yoshio; Ito, Hisao

2010-08-01

To evaluate whether dual-time point scanning with integrated fluorine-18 fluorodeoxyglucose ((18)F-FDG) positron emission tomography and computed tomography (PET/CT) is useful for evaluation of mediastinal and hilar lymph nodes in non-small cell lung cancer diagnosed as operable by contrast-enhanced CT. PET/CT data and pathological findings of 560 nodal stations in 129 patients with pathologically proven non-small cell lung cancer diagnosed as operable by contrast-enhanced CT were reviewed retrospectively. Standardized uptake values (SUVs) on early scans (SUVe) 1h, and on delayed scans (SUVd) 2h after FDG injection of each nodal station were measured. Retention index (RI) (%) was calculated by subtracting SUVe from SUVd and dividing by SUVe. Logistic regression analysis was performed with seven kinds of models, consisting of (1) SUVe, (2) SUVd, (3) RI, (4) SUVe and SUVd, (5) SUVe and RI, (6) SUVd and RI, and (7) SUVe, SUVd and RI. The seven derived models were compared by receiver-operating characteristic (ROC) analysis. k-Fold cross-validation was performed with k values of 5 and 10. p<0.05 was considered statistically significant. Model (1) including the term of SUVe showed the largest area under the ROC curve among the seven models. The cut-off probability of metastasis of 3.5% with SUVe of 2.5 revealed a sensitivity of 78% and a specificity of 81% on ROC analysis, and approximately 60% and 80% on k-fold cross-validation. Single scanning of PET/CT is sufficiently useful for evaluating mediastinal and hilar nodes for metastasis. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Anisotropy of anomalous diffusion improves the accuracy of differentiating low- and high-grade cerebral gliomas.

PubMed

Xu, Boyan; Su, Lu; Wang, Zhenxiong; Fan, Yang; Gong, Gaolang; Zhu, Wenzhen; Gao, Peiyi; Gao, Jia-Hong

2018-04-17

Anomalous diffusion model has been introduced and shown to be beneficial in clinical applications. However, only the directionally averaged values of anomalous diffusion parameters were investigated, and the anisotropy of anomalous diffusion remains unexplored. The aim of this study was to demonstrate the feasibility of using anisotropy of anomalous diffusion for differentiating low- and high-grade cerebral gliomas. Diffusion MRI images were acquired from brain tumor patients and analyzed using the fractional motion (FM) model. Twenty-two patients with histopathologically confirmed gliomas were selected. An anisotropy metric for the FM-related parameters, including the Noah exponent (α) and the Hurst exponent (H), was introduced and their values were statistically compared between the low- and high-grade gliomas. Additionally, multivariate logistic regression analysis was performed to assess the combination of the anisotropy metric and the directionally averaged value for each parameter. The diagnostic performances for grading gliomas were evaluated using a receiver operating characteristic (ROC) analysis. The Hurst exponent H was more anisotropic in high-grade than in low-grade gliomas (P = 0.015), while no significant difference was observed for the anisotropy of α. The ROC analysis revealed that larger areas under the ROC curves were produced for the combination of α (1) and the combination of H (0.813) compared with the directionally averaged α (0.979) and H (0.594), indicating an improved performance for tumor differentiation. The anisotropy of anomalous diffusion can provide distinctive information and benefit the differentiation of low- and high-grade gliomas. The utility of anisotropic anomalous diffusion may have an improved effect for investigating pathological changes in tissues. Copyright © 2018 Elsevier Inc. All rights reserved.

Bishop score and ultrasound assessment of the cervix for prediction of time to onset of labor and time to delivery in prolonged pregnancy.

PubMed

Strobel, E; Sladkevicius, P; Rovas, L; De Smet, F; Karlsson, E Dejin; Valentin, L

2006-09-01

To determine the ability of Bishop score and sonographic cervical length to predict time to spontaneous onset of labor and time to delivery in prolonged pregnancy. Ninety-seven women underwent transvaginal ultrasound examination and palpation of the cervix at 291-296 days' gestation according to ultrasound fetometry at 12-20 weeks' gestation. Sonographic cervical length and Bishop score were recorded. Multivariate logistic regression analysis was used to determine which variables were independent predictors of the onset of labor/delivery < or = 24 h, < or = 48 h, and < or = 96 h. Receiver-operating characteristics (ROC) curves were drawn to assess diagnostic performance. In nulliparous women (n = 45), both Bishop score and sonographic cervical length predicted the onset of labor/delivery < or = 24 h and < or = 48 h (area under ROC curve for the onset of labor < or = 24 h 0.79 vs. 0.80, P = 0.94; for delivery < or = 24 h 0.81 vs. 0.85, P = 0.64; for the onset of labor < or = 48 h 0.73 vs. 0.74, P = 0.90; for delivery < or = 48 h 0.77 vs. 0.71, P = 0.50). Only Bishop score discriminated between nulliparous women who went into labor/delivered < or = 96 h or > 96 h. A logistic regression model including Bishop score and cervical length was superior to Bishop score alone in predicting delivery < or = 24 h (area under ROC curve 0.93 vs. 0.81, P = 0.03) and superior to Bishop score alone and cervical length alone in predicting the onset of labor < or = 24 h (area under ROC curve 0.90 vs. 0.79, P = 0.06; and 0.90 vs. 0.80, P = 0.06). In parous women (n = 52), Bishop score and sonographic cervical length predicted the onset of labor/delivery < or = 24 h (area under ROC curve for the onset of labor 0.75 vs. 0.69, P = 0.49; for delivery 0.74 vs. 0.70, P = 0.62), but only Bishop score discriminated between women who went into labor/delivered < or = 48 h and > 48 h. Three parous women had not gone into labor and six had not given birth at 96 h. In parous women logistic regression models including both Bishop score and cervical length did not substantially improve prediction of the time to onset of labor/delivery. In prolonged pregnancy Bishop score and sonographic cervical length have a similar ability to predict the time to the onset of labor and delivery. In nulliparous women the use of logistic regression models including Bishop score and cervical length is likely to offer better prediction of the onset of labor/delivery < or = 24 h than the use of the Bishop score alone. Copyright 2006 ISUOG. Published by John Wiley & Sons, Ltd.

Serum Squamous Cell Carcinoma Antigen in Psoriasis: A Potential Quantitative Biomarker for Disease Severity.

PubMed

Sun, Ziwen; Shi, Xiaomin; Wang, Yun; Zhao, Yi

2018-06-05

An objective and quantitative method to evaluate psoriasis severity is important for practice and research in the precision care of psoriasis. We aimed to explore serum biomarkers quantitatively in association with disease severity and treatment response in psoriasis patients, with serum squamous cell carcinoma antigen (SCCA) evaluated in this pilot study. 15 psoriasis patients were treated with adalimumab. At different visits before and after treatment, quantitative body surface area (qBSA) was obtained from standardized digital body images of the patients, and the psoriasis area severity index (PASI) was also monitored. SCCA were detected by using microparticle enzyme immunoassay. The serum biomarkers were also tested in healthy volunteers as normal controls. Receiver-operating characteristic (ROC) curve analysis was used to explore the optimal cutoff point of SCCA to differentiate mild and moderate-to-severe psoriasis. The serum SCCA level in the psoriasis group was significantly higher (p < 0.05) than in the normal control group. After treatment, the serum SCCA levels were significantly decreased (p < 0.05). The SCCA level was well correlated with PASI and qBSA. In ROC analysis, when taking PASI = 10 or qBSA = 10% as the threshold, an optimal cutoff point of SCCA was found at 2.0 ng/mL with the highest Youden index. Serum SCCA might be a useful quantitative biomarker for psoriasis disease severity. © 2018 S. Karger AG, Basel.

Rollover Car Crashes with Ejection: A Deadly Combination—An Analysis of 719 Patients

PubMed Central

Latifi, Rifat; El-Menyar, Ayman; El-Hennawy, Hany; Al-Thani, Hassan

2014-01-01

Rollover car crashes (ROCs) are serious public safety concerns worldwide. Objective. To determine the incidence and outcomes of ROCs with or without ejection of occupants in the State of Qatar. Methods. A retrospective study of all patients involved in ROCs admitted to Level I trauma center in Qatar (2011-2012). Patients were divided into Group I (ROC with ejection) and Group II (ROC without ejection). Results. A total of 719 patients were evaluated (237 in Group I and 482 in Group II). The mean age in Group I was lower than in Group II (24.3 ± 10.3 versus 29 ± 12.2; P = 0.001). Group I had higher injury severity score and sustained significantly more head, chest, and abdominal injuries in comparison to Group II. The mortality rate was higher in Group I (25% versus 7%; P = 0.001). Group I patients required higher ICU admission rate (P = 0.001). Patients in Group I had a 5-fold increased risk for age-adjusted mortality (OR 5.43; 95% CI 3.11–9.49), P = 0.001). Conclusion. ROCs with ejection are associated with higher rate of morbidity and mortality compared to ROCs without ejection. As an increased number of young Qatari males sustain ROCs with ejection, these findings highlight the need for research-based injury prevention initiatives in the country. PMID:24693231

Accuracy of different diagnostic tests for early, delayed and late prosthetic joint infection.

PubMed

Fernández-Sampedro, M; Fariñas-Alvarez, C; Garces-Zarzalejo, C; Alonso-Aguirre, M A; Salas-Venero, C; Martínez-Martínez, L; Fariñas, M C

2017-08-25

A combination of laboratory, histopathological and microbiological tests for diagnosis of prosthetic joint infection (PJI) have been strongly recommended. This study aims to characterize the accuracy of individual or group tests, such as culture of sonicate fluid, synovial fluid and peri-implant tissue, C-reactive protein (CRP) and histopathology for detection of early, delayed and late PJI. A prospective study of patients undergoing hip or knee arthroplasty from February 2009 to February 2014 was performed in a Spanish tertiary health care hospital. The diagnostic accuracy of the different methods was evaluated constructing receiver-operating-characteristic (ROC) curve areas. One hundred thirty consecutive patients were included: 18 (13.8%) early PJI, 35 (27%) delayed PJI and 77 (59.2%) late PJI. For individual parameters, the area under the ROC curve for peri-implant tissue culture was larger for early (0.917) than for delayed (0.829) and late PJI (0.778), p = 0.033. There was a significantly larger difference for ROC area in the synovial fluid culture for delayed (0.803) than for early (0.781) and late infections (0.679), p = 0.039. The comparison of the areas under the ROC curves for the two microbiological tests showed that sonicate fluid was significantly different from peri-implant tissue in delayed (0.951 vs 0.829, p = 0.005) and late PJI (0.901 vs 0.778, p = 0.000). The conjunction of preoperative parameters, synovial fluid culture and CRP, improved the accuracy for late PJI (p = 0.01). The conjunction of histopathology and sonicate fluid culture increased the area under ROC curve of sonication in early (0.917 vs 1.000); p = 0.06 and late cases (0.901 vs 0.999); p < 0.001. For early PJI, sonicate fluid and peri-implant tissue cultures achieve the same best sensitivity. For delayed and late PJI, sonicate fluid culture is the most sensitive individual diagnostic method. By combining histopathology and peri-implant tissue, all early, 97% of delayed and 94.8% of late cases are diagnosed. The conjunction of histopathology and sonicate fluid culture yields a sensitivity of 100% for all types of infection.

Sensitivity to imputation models and assumptions in receiver operating characteristic analysis with incomplete data

PubMed Central

Karakaya, Jale; Karabulut, Erdem; Yucel, Recai M.

2015-01-01

Modern statistical methods using incomplete data have been increasingly applied in a wide variety of substantive problems. Similarly, receiver operating characteristic (ROC) analysis, a method used in evaluating diagnostic tests or biomarkers in medical research, has also been increasingly popular problem in both its development and application. While missing-data methods have been applied in ROC analysis, the impact of model mis-specification and/or assumptions (e.g. missing at random) underlying the missing data has not been thoroughly studied. In this work, we study the performance of multiple imputation (MI) inference in ROC analysis. Particularly, we investigate parametric and non-parametric techniques for MI inference under common missingness mechanisms. Depending on the coherency of the imputation model with the underlying data generation mechanism, our results show that MI generally leads to well-calibrated inferences under ignorable missingness mechanisms. PMID:26379316

Estimation of AUC or Partial AUC under Test-Result-Dependent Sampling.

PubMed

Wang, Xiaofei; Ma, Junling; George, Stephen; Zhou, Haibo

2012-01-01

The area under the ROC curve (AUC) and partial area under the ROC curve (pAUC) are summary measures used to assess the accuracy of a biomarker in discriminating true disease status. The standard sampling approach used in biomarker validation studies is often inefficient and costly, especially when ascertaining the true disease status is costly and invasive. To improve efficiency and reduce the cost of biomarker validation studies, we consider a test-result-dependent sampling (TDS) scheme, in which subject selection for determining the disease state is dependent on the result of a biomarker assay. We first estimate the test-result distribution using data arising from the TDS design. With the estimated empirical test-result distribution, we propose consistent nonparametric estimators for AUC and pAUC and establish the asymptotic properties of the proposed estimators. Simulation studies show that the proposed estimators have good finite sample properties and that the TDS design yields more efficient AUC and pAUC estimates than a simple random sampling (SRS) design. A data example based on an ongoing cancer clinical trial is provided to illustrate the TDS design and the proposed estimators. This work can find broad applications in design and analysis of biomarker validation studies.

Quantitative evaluation of the memory bias effect in ROC studies with PET/CT

NASA Astrophysics Data System (ADS)

Kallergi, Maria; Pianou, Nicoletta; Georgakopoulos, Alexandros; Kafiri, Georgia; Pavlou, Spiros; Chatziioannou, Sofia

2012-02-01

PURPOSE. The purpose of the study was to evaluate the memory bias effect in ROC experiments with tomographic data and, specifically, in the evaluation of two different PET/CT protocols for the detection and diagnosis of recurrent thyroid cancer. MATERIALS AND METHODS. Two readers participated in an ROC experiment that evaluated tomographic images from 43 patients followed up for thyroid cancer recurrence. Readers evaluated first whole body PET/CT scans of the patients and then a combination of whole body and high-resolution head and neck scans of the same patients. The second set was read twice. Once within 48 hours of the first set and the second time at least a month later. The detection and diagnostic performances of the readers in the three reading sessions were assessed with the DBMMRMC and LABMRMC software using the area under the ROC curve as a performance index. Performances were also evaluated by comparing the number and the size of the detected abnormal foci among the three readings. RESULTS. There was no performance difference between first and second treatments. There were statistically significant differences between first and third, and second and third treatments showing that memory can seriously affect the outcome of ROC studies. CONCLUSION. Despite the fact that tomographic data involve numerous image slices per patient, the memory bias effect is present and substantial and should be carefully eliminated from analogous ROC experiments.

Urine colorimetry for therapeutic drug monitoring of pyrazinamide during tuberculosis treatment.

PubMed

Zentner, Isaac; Modongo, Chawangwa; Zetola, Nicola M; Pasipanodya, Jotam G; Srivastava, Shashikant; Heysell, Scott K; Mpagama, Stellah; Schlect, Hans P; Gumbo, Tawanda; Bisson, Gregory P; Vinnard, Christopher

2018-03-01

Pyrazinamide is a key drug in the first-line treatment regimen for tuberculosis, with a potent sterilizing effect. Although low pyrazinamide peak serum concentrations (C max ) are associated with poor treatment outcomes, many resource-constrained settings do not have sufficient laboratory capacity to support therapeutic drug monitoring (TDM). The objective of this study was to determine whether a colorimetric test of urine can identify tuberculosis patients with adequate pyrazinamide exposures, as defined by serum C max above a target threshold. In the derivation study of healthy volunteers, three dose sizes of pyrazinamide were evaluated, and intensive pharmacokinetic blood sampling was performed over an 8-h period, with a timed urine void at 4h post-dosing. Pyrazinamide in urine was isolated by spin column centrifugation with an exchange resin, followed by colorimetric analysis; the absorbance peak at 495nm was measured. The urine assay was then evaluated in a study of 39 HIV/tuberculosis patients in Botswana enrolled in an intensive pharmacokinetic study. Receiver operating characteristics (ROC) curves were used to measure diagnostic accuracy. The guideline-recommended pyrazinamide serum C max target of 35mg/l was evaluated in the primary analysis; this target was found to be predictive of favorable outcomes in a clinical study. Following this, a higher serum C max target of 58mg/l was evaluated in the secondary analysis. At the optimal cut-off identified in the derivation sample, the urine colorimetric assay was 97% sensitive and 50% specific to identify 35 of 39 HIV/tuberculosis patients with pharmacokinetic target attainment, with an area under the ROC curve of 0.81 (95% confidence interval 0.60-0.97). Diagnostic accuracy was lower at the 58mg/l serum C max target, with an area under the ROC curve of 0.68 (95% confidence interval 0.48-0.84). Men were less likely than women to attain either serum pharmacokinetic target. The urine colorimetric assay was sensitive but not specific for the detection of adequate pyrazinamide pharmacokinetic exposures among HIV/tuberculosis patients in a high-burden setting. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Iodine concentration: a new, important characteristic of the spot sign that predicts haematoma expansion.

PubMed

Fu, Fan; Sun, Shengjun; Liu, Liping; Li, Jianying; Su, Yaping; Li, Yingying

2018-04-19

The computed tomography angiography (CTA) spot sign is a validated predictor of haematoma expansion (HE) in spontaneous intracerebral haemorrhage (SICH). We investigated whether defining the iodine concentration (IC) inside the spot sign and the haematoma on Gemstone spectral imaging (GSI) would improve its sensitivity and specificity for predicting HE. From 2014 to 2016, we prospectively enrolled 65 SICH patients who underwent single-phase spectral CTA within 6 h. Logistic regression was performed to assess the risk factors for HE. The predictive performance of individual spot sign characteristics was examined via receiver operating characteristic (ROC) analysis. The spot sign was detected in 46.1% (30/65) of patients. ROC analysis indicated that IC inside the spot sign had the greatest area under the ROC curve for HE (0.858; 95% confidence interval, 0.727-0.989; p = 0.003). Multivariate analysis found that spot sign with higher IC (i.e. IC > 7.82 100 μg/ml) was an independent predictor of HE (odds ratio = 34.27; 95% confidence interval, 5.608-209.41; p < 0.001) with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.81, 0.75, 0.90 and 0.60, respectively; while the spot sign showed sensitivity, specificity, PPV and NPV of 0.81, 0.79, 0.73 and 0.86. Logistic regression analysis indicated that the IC in haematomas was independently associated with HE (odds ratio = 1.525; 95% confidence interval, 1.041-2.235; p = 0.030). ICs in haematoma and in spot sign were all independently associated with HE. IC analysis in spectral imaging may help to identify SICH patients for targeted haemostatic therapy. • Iodine concentration in spot sign and haematoma can predict haematoma expansion • Spectral imaging could measure the IC inside the spot sign and haematoma • IC in spot sign improved the positive predictive value (PPV) cf. CTA.

Urinary Squamous Epithelial Cells Do Not Accurately Predict Urine Culture Contamination, but May Predict Urinalysis Performance in Predicting Bacteriuria.

PubMed

Mohr, Nicholas M; Harland, Karisa K; Crabb, Victoria; Mutnick, Rachel; Baumgartner, David; Spinosi, Stephanie; Haarstad, Michael; Ahmed, Azeemuddin; Schweizer, Marin; Faine, Brett

2016-03-01

The presence of squamous epithelial cells (SECs) has been advocated to identify urinary contamination despite a paucity of evidence supporting this practice. We sought to determine the value of using quantitative SECs as a predictor of urinalysis contamination. Retrospective cross-sectional study of adults (≥18 years old) presenting to a tertiary academic medical center who had urinalysis with microscopy and urine culture performed. Patients with missing or implausible demographic data were excluded (2.5% of total sample). The primary analysis aimed to determine an SEC threshold that predicted urine culture contamination using receiver operating characteristics (ROC) curve analysis. The a priori secondary analysis explored how demographic variables (age, sex, body mass index) may modify the SEC test performance and whether SECs impacted traditional urinalysis indicators of bacteriuria. A total of 19,328 records were included. ROC curve analysis demonstrated that SEC count was a poor predictor of urine culture contamination (area under the ROC curve = 0.680, 95% confidence interval [CI] = 0.671 to 0.689). In secondary analysis, the positive likelihood ratio (LR+) of predicting bacteriuria via urinalysis among noncontaminated specimens was 4.98 (95% CI = 4.59 to 5.40) in the absence of SECs, but the LR+ fell to 2.35 (95% CI = 2.17 to 2.54) for samples with more than 8 SECs/low-powered field (lpf). In an independent validation cohort, urinalysis samples with fewer than 8 SECs/lpf predicted bacteriuria better (sensitivity = 75%, specificity = 84%) than samples with more than 8 SECs/lpf (sensitivity = 86%, specificity = 70%; diagnostic odds ratio = 17.5 [14.9 to 20.7] vs. 8.7 [7.3 to 10.5]). Squamous epithelial cells are a poor predictor of urine culture contamination, but may predict poor predictive performance of traditional urinalysis measures. © 2016 by the Society for Academic Emergency Medicine.

Radiological indeterminate vestibular schwannoma and meningioma in cerebellopontine angle area: differentiating using whole-tumor histogram analysis of apparent diffusion coefficient.

PubMed

Xu, Xiao-Quan; Li, Yan; Hong, Xun-Ning; Wu, Fei-Yun; Shi, Hai-Bin

2017-02-01

To assess the role of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating radiological indeterminate vestibular schwannoma (VS) from meningioma in cerebellopontine angle (CPA). Diffusion-weighted (DW) images (b = 0 and 1000 s/mm 2 ) of pathologically confirmed and radiological indeterminate CPA meningioma (CPAM) (n = 27) and VS (n = 12) were retrospectively collected and processed with mono-exponential model. Whole-tumor regions of interest were drawn on all slices of the ADC maps to obtain histogram parameters, including the mean ADC (ADC mean ), median ADC (ADC median ), 10th/25th/75th/90th percentile ADC (ADC 10 , ADC 25 , ADC 75 and ADC 90 ), skewness and kurtosis. The differences of ADC histogram parameters between CPAM and VS were compared using unpaired t-test. Multiple receiver operating characteristic (ROC) curves analysis was used to determine and compare the diagnostic value of each significant parameter. Significant differences were found on the ADC mean , ADC median , ADC 10 , ADC 25 , ADC 75 and ADC 90 between CPAM and VS (all p values < 0.001), while no significant difference was found on kurtosis (p = 0.562) and skewness (p = 0.047). ROC curves analysis revealed, a cut-off value of 1.126 × 10 -3 mm 2 /s for the ADC 90 value generated highest area under curves (AUC) for differentiating CPAM from VS (AUC, 0.975; sensitivity, 100%; specificity, 88.9%). Histogram analysis of ADC maps based on whole tumor can be a useful tool for differentiating radiological indeterminate CPAM from VS. The ADC 90 value was the most promising parameter for differentiating these two entities.

[Relationships between soil and rocky desertification in typical karst mountain area based on redundancy analysis].

PubMed

Long, Jian; Liao, Hong-Kai; Li, Juan; Chen, Cai-Yun

2012-06-01

Redundancy analysis (RDA) was employed to reveal the relationships between soil and rocky desertification through vegetation investigation and analysis of soil samples collected in typical karst mountain area of southwest Guizhou Province. The results showed that except TP, TK and ACa, all other variables including SOC, TN, MBC, ROC, DOC, available nutrients and basal respiration showed significant downward trends during the rocky desertification process. RDA results showed significant correlations between different types of desertification and soil variables, described as non-degraded > potential desertification > light desertification > moderate desertification > severe desertification. Moreover, RDA showed that using SOC, TN, AN, and BD as soil indicators, 74.4% of the variance information on soil and rocky desertification could be explained. Furthermore, the results of correlation analysis showed that soil variables were significantly affected by surface vegetation. Considering the ecological function of the aboveground vegetation and the soil quality, Zanthoxylum would be a good choice for restoration of local vegetation in karst mountain area.

Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features

NASA Astrophysics Data System (ADS)

Naghibi, Seyed Amir; Moradi Dashtpagerdi, Mostafa

2017-01-01

One important tool for water resources management in arid and semi-arid areas is groundwater potential mapping. In this study, four data-mining models including K-nearest neighbor (KNN), linear discriminant analysis (LDA), multivariate adaptive regression splines (MARS), and quadric discriminant analysis (QDA) were used for groundwater potential mapping to get better and more accurate groundwater potential maps (GPMs). For this purpose, 14 groundwater influence factors were considered, such as altitude, slope angle, slope aspect, plan curvature, profile curvature, slope length, topographic wetness index (TWI), stream power index, distance from rivers, river density, distance from faults, fault density, land use, and lithology. From 842 springs in the study area, in the Khalkhal region of Iran, 70 % (589 springs) were considered for training and 30 % (253 springs) were used as a validation dataset. Then, KNN, LDA, MARS, and QDA models were applied in the R statistical software and the results were mapped as GPMs. Finally, the receiver operating characteristics (ROC) curve was implemented to evaluate the performance of the models. According to the results, the area under the curve of ROCs were calculated as 81.4, 80.5, 79.6, and 79.2 % for MARS, QDA, KNN, and LDA, respectively. So, it can be concluded that the performances of KNN and LDA were acceptable and the performances of MARS and QDA were excellent. Also, the results depicted high contribution of altitude, TWI, slope angle, and fault density, while plan curvature and land use were seen to be the least important factors.

Developing a clinical utility framework to evaluate prediction models in radiogenomics

NASA Astrophysics Data System (ADS)

Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

2015-03-01

Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

Do We Know Who Will Drop out?: A Review of the Predictors of Dropping out of High School--Precision, Sensitivity, and Specificity

ERIC Educational Resources Information Center

Bowers, Alex J.; Sprott, Ryan; Taff, Sherry A.

2013-01-01

The purpose of this study is to review the literature on the most accurate indicators of students at risk of dropping out of high school. We used Relative Operating Characteristic (ROC) analysis to compare the sensitivity and specificity of 110 dropout flags across 36 studies. Our results indicate that 1) ROC analysis provides a means to compare…

Untargeted mass spectrometry-based metabolomic profiling of pleural effusions: fatty acids as novel cancer biomarkers for malignant pleural effusions.

PubMed

Lam, Ching-Wan; Law, Chun-Yiu

2014-09-05

Untargeted mass spectrometry-based metabolomic profiling is a powerful analytical method used for broad-spectrum identification and quantification of metabolites in biofluids in human health and disease states. In this study, we exploit metabolomic profiling for cancer biomarker discovery for diagnosis of malignant pleural effusions. We envisage the result will be clinically useful since currently there are no cancer biomarkers that are accurate enough for the diagnosis of malignant pleural effusions. Metabolomes of 32 malignant pleural effusions from lung cancer patients and 18 benign effusions from patients with pulmonary tuberculosis were analyzed using reversed-phase liquid chromatography tandem mass spectrometry (LC-MS/MS) using AB SCIEX TripleTOF 5600. MS spectra were analyzed using XCMS, PeakView, and LipidView. Metabolome-Wide Association Study (MWAS) was performed by Receiver Operating Characteristic Curve Explorer and Tester (ROCCET). Insignificant markers were filtered out using a metabolome-wide significance level (MWSL) with p-value < 2 × 10(-5) for t test. Only compounds in Human Metabolome Database (HMDB) will be used as cancer biomarkers. ROCCET analysis of ESI positive and negative MS spectra revealed free fatty acid (FFA) 18:1 (oleic acid) had the largest area-under-ROC of 0.96 (95% CI = 0.87-1.00) in malignant pleural effusions. Using a ratio of FFA 18:1-to-ceramide (d18:1/16:0), the area-under-ROC was further increased to 0.99 (95% CI = 0.91-1.00) with sensitivity 93.8% and specificity 100.0%. Using untargeted metabolomic profiling, the diagnostic cancer biomarker with the largest area-under-ROC can be determined objectively. This lipogenic phenotype could be explained by overexpression of fatty acid synthase (FASN) in cancer cells. The diagnostic performance of FFA 18:1-to-ceramide (d18:1/16:0) ratio supports its use for diagnosis of malignant pleural effusions.

Fluorescence spectroscopy for diagnosis of squamous intraepithelial lesions of the cervix.

PubMed

Mitchell, M F; Cantor, S B; Ramanujam, N; Tortolero-Luna, G; Richards-Kortum, R

1999-03-01

To calculate receiver operating characteristic (ROC) curves for fluorescence spectroscopy in order to measure its performance in the diagnosis of squamous intraepithelial lesions (SILs) and to compare these curves with those for other diagnostic methods: colposcopy, cervicography, speculoscopy, Papanicolaou smear screening, and human papillomavirus (HPV) testing. Data from our previous clinical study were used to calculate ROC curves for fluorescence spectroscopy. Curves for other techniques were calculated from other investigators' reports. To identify these, a MEDLINE search for articles published from 1966 to 1996 was carried out, using the search terms "colposcopy," "cervicoscopy," "cervicography," "speculoscopy," "Papanicolaou smear," "HPV testing," "fluorescence spectroscopy," and "polar probe" in conjunction with the terms "diagnosis," "positive predictive value," "negative predictive value," and "receiver operating characteristic curve." We found 270 articles, from which articles were selected if they reported results of studies involving high-disease-prevalence populations, reported findings of studies in which colposcopically directed biopsy was the criterion standard, and included sufficient data for recalculation of the reported sensitivities and specificities. We calculated ROC curves for fluorescence spectroscopy using Bayesian and neural net algorithms. A meta-analytic approach was used to calculate ROC curves for the other techniques. Areas under the curves were calculated. Fluorescence spectroscopy using the neural net algorithm had the highest area under the ROC curve, followed by fluorescence spectroscopy using the Bayesian algorithm, followed by colposcopy, the standard diagnostic technique. Cervicography, Papanicolaou smear screening, and HPV testing performed comparably with each other but not as well as fluorescence spectroscopy and colposcopy. Fluorescence spectroscopy performs better than colposcopy and other techniques in the diagnosis of SILs. Because it also permits real-time diagnosis and has the potential of being used by inexperienced health care personnel, this technology holds bright promise.

Diagnostic evaluation of three cardiac software packages using a consecutive group of patients

PubMed Central

2011-01-01

Purpose The aim of this study was to compare the diagnostic performance of the three software packages 4DMSPECT (4DM), Emory Cardiac Toolbox (ECTb), and Cedars Quantitative Perfusion SPECT (QPS) for quantification of myocardial perfusion scintigram (MPS) using a large group of consecutive patients. Methods We studied 1,052 consecutive patients who underwent 2-day stress/rest 99mTc-sestamibi MPS studies. The reference/gold-standard classifications for the MPS studies were obtained from three physicians, with more than 25 years each of experience in nuclear cardiology, who re-evaluated all MPS images. Automatic processing was carried out using 4DM, ECTb, and QPS software packages. Total stress defect extent (TDE) and summed stress score (SSS) based on a 17-segment model were obtained from the software packages. Receiver-operating characteristic (ROC) analysis was performed. Results A total of 734 patients were classified as normal and the remaining 318 were classified as having infarction and/or ischemia. The performance of the software packages calculated as the area under the SSS ROC curve were 0.87 for 4DM, 0.80 for QPS, and 0.76 for ECTb (QPS vs. ECTb p = 0.03; other differences p < 0.0001). The area under the TDE ROC curve were 0.87 for 4DM, 0.82 for QPS, and 0.76 for ECTb (QPS vs. ECTb p = 0.0005; other differences p < 0.0001). Conclusion There are considerable differences in performance between the three software packages with 4DM showing the best performance and ECTb the worst. These differences in performance should be taken in consideration when software packages are used in clinical routine or in clinical studies. PMID:22214226

Analytical and clinical performance of thyroglobulin autoantibody assays in thyroid cancer follow-up.

PubMed

Katrangi, Waddah; Grebe, Stephan K G; Algeciras-Schimnich, Alicia

2017-10-26

While thyroglobulin autoantibodies (TgAb) can result in false low serum thyroglobulin (Tg) immunoassay (IA) measurements, they might also be indicators of disease persistence/recurrence. Hence, accurate TgAb measurement, in addition to Tg quantification, is crucial for thyroid cancer monitoring. We compared the analytical and clinical performance of four commonly used TgAb IAs. We measured Tg by mass spectrometry (Tg-MS) and by four pairs of Tg and TgAb IAs (Beckman, Roche, Siemens, Thermo) in 576 samples. Limit of quantitation (LOQ) and manufacturers' upper reference interval cut-off (URI) were used for comparisons. Clinical performance was assessed by receiving operator characteristics (ROC) curve analysis. Quantitative and qualitative agreement between TgAb-IAs was moderate with R2 of 0.20-0.70 and κ from 0.41-0.66 using LOQ and 0.47-0.71 using URI. In samples with TgAb interference, detection rates of TgAb were similar using LOQ and URI for Beckman, Siemens, and Thermo, but much lower for the Roche TgAb-IA when the URI was used. In TgAb positive cases, the ROC areas under the curve (AUC) for the TgAb-IAs were 0.59 (Beckman), 0.62 (Siemens), 0.59 (Roche), and 0.59 (Thermo), similar to ROC AUCs achieved with Tg. Combining Tg and TgAb measurements improved the ROC AUCs compared to Tg or TgAb alone. TgAb-IAs show significant qualitative and quantitative differences. For 2 of the 4 TgAb-IAs, using the LOQ improves the detection of interfering TgAbs. All assays showed suboptimal clinical performance when used as surrogate markers of disease, with modest improvements when Tg and TgAb were combined.

Magnetic Resonance Imaging of Intracranial Hypotension: Diagnostic Value of Combined Qualitative Signs and Quantitative Metrics.

PubMed

Aslan, Kerim; Gunbey, Hediye Pinar; Tomak, Leman; Ozmen, Zafer; Incesu, Lutfi

The aim of this study was to investigate whether the use of combination quantitative metrics (mamillopontine distance [MPD], pontomesencephalic angle, and mesencephalon anterior-posterior/medial-lateral diameter ratios) with qualitative signs (dural enhancement, subdural collections/hematoma, venous engorgement, pituitary gland enlargements, and tonsillar herniations) provides a more accurate diagnosis of intracranial hypotension (IH). The quantitative metrics and qualitative signs of 34 patients and 34 control subjects were assessed by 2 independent observers. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of quantitative metrics and qualitative signs, and for the diagnosis of IH, optimum cutoff values of quantitative metrics were found with ROC analysis. Combined ROC curve was measured for the quantitative metrics, and qualitative signs combinations in determining diagnostic accuracy and sensitivity, specificity, and positive and negative predictive values were found, and the best model combination was formed. Whereas MPD and pontomesencephalic angle were significantly lower in patients with IH when compared with the control group (P < 0.001), mesencephalon anterior-posterior/medial-lateral diameter ratio was significantly higher (P < 0.001). For qualitative signs, the highest individual distinctive power was dural enhancement with area under the ROC curve (AUC) of 0.838. For quantitative metrics, the highest individual distinctive power was MPD with AUC of 0.947. The best accuracy in the diagnosis of IH was obtained by combination of dural enhancement, venous engorgement, and MPD with an AUC of 1.00. This study showed that the combined use of dural enhancement, venous engorgement, and MPD had diagnostic accuracy of 100 % for the diagnosis of IH. Therefore, a more accurate IH diagnosis can be provided with combination of quantitative metrics with qualitative signs.

Diagnostic accuracy of routine blood examinations and CSF lactate level for post-neurosurgical bacterial meningitis.

PubMed

Zhang, Yang; Xiao, Xiong; Zhang, Junting; Gao, Zhixian; Ji, Nan; Zhang, Liwei

2017-06-01

To evaluate the diagnostic accuracy of routine blood examinations and Cerebrospinal Fluid (CSF) lactate level for Post-neurosurgical Bacterial Meningitis (PBM) at a large sample-size of post-neurosurgical patients. The diagnostic accuracies of routine blood examinations and CSF lactate level to distinguish between PAM and PBM were evaluated with the values of the Area Under the Curve of the Receiver Operating Characteristic (AUC -ROC ) by retrospectively analyzing the datasets of post-neurosurgical patients in the clinical information databases. The diagnostic accuracy of routine blood examinations was relatively low (AUC -ROC <0.7). The CSF lactate level achieved rather high diagnostic accuracy (AUC -ROC =0.891; CI 95%, 0.852-0.922). The variables of patient age, operation duration, surgical diagnosis and postoperative days (the interval days between the neurosurgery and examinations) were shown to affect the diagnostic accuracy of these examinations. The variables were integrated with routine blood examinations and CSF lactate level by Fisher discriminant analysis to improve their diagnostic accuracy. As a result, the diagnostic accuracy of blood examinations and CSF lactate level was significantly improved with an AUC -ROC value=0.760 (CI 95%, 0.737-0.782) and 0.921 (CI 95%, 0.887-0.948) respectively. The PBM diagnostic accuracy of routine blood examinations was relatively low, whereas the accuracy of CSF lactate level was high. Some variables that are involved in the incidence of PBM can also affect the diagnostic accuracy for PBM. Taking into account the effects of these variables significantly improves the diagnostic accuracies of routine blood examinations and CSF lactate level. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Tumor necrosis factor receptor 1 (TNFRI) for ventilator-associated pneumonia diagnosis by cytokine multiplex analysis.

PubMed

Martin-Loeches, Ignacio; Bos, Lieuwe D; Povoa, Pedro; Ramirez, Paula; Schultz, Marcus J; Torres, Antoni; Artigas, Antonio

2015-12-01

The diagnosis of ventilator-associated pneumonia (VAP) is challenging. An important aspect to improve outcome is early recognition of VAP and the initiation of the appropriate empirical treatment. We hypothesized that biological markers in plasma can rule out VAP at the moment of clinical suspicion and could rule in VAP before the diagnosis can be made clinically. In this prospective study, patients with VAP (n = 24, microbiology confirmed) were compared to controls (n = 19) with a similar duration of mechanical ventilation. Blood samples from the day of VAP diagnosis and 1 and 3 days before were analyzed with a multiplex array for markers of inflammation, coagulation, and apoptosis. The best biomarker combination was selected and the diagnostic accuracy was given by the area under the receiver operating characteristic curve (ROC-AUC). TNF-receptor 1 (TNFRI) and granulocyte colony-stimulating factor (GCSF) were selected as optimal biomarkers at the day of VAP diagnosis, which resulted in a ROC-AUC of 0.96, with excellent sensitivity. Three days before the diagnosis TNFRI and plasminogen activator inhibitor-1 (PAI-1) levels in plasma predicted VAP with a ROC-AUC of 0.79. The slope of IL-10 and PAI-1 resulted in a ROC-AUC of 0.77. These biomarkers improved the classification of the clinical pulmonary infection score when combined. Concentration of TNFRI and PAI-1 and the slope of PAI-1 and IL-10 may be used to predict the development of VAP as early as 3 days before the diagnosis made clinically. TNFRI and GCSF may be used to exclude VAP at the moment of clinical suspicion. Especially TNFRI seems to be a promising marker for the prediction and diagnosis of VAP.

Pulse Oximetry for the Detection of Obstructive Sleep Apnea Syndrome: Can the Memory Capacity of Oxygen Saturation Influence Their Diagnostic Accuracy?

PubMed Central

Nigro, Carlos A.; Dibur, Eduardo; Rhodius, Edgardo

2011-01-01

Objective. To assess the diagnostic ability of WristOx 3100 using its three different recording settings in patients with suspected obstructive sleep apnea syndrome (OSAS). Methods. All participants (135) performed the oximetry (three oximeters WristOx 3100) and polysomnography (PSG) simultaneously in the sleep laboratory. Both recordings were interpreted blindly. Each oximeter was set to one of three different recording settings (memory capabilities 0.25, 0.5, and 1 Hz). The software (nVision 5.1) calculated the adjusted O2 desaturation index-mean number of O2 desaturation per hour of analyzed recording ≥2, 3, and 4% (ADI2, 3, and 4). The ADI2, 3, and 4 cutoff points that better discriminated between subjects with or without OSAS arose from the receiver-operator characteristics (ROCs) curve analysis. OSAS was defined as a respiratory disturbance index (RDI) ≥ 5. Results. 101 patients were included (77 men, mean age 52, median RDI 22.6, median BMI 27.4 kg/m2). The area under the ROCs curves (AUC-ROCs) of ADI2, 3, and 4 with different data storage rates were similar (AUC-ROCs with data storage rates of 0.25/0.5/1 Hz: ADI2: 0.958/0.948/0.965, ADI3: 0.961/0.95/0.966, and ADI4: 0.957/0.949/0.963, P NS). Conclusions. The ability of WristOx 3100 to detect patients with OSAS was not affected by the data storage rate of the oxygen saturation signal. Both memory capacity of 0.25, 0.5, or 1 Hz showed a similar performance for the diagnosis of OSAS. PMID:23471171

Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective, longitudinal study

PubMed Central

Wang, Haoyu; Liu, Aihua; Zhao, Tong; Gong, Xun; Pang, Tianxiao; Zhou, Yingying; Xiao, Yue; Yan, Yumeng; Fan, Chenling; Teng, Weiping; Lai, Yaxin; Shan, Zhongyan

2017-01-01

Objectives Our study aimed to distinguish the ability of anthropometric indices to assess the risk of metabolic syndrome (MetS). Design Prospective cohort study. Setting Shenyang, China. Participants A total of 379 residents aged between 40 and 65 were enrolled. 253 of them were free of MetS and had been followed up for 4.5 years. Methods At baseline, all the participants underwent a thorough medical examination. A variety of anthropometric parameters were measured and calculated, including waist circumference (WC), body mass index (BMI), a body shape index (ABSI), abdominal volume index (AVI), body adiposity index, body roundness index, conicity index, waist-to-hip ratio and visceral adiposity index (VAI). After 4.5 year follow-up, we re-examined whether participants were suffering from MetS. A receiver operating characteristic (ROC) curve was applied to examine the potential of the above indices to identify the status and risk of MetS. Outcomes Occurrence of MetS. Results At baseline, 33.2% participants suffered from MetS. All of the anthropometric indices showed clinical significance, and VAI was superior to the other indices as it was found to have the largest area under the ROC curve. After a 4.5 year follow-up, 37.8% of men and 23.9% of women developed MetS. ROC curve analysis suggested that baseline BMI was the strongest predictor of MetS for men (0.77 (0.68–0.85)), and AVI was the strongest for women (0.72 (0.64–0.79)). However, no significant difference was observed between WC and both indices. In contrast, the baseline ABSI did not predict MetS in both genders. Conclusions The present study indicated that these different indices derived from anthropometric parameters have different discriminatory abilities for MetS. Although WC did not have the largest area under the ROC curve for diagnosing and predicting MetS, it may remain a better index of MetS status and risk because of its simplicity and wide use. PMID:28928179

Power calculation for comparing diagnostic accuracies in a multi-reader, multi-test design.

PubMed

Kim, Eunhee; Zhang, Zheng; Wang, Youdan; Zeng, Donglin

2014-12-01

Receiver operating characteristic (ROC) analysis is widely used to evaluate the performance of diagnostic tests with continuous or ordinal responses. A popular study design for assessing the accuracy of diagnostic tests involves multiple readers interpreting multiple diagnostic test results, called the multi-reader, multi-test design. Although several different approaches to analyzing data from this design exist, few methods have discussed the sample size and power issues. In this article, we develop a power formula to compare the correlated areas under the ROC curves (AUC) in a multi-reader, multi-test design. We present a nonparametric approach to estimate and compare the correlated AUCs by extending DeLong et al.'s (1988, Biometrics 44, 837-845) approach. A power formula is derived based on the asymptotic distribution of the nonparametric AUCs. Simulation studies are conducted to demonstrate the performance of the proposed power formula and an example is provided to illustrate the proposed procedure. © 2014, The International Biometric Society.

3D quantitative breast ultrasound analysis for differentiating fibroadenomas and carcinomas smaller than 1cm.

PubMed

Meel-van den Abeelen, A S S; Weijers, G; van Zelst, J C M; Thijssen, J M; Mann, R M; de Korte, C L

2017-03-01

In (3D) ultrasound, accurate discrimination of small solid masses is difficult, resulting in a high frequency of biopsies for benign lesions. In this study, we investigate whether 3D quantitative breast ultrasound (3DQBUS) analysis can be used for improving non-invasive discrimination between benign and malignant lesions. 3D US studies of 112 biopsied solid breast lesions (size <1cm), were included (34 fibroadenomas and 78 invasive ductal carcinomas). The lesions were manually delineated and, based on sonographic criteria used by radiologists, 3 regions of interest were defined in 3D for analysis: ROI (ellipsoid covering the inside of the lesion), PER (peritumoural surrounding: 0.5mm around the lesion), and POS (posterior-tumoural acoustic phenomena: region below the lesion with the same size as delineated for the lesion). After automatic gain correction (AGC), the mean and standard deviation of the echo level within the regions were calculated. For the ROI and POS also the residual attenuation coefficient was estimated in decibel per cm [dB/cm]. The resulting eight features were used for classification of the lesions by a logistic regression analysis. The classification accuracy was evaluated by leave-one-out cross-validation. Receiver operating characteristic (ROC) curves were constructed to assess the performance of the classification. All lesions were delineated by two readers and results were compared to assess the effect of the manual delineation. The area under the ROC curve was 0.86 for both readers. At 100% sensitivity, a specificity of 26% and 50% was achieved for reader 1 and 2, respectively. Inter-reader variability in lesion delineation was marginal and did not affect the accuracy of the technique. The area under the ROC curve of 0.86 was reached for the second reader when the results of the first reader were used as training set yielding a sensitivity of 100% and a specificity of 40%. Consequently, 3DQBUS would have achieved a 40% reduction in biopsies for benign lesions for reader 2, without a decrease in sensitivity. This study shows that 3DQBUS is a promising technique to classify suspicious breast lesions as benign, potentially preventing unnecessary biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves.

PubMed

Carter, Jane V; Pan, Jianmin; Rai, Shesh N; Galandiuk, Susan

2016-06-01

It is vital for clinicians to understand and interpret correctly medical statistics as used in clinical studies. In this review, we address current issues and focus on delivering a simple, yet comprehensive, explanation of common research methodology involving receiver operating characteristic (ROC) curves. ROC curves are used most commonly in medicine as a means of evaluating diagnostic tests. Sample data from a plasma test for the diagnosis of colorectal cancer were used to generate a prediction model. These are actual, unpublished data that have been used to describe the calculation of sensitivity, specificity, positive predictive and negative predictive values, and accuracy. The ROC curves were generated to determine the accuracy of this plasma test. These curves are generated by plotting the sensitivity (true-positive rate) on the y axis and 1 - specificity (false-positive rate) on the x axis. Curves that approach closest to the coordinate (x = 0, y = 1) are more highly predictive, whereas ROC curves that lie close to the line of equality indicate that the result is no better than that obtained by chance. The optimum sensitivity and specificity can be determined from the graph as the point where the minimum distance line crosses the ROC curve. This point corresponds to the Youden index (J), a function of sensitivity and specificity used commonly to rate diagnostic tests. The area under the curve is used to quantify the overall ability of a test to discriminate between 2 outcomes. By following these simple guidelines, interpretation of ROC curves will be less difficult and they can then be interpreted more reliably when writing, reviewing, or analyzing scientific papers. Copyright © 2016 Elsevier Inc. All rights reserved.

Sensitivity and specificity of machine learning classifiers and spectral domain OCT for the diagnosis of glaucoma.

PubMed

Vidotti, Vanessa G; Costa, Vital P; Silva, Fabrício R; Resende, Graziela M; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S

2012-06-15

Purpose. To investigate the sensitivity and specificity of machine learning classifiers (MLC) and spectral domain optical coherence tomography (SD-OCT) for the diagnosis of glaucoma. Methods. Sixty-two patients with early to moderate glaucomatous visual field damage and 48 healthy individuals were included. All subjects underwent a complete ophthalmologic examination, achromatic standard automated perimetry, and RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, California, USA). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters. Subsequently, the following MLCs were tested: Classification Tree (CTREE), Random Forest (RAN), Bagging (BAG), AdaBoost M1 (ADA), Ensemble Selection (ENS), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Naive-Bayes (NB), and Support Vector Machine (SVM). Areas under the ROC curves (aROCs) obtained for each parameter and each MLC were compared. Results. The mean age was 57.0±9.2 years for healthy individuals and 59.9±9.0 years for glaucoma patients (p=0.103). Mean deviation values were -4.1±2.4 dB for glaucoma patients and -1.5±1.6 dB for healthy individuals (p<0.001). The SD-OCT parameters with the greater aROCs were inferior quadrant (0.813), average thickness (0.807), 7 o'clock position (0.765), and 6 o'clock position (0.754). The aROCs from classifiers varied from 0.785 (ADA) to 0.818 (BAG). The aROC obtained with BAG was not significantly different from the aROC obtained with the best single SD-OCT parameter (p=0.93). Conclusions. The SD-OCT showed good diagnostic accuracy in a group of patients with early glaucoma. In this series, MLCs did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.

Defining a new diagnostic assessment parameter for wound care: Elevated protease activity, an indicator of nonhealing, for targeted protease-modulating treatment.

PubMed

Serena, Thomas E; Cullen, Breda M; Bayliff, Simon W; Gibson, Molly C; Carter, Marissa J; Chen, Lingyun; Yaakov, Raphael A; Samies, John; Sabo, Matthew; DeMarco, Daniel; Le, Namchi; Galbraith, James

2016-05-01

It is widely accepted that elevated protease activity (EPA) in chronic wounds impedes healing. However, little progress has occurred in quantifying the level of protease activity that is detrimental for healing. The aim of this study was to determine the relationship between inflammatory protease activity and wound healing status, and to establish the level of EPA above which human neutrophil-derived elastase (HNE) and matrix metalloproteases (MMP) activities correlate with nonhealing wounds. Chronic wound swab samples (n = 290) were collected from four wound centers across the USA to measure HNE and MMP activity. Healing status was determined according to percentage reduction in wound area over the previous 2-4 weeks; this was available for 211 wounds. Association between protease activity and nonhealing wounds was determined by receiver operating characteristic analysis (ROC), a statistical technique used for visualizing and analyzing the performance of diagnostic tests. ROC analysis showed that area under the curve (AUC) for HNE were 0.69 for all wounds and 0.78 for wounds with the most reliable wound trajectory information, respectively. For MMP, the corresponding AUC values were 0.70 and 0.82. Analysis suggested that chronic wounds having values of HNE >5 and/or MMP ≥13, should be considered wound healing impaired. EPA is indicative of nonhealing wounds. Use of a diagnostic test to detect EPA in clinical practice could enable clinicians to identify wounds that are nonhealing, thus enabling targeted treatment with protease modulating therapies. © 2016 by the Wound Healing Society.

A Primer on Receiver Operating Characteristic Analysis and Diagnostic Efficiency Statistics for Pediatric Psychology: We Are Ready to ROC

PubMed Central

2014-01-01

Objective To offer a practical demonstration of receiver operating characteristic (ROC) analyses, diagnostic efficiency statistics, and their application to clinical decision making using a popular parent checklist to assess for potential mood disorder. Method Secondary analyses of data from 589 families seeking outpatient mental health services, completing the Child Behavior Checklist and semi-structured diagnostic interviews. Results Internalizing Problems raw scores discriminated mood disorders significantly better than did age- and gender-normed T scores, or an Affective Problems score. Internalizing scores <8 had a diagnostic likelihood ratio <0.3, and scores >30 had a diagnostic likelihood ratio of 7.4. Conclusions This study illustrates a series of steps in defining a clinical problem, operationalizing it, selecting a valid study design, and using ROC analyses to generate statistics that support clinical decisions. The ROC framework offers important advantages for clinical interpretation. Appendices include sample scripts using SPSS and R to check assumptions and conduct ROC analyses. PMID:23965298

A primer on receiver operating characteristic analysis and diagnostic efficiency statistics for pediatric psychology: we are ready to ROC.

PubMed

Youngstrom, Eric A

2014-03-01

To offer a practical demonstration of receiver operating characteristic (ROC) analyses, diagnostic efficiency statistics, and their application to clinical decision making using a popular parent checklist to assess for potential mood disorder. Secondary analyses of data from 589 families seeking outpatient mental health services, completing the Child Behavior Checklist and semi-structured diagnostic interviews. Internalizing Problems raw scores discriminated mood disorders significantly better than did age- and gender-normed T scores, or an Affective Problems score. Internalizing scores <8 had a diagnostic likelihood ratio <0.3, and scores >30 had a diagnostic likelihood ratio of 7.4. This study illustrates a series of steps in defining a clinical problem, operationalizing it, selecting a valid study design, and using ROC analyses to generate statistics that support clinical decisions. The ROC framework offers important advantages for clinical interpretation. Appendices include sample scripts using SPSS and R to check assumptions and conduct ROC analyses.

Bayesian modeling and inference for diagnostic accuracy and probability of disease based on multiple diagnostic biomarkers with and without a perfect reference standard.

PubMed

Jafarzadeh, S Reza; Johnson, Wesley O; Gardner, Ian A

2016-03-15

The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle. Copyright © 2015 John Wiley & Sons, Ltd.

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

PubMed

Haker, Steven; Wells, William M; Warfield, Simon K; Talos, Ion-Florin; Bhagwat, Jui G; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H

2005-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation*

PubMed Central

Haker, Steven; Wells, William M.; Warfield, Simon K.; Talos, Ion-Florin; Bhagwat, Jui G.; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H.

2010-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging. PMID:16685884

Exploring the utility of narrative analysis in diagnostic decision making: picture-bound reference, elaboration, and fetal alcohol spectrum disorders.

PubMed

Thorne, John C; Coggins, Truman E; Carmichael Olson, Heather; Astley, Susan J

2007-04-01

To evaluate classification accuracy and clinical feasibility of a narrative analysis tool for identifying children with a fetal alcohol spectrum disorder (FASD). Picture-elicited narratives generated by 16 age-matched pairs of school-aged children (FASD vs. typical development [TD]) were coded for semantic elaboration and reference strategy by judges who were unaware of age, gender, and group membership of the participants. Receiver operating characteristic (ROC) curves were used to examine the classification accuracy of the resulting set of narrative measures for making 2 classifications: (a) for the 16 children diagnosed with FASD, low performance (n = 7) versus average performance (n = 9) on a standardized expressive language task and (b) FASD (n = 16) versus TD (n = 16). Combining the rates of semantic elaboration and pragmatically inappropriate reference perfectly matched a classification based on performance on the standardized language task. More importantly, the rate of ambiguous nominal reference was highly accurate in classifying children with an FASD regardless of their performance on the standardized language task (area under the ROC curve = .863, confidence interval = .736-.991). Results support further study of the diagnostic utility of narrative analysis using discourse level measures of elaboration and children's strategic use of reference.

[Mobbing, organizational dysfunction and bio-psycho-social effects: an integrated assessment. Preliminary data for the validation of the Questionnaire in the Neapoletan dialect on Distress at Work(Qn-DL)].

PubMed

Nolfe, Giovanni; Petrella, Claudio; Triassi, Maria; Zontini, Gemma; Uttieri, Simona; Pagliaro, Alessia; Blasi, Francesco; Cappuccio, Antonella; Nolfe, Giuseppe

2013-01-01

The aim of this study is to produce preliminary data about the validation of the "Naples-Questionnaire of Distress at Work" (nQ.DW). This inventory is a new assessment tool in order to evaluate the distress perceived in the working environment by means of the differentiation of the conditions linked to the mobbing from which related to organizational disfunction. The nQ-DW also measures the bio-psycho-social global effects of these two phenomena. The questionnaire has been administered to workers suffering of a psychopathological disturbance related to work distress and to a control group matched for the sociodemographic and working variables. The statistical analysis demonstrated a significant validity and reliability. The degree of internal coherence was satisfactory. The ROC curves allow the determination of a threshold value which allows to separate the workers subjected to mobbing and/or organizational stress from control-workers with an optimal reliability degree. The values of the area under the ROC curves show that the inventory has a high discriminating capacity. Future studies, based on a greater sample size, will be oriented to the analysis of the questionnaire by means of multivariate techniques like the factorial analysis.

Development and Validation Study of the Internet Overuse Screening Questionnaire

PubMed Central

Lee, Han-Kyeong; Lee, Hae-Woo; Han, Joo Hyun; Park, Subin; Ju, Seok-Jin; Choi, Kwanwoo; Lee, Ji Hyeon; Jeon, Hong Jin

2018-01-01

Objective Concerns over behavioral and emotional problems caused by excessive internet usage have been developed. This study intended to develop and a standardize questionnaire that can efficiently identify at-risk internet users through their internet usage habits. Methods Participants (n=158) were recruited at six I-will-centers located in Seoul, South Korea. From the initial 36 questionnaire item pool, 28 preliminary items were selected through expert evaluation and panel discussions. The construct validity, internal consistency, and concurrent validity were examined. We also conducted Receiver Operating Curve (ROC) analysis to assess diagnostic ability of the Internet Overuse Screening-Questionnaire (IOS-Q). Results The exploratory factor analysis yielded a five factor structure. Four factors with 17 items remained after items that had unclear factor loading were removed. The Cronbach’s alpha for the IOS-Q total score was 0.91, and test-retest reliability was 0.72. The correlation between Young’s internet addiction scale and K-scale supported concurrent validity. ROC analysis showed that the IOS-Q has superior diagnostic ability with the Area Under the Curve of 0.87. At the cut-off point of 25.5, the sensitivity was 0.93 and specificity was 0.86. Conclusion Overall, this study supports the use of IOS-Q for internet addiction research and for screening high-risk individuals. PMID:29669406

Persistent free radicals in carbon-based materials on transformation of refractory organic contaminants (ROCs) in water: A critical review.

PubMed

Qin, Yaxin; Li, Guiying; Gao, Yanpeng; Zhang, Lizhi; Ok, Yong Sik; An, Taicheng

2018-06-15

With the increased concentrations and kinds of refractory organic contaminants (ROCs) in aquatic environments, many previous reviews systematically summarized the applications of carbon-based materials in the adsorption and catalytic degradation of ROCs for their economically viable and environmentally friendly behavior. Interestingly, recent studies indicated that carbon-based materials in natural environment can also mediate the transformation of ROCs directly or indirectly due to their abundant persistent free radicals (PFRs). Understanding the formation mechanisms of PFRs in carbo-based materials and their interactions with ROCs is essential to develop their further applications in environment remediation. However, there is no comprehensive review so far about the direct and indirect removal of ROCs mediated by PFRs in amorphous, porous and crystalline carbon-based materials. The review aims to evaluate the formation mechanisms of PFRs in carbon-based materials synthesized through pyrolysis and hydrothermal carbonization processes. The influence of synthesis conditions (temperature and time) and carbon sources on the types as well as the concentrations of PFRs in carbon-based materials are also discussed. In particular, the effects of metals on the concentrations and types of PFRs in carbon-based materials are highlighted because they are considered as the catalysts for the formation of PFRs. The formation mechanisms of reactive species and the further transformation mechanisms of ROCs are briefly summarized, and the surface properties of carbon-based materials including surface area, types and number of functional groups, etc. are found to be the key parameters controlling their activities. However, due to diversity and complexity of carbon-based materials, the exact relationships between the activities of carbon-based materials and PFRs are still uncertain. Finally, the existing problems and current challenges for the ROCs transformation with carbon-based materials are also pointed out. Copyright © 2018 Elsevier Ltd. All rights reserved.

External validation of the ability of the DRAGON score to predict outcome after thrombolysis treatment.

PubMed

Ovesen, C; Christensen, A; Nielsen, J K; Christensen, H

2013-11-01

Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant plasminogen activator between 2009 and 2011 were included. Upon admission all patients underwent physical and neurological examination using the National Institutes of Health Stroke Scale along with non-contrast CT scans and CT angiography. Patients were followed up through the Outpatient Clinic and their modified Rankin Scale (mRS) was assessed after 3 months. Three hundred and three patients were included in the analysis. The DRAGON scale proved to have a good discriminative ability for predicting highly unfavourable outcome (mRS 5-6) (area under the curve-receiver operating characteristic [AUC-ROC]: 0.89; 95% confidence interval [CI] 0.81-0.96; p<0.001) and good outcome (mRS 0-2) (AUC-ROC: 0.79; 95% CI 0.73-0.85; p<0.001). When only patients with M1 occlusions were selected the DRAGON scale provided good discriminative capability (AUC-ROC: 0.89; 95% CI 0.78-1.0; p=0.003) for highly unfavourable outcome. We confirmed the validity of the DRAGON scale in predicting outcome after thrombolysis treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Diagnostic value of fibronectin discriminant score for predicting liver fibrosis stages in chronic hepatitis C virus patients.

PubMed

Attallah, Abdelfattah M; Abdallah, Sanaa O; Attallah, Ahmed A; Omran, Mohamed M; Farid, Khaled; Nasif, Wesam A; Shiha, Gamal E; Abdel-Aziz, Abdel-Aziz F; Rasafy, Nancy; Shaker, Yehia M

2013-01-01

Several noninvasive predictive models were developed to substitute liver biopsy for fibrosis assessment. To evaluate the diagnostic value of fibronectin which reflect extracellular matrix metabolism and standard liver functions tests which reflect alterations in hepatic functions. Chronic hepatitis C (CHC) patients (n = 145) were evaluated using ROC curves and stepwise multivariate discriminant analysis (MDA) and was validated in 180 additional patients. Liver biochemical profile including transaminases, bilirubin, alkaline phosphatase, albumin, complete blood count were estimated. Fibronectin concentration was determined using monoclonal antibody and ELISA. A novel index named fibronectin discriminant score (FDS) based on fibronectin, APRI and albumin was developed. FDS produced areas under ROC curves (AUC) of 0.91 for significant fibrosis and 0.81 for advanced fibrosis. The FDS correctly classified 79% of the significant liver fibrosis patients (F2-F4) with 87% sensitivity and 75% specificity. The relative risk [odds ratio (OR)] of having significant liver fibrosis using the cut-off values determined by ROC curve analyses were 6.1 for fibronectin, 4.9 for APRI, and 4.2 for albumin. FDS predicted liver fibrosis with an OR of 16.8 for significant fibrosis and 8.6 for advanced fibrosis. The FDS had similar AUC and OR in the validation group to the estimation group without statistically significant difference. FDS predicted liver fibrosis with high degree of accuracy, potentially decreasing the number of liver biopsy required.

First trimester prediction of maternal glycemic status.

PubMed

Gabbay-Benziv, Rinat; Doyle, Lauren E; Blitzer, Miriam; Baschat, Ahmet A

2015-05-01

To predict gestational diabetes mellitus (GDM) or normoglycemic status using first trimester maternal characteristics. We used data from a prospective cohort study. First trimester maternal characteristics were compared between women with and without GDM. Association of these variables with sugar values at glucose challenge test (GCT) and subsequent GDM was tested to identify key parameters. A predictive algorithm for GDM was developed and receiver operating characteristics (ROC) statistics was used to derive the optimal risk score. We defined normoglycemic state, when GCT and all four sugar values at oral glucose tolerance test, whenever obtained, were normal. Using same statistical approach, we developed an algorithm to predict the normoglycemic state. Maternal age, race, prior GDM, first trimester BMI, and systolic blood pressure (SBP) were all significantly associated with GDM. Age, BMI, and SBP were also associated with GCT values. The logistic regression analysis constructed equation and the calculated risk score yielded sensitivity, specificity, positive predictive value, and negative predictive value of 85%, 62%, 13.8%, and 98.3% for a cut-off value of 0.042, respectively (ROC-AUC - area under the curve 0.819, CI - confidence interval 0.769-0.868). The model constructed for normoglycemia prediction demonstrated lower performance (ROC-AUC 0.707, CI 0.668-0.746). GDM prediction can be achieved during the first trimester encounter by integration of maternal characteristics and basic measurements while normoglycemic status prediction is less effective.

Cardiopulmonary exercise testing for the prediction of morbidity risk after rectal cancer surgery.

PubMed

West, M A; Parry, M G; Lythgoe, D; Barben, C P; Kemp, G J; Grocott, M P W; Jack, S

2014-08-01

This study investigated the relationship between objectively measured physical fitness variables derived by cardiopulmonary exercise testing (CPET) and in-hospital morbidity after rectal cancer surgery. Patients scheduled for rectal cancer surgery underwent preoperative CPET (reported blind to patient characteristics) with recording of morbidity (recorded blind to CPET variables). Non-parametric receiver operating characteristic (ROC) curves and logistic regression were used to assess the relationship between CPET variables and postoperative morbidity. Of 105 patients assessed, 95 (72 men) were included; ten patients had no surgery and were excluded (3 by choice, 7 owing to unresectable metastasis). Sixty-eight patients had received neoadjuvant treatment. ROC curve analysis of oxygen uptake (V˙o2 ) at estimated lactate threshold (θ^L ) and peak V˙o2 gave an area under the ROC curve of 0·87 (95 per cent confidence interval 0·78 to 0·95; P < 0·001) and 0·85 (0·77 to 0·93; P < 0·001) respectively, indicating that they can help discriminate patients at risk of postoperative morbidity. The optimal cut-off points identified were 10·6 and 18·6 ml per kg per min for V˙o2 at θ^L and peak respectively. CPET can help predict morbidity after rectal cancer surgery. © 2014 BJS Society Ltd. Published by John Wiley & Sons Ltd.

Cerebrospinal fluid cytokines in the diagnosis of bacterial meningitis in infants.

PubMed

Srinivasan, Lakshmi; Kilpatrick, Laurie; Shah, Samir S; Abbasi, Soraya; Harris, Mary C

2016-10-01

Bacterial meningitis poses diagnostic challenges in infants. Antibiotic pretreatment and low bacterial density diminish cerebrospinal fluid (CSF) culture yield, while laboratory parameters do not reliably identify bacterial meningitis. Pro and anti-inflammatory cytokines are elevated in bacterial meningitis and may be useful diagnostic adjuncts when CSF cultures are negative. In a prospective cohort study of infants, we used cytometric bead arrays to measure tumor necrosis factor alpha (TNF-α), interleukin 1 (IL-1), IL-6, IL-8, IL-10, and IL-12 in CSF. Receiver operating characteristic (ROC) analyses and Principal component analysis (PCA) were used to determine cytokine combinations that identified bacterial meningitis. Six hundred and eighty four infants < 6 mo were included; 11 had culture-proven bacterial meningitis. IL-6 and IL-10 were the individual cytokines possessing greatest accuracy in diagnosis of culture proven bacterial meningitis (ROC analyses; area under the concentration-time curve (AUC) 0.91; 0.9103 respectively), and performed as well as, or better than combinations identified using ROC and PCA. CSF cytokines were highly correlated with each other and with CSF white blood cell count (WBC) counts in infants with meningitis. A subset of antibiotic pretreated culture-negative subjects demonstrated cytokine patterns similar to culture positive subjects. CSF cytokine levels may aid diagnosis of bacterial meningitis, and facilitate decision-making regarding treatment for culture negative meningitis.

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays

PubMed Central

Goodman, Corey W.; Major, Heather J.; Walls, William D.; Sheffield, Val C.; Casavant, Thomas L.; Darbro, Benjamin W.

2016-01-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. PMID:25595567

A comparison of the validity of GHQ-12 and CHQ-12 in Chinese primary care patients in Manchester.

PubMed

Pan, P C; Goldberg, D P

1990-11-01

The present study compares the efficacy of the GHQ-12 and the Chinese Health Questionnaire (CHQ-12) in Cantonese speaking Chinese primary-care patients living in Greater Manchester, using relative operating characteristic (ROC) analysis. We did not find that the Chinese version offered any advantage over the conventional version of the GHQ in this population. Stepwise discriminant analysis however confirmed the value of individual items in the former pertaining to specific somatic symptoms and interpersonal relationships in differentiating cases from non-cases. Information biases, arising from the lack of a reliability study on the second-stage case identifying interview and the unique linguistic characteristics of the Chinese language may have affected the overall validity indices of the questionnaires. The study also examines the effects of using different criteria to define a case, and shows that with increasing levels of severity, there is an improvement in the diagnostic performance of the two questionnaires as reflected by areas under ROC curves and traditional validity indices. Possible explanations of these findings are discussed. The scoring method proposed by Goodchild & Duncan-Jones (1985) when used on these questionnaires had no demonstrable advantage over the conventional scoring method.

Extent of agreement between the body fluid model of Sysmex XN-20 and the manual microscopy method.

PubMed

Huang, Wei-Hua; Lu, Lin-Peng; Wu, Kang; Guo, Fang-Yu; Guo, Jie; Yu, Jing-Long; Zhou, Dao-Yin; Sun, Yi; Deng, An-Mei

2017-09-01

Although the correlations concerning cellular component analysis between the Sysmex XN-20 body fluid (BF) model and manual microscopy have been investigated by several studies, the extent of agreement between these two methods has not been investigated. A total of 90 BF samples were prospectively collected and analyzed using the Sysmex XN-20 BF model and microscopy. The extent of agreement between these two methods was evaluated using the Bland-Altman approach. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic accuracy of high-fluorescence (HF) BF cells for malignant diseases. The agreements of white blood cell (WBC), red blood cell (RBC), and percentages of neutrophils, lymphocytes, and monocytes between the Sysmex XN-20 BF model and manual microscopy were imperfect. The areas under the ROC curves for absolute and relative HF cells were 0.67 (95% confidence interval [CI]: 0.56-0.78) and 0.60 (95% CI: 0.48-0.72), respectively. Due to the Sysmex XN-20 BF model's imperfect agreement with manual microscopy and its weak diagnostic accuracy for malignant diseases, the current evidence does not support replacing manual microscopy with this model in clinical practice. © 2016 Wiley Periodicals, Inc.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

Using BIRADS categories in ROC experiments

NASA Astrophysics Data System (ADS)

Kallergi, Maria; Hersh, Marla R.; Thomas, Jerry A.

2002-04-01

This paper investigated the use of the Breast Imaging Reporting And Data System (BIRADS) Lexicon in ROC mammography experiments. Analysis was based on data from parallel ROC experiments performed at two Institutions with different readers and databases to compare film to digitized mammography. Seven readers participated in the studies and read approximately 200 cases each in two formats: film and digital or softcopy. Reporting was done using BIRADS categories 1 through 5. Training was done with a separate set of cases and included detailed review of the relationship between BIRADS and a standard ROC discrete 5-point rating scale. The results from both sites showed equivalency between film and softcopy mammography. Decisions using the BIRADS categories showed no unsampled ROC regions and no degenerate data. Fits yielded smooth ROC curves that correlated to clinical practice. In a qualitative evaluation, all observers indicated preference in using the BIRADS classes instead of a discrete or continuous rating scheme. Familiarity with the rating process seems to relieve some of the bias associated with the interpretation of digitized mammograms from computer monitors (softcopy reading). Our results suggested that BIRADS categories can be used in comparative ROC studies because they represent a scale familiar to the reader that can be followed consistently and they provide a rating approach that accounts for both positive and negative cases to be evaluated and categorized.

The Prognostic Role of Angiotensin II Type 1 Receptor Autoantibody in Non-Gravid Hypertension and Pre-eclampsia

PubMed Central

Lei, Jinghui; Li, Yafeng; Zhang, Suli; Wu, Ye; Wang, Pengli; Liu, Huirong

2016-01-01

Abstract Angiotensin II type 1 receptor autoantibody (AT1-AA) is found in patients with non-gravid hypertension or pre-eclampsia, but the relationship is uncertain. The aim of the present study was to assess the association between AT1-AA and high blood pressure using meta-analysis, and to evaluate the prognosis value of AT1-AA for hypertensive diseases. Literature search from PubMed, Embase, and Cochrane databases were conducted using keywords “hypertension” or “pre-eclampsia,” “angiotensin II receptor type 1 autoantibody,” and its aliases from April 1999 to December 2015. Studies evaluating the association between AT1-AA and non-gravid hypertension or pre-eclampsia were included in this analysis. The quality of the eligible studies was assessed based on the Newcastle–Ottawa Scale with some modifications. Two researchers then independently reviewed all included studies and extracted all relevant data. Association between AT1-AA and hypertension was tested with pooled odds ratios (ORs) and 95% confidence intervals (CIs). Finally, we evaluated whether AT1-AA predicted the prognosis of hypertension by using a summary receiver-operating characteristic (ROC) curve and sensitivity analysis. Ten studies were finally included in this meta-analysis. AT1-AA showed more significant association with pre-eclampsia than that with non-gravid hypertension (pooled OR 32.84, 95% CI 17.19–62.74; and pooled OR 4.18, 95% CI 2.20–7.98, respectively). Heterogeneity among studies was also detected probably due to different hypertensive subtypes and AT1-AA measuring methods. Area under summary ROC curve (AUC) of pre-eclampsia was 0.92 (sensitivity 0.76; specificity 0.86). Area under the ROC curve of overall hypertensive diseases or non-gravid hypertension was lower than that of pre-eclampsia (0.86 and 0.72, respectively) with lower sensitivities (0.46 and 0.26, respectively). The major limitation of this analysis was the publication bias due to lack of unpublished data and the language limitation during literature search. Prospective study with large simple size and specific measuring data collection are needed to enhance our findings in the future. Our analysis confirms that elevated AT1-AA in serum is significantly associated with hypertensive disorder, especially pre-eclampsia. AT1-AA may be a valuable indicator for poorer prognosis of patients with pre-eclampsia, and could be used in patients with hypertensive disease for risk evaluation and making individual treatment decision. PMID:27124051

Whole Body Magnetic Resonance Imaging Features in Diffuse Idiopathic Skeletal Hyperostosis in Conjunction with Clinical Variables to Whole Body MRI and Clinical Variables in Ankylosing Spondylitis.

PubMed

Weiss, Bettina G; Bachmann, Lucas M; Pfirrmann, Christian W A; Kissling, Rudolf O; Zubler, Veronika

2016-02-01

Discrimination of diffuse idiopathic skeletal hyperostosis (DISH) and ankylosing spondylitis (AS) can be challenging. Usefulness of whole-body magnetic resonance imaging (WB-MRI) in diagnosing spondyloarthritis has been recently proved. We assessed the value of clinical variables alone and in combination with WB-MRI to distinguish between DISH and AS. Diagnostic case-control study: 33 patients with AS and 15 patients with DISH were included. All patients underwent 1.5 Tesla WB-MRI scanning. MR scans were read by a blinded radiologist using the Canadian-Danish Working Group's recommendation. Imaging and clinical variables were identified using the bootstrap. The most important variables from MR and clinical history were assessed in a multivariate fashion resulting in 3 diagnostic models (MRI, clinical, and combined). The discriminative capacity was quantified using the area under the receiver-operating characteristic (ROC) curve. The strength of diagnostic variables was quantified with OR. Forty-eight patients provided 1545 positive findings (193 DISH/1352 AS). The final MR model contained upper anterior corner fat infiltration (32 DISH/181 AS), ankylosis on the vertebral endplate (4 DISH/60 AS), facet joint ankylosis (4 DISH/49 AS), sacroiliac joint edema (11 DISH/91 AS), sacroiliac joint fat infiltration (2 DISH/114 AS), sacroiliac joint ankylosis (2 DISH/119 AS); area under the ROC curve was 0.71, 95% CI 0.64-0.78. The final clinical model contained patient's age and body mass index (area under the ROC curve 0.90, 95% CI 0.89-0.91). The full diagnostic model containing clinical and MR information had an area under the ROC curve of 0.93 (95% CI 0.92-0.95). WB-MRI features can contribute to the correct diagnosis after a thorough conventional workup of patients with DISH and AS.

The ROC Toolbox: A toolbox for analyzing receiver-operating characteristics derived from confidence ratings.

PubMed

Koen, Joshua D; Barrett, Frederick S; Harlow, Iain M; Yonelinas, Andrew P

2017-08-01

Signal-detection theory, and the analysis of receiver-operating characteristics (ROCs), has played a critical role in the development of theories of episodic memory and perception. The purpose of the current paper is to present the ROC Toolbox. This toolbox is a set of functions written in the Matlab programming language that can be used to fit various common signal detection models to ROC data obtained from confidence rating experiments. The goals for developing the ROC Toolbox were to create a tool (1) that is easy to use and easy for researchers to implement with their own data, (2) that can flexibly define models based on varying study parameters, such as the number of response options (e.g., confidence ratings) and experimental conditions, and (3) that provides optimal routines (e.g., Maximum Likelihood estimation) to obtain parameter estimates and numerous goodness-of-fit measures.The ROC toolbox allows for various different confidence scales and currently includes the models commonly used in recognition memory and perception: (1) the unequal variance signal detection (UVSD) model, (2) the dual process signal detection (DPSD) model, and (3) the mixture signal detection (MSD) model. For each model fit to a given data set the ROC toolbox plots summary information about the best fitting model parameters and various goodness-of-fit measures. Here, we present an overview of the ROC Toolbox, illustrate how it can be used to input and analyse real data, and finish with a brief discussion on features that can be added to the toolbox.

Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers.

PubMed

Zangwill, Linda M; Chan, Kwokleung; Bowd, Christopher; Hao, Jicuang; Lee, Te-Won; Weinreb, Robert N; Sejnowski, Terrence J; Goldbaum, Michael H

2004-09-01

To determine whether topographical measurements of the parapapillary region analyzed by machine learning classifiers can detect early to moderate glaucoma better than similarly processed measurements obtained within the disc margin and to improve methods for optimization of machine learning classifier feature selection. One eye of each of 95 patients with early to moderate glaucomatous visual field damage and of each of 135 normal subjects older than 40 years participating in the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS) were included. Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) mean height contour was measured in 36 equal sectors, both along the disc margin and in the parapapillary region (at a mean contour line radius of 1.7 mm). Each sector was evaluated individually and in combination with other sectors. Gaussian support vector machine (SVM) learning classifiers were used to interpret HRT sector measurements along the disc margin and in the parapapillary region, to differentiate between eyes with normal and glaucomatous visual fields and to compare the results with global and regional HRT parameter measurements. The area under the receiver operating characteristic (ROC) curve was used to measure diagnostic performance of the HRT parameters and to evaluate the cross-validation strategies and forward selection and backward elimination optimization techniques that were used to generate the reduced feature sets. The area under the ROC curve for mean height contour of the 36 sectors along the disc margin was larger than that for the mean height contour in the parapapillary region (0.97 and 0.85, respectively). Of the 36 individual sectors along the disc margin, those in the inferior region between 240 degrees and 300 degrees, had the largest area under the ROC curve (0.85-0.91). With SVM Gaussian techniques, the regional parameters showed the best ability to discriminate between normal eyes and eyes with glaucomatous visual field damage, followed by the global parameters, mean height contour measures along the disc margin, and mean height contour measures in the parapapillary region. The area under the ROC curve was 0.98, 0.94, 0.93, and 0.85, respectively. Cross-validation and optimization techniques demonstrated that good discrimination (99% of peak area under the ROC curve) can be obtained with a reduced number of HRT parameters. Mean height contour measurements along the disc margin discriminated between normal and glaucomatous eyes better than measurements obtained in the parapapillary region. Copyright Association for Research in Vision and Ophthalmology

Heidelberg Retina Tomograph Measurements of the Optic Disc and Parapapillary Retina for Detecting Glaucoma Analyzed by Machine Learning Classifiers

PubMed Central

Zangwill, Linda M.; Chan, Kwokleung; Bowd, Christopher; Hao, Jicuang; Lee, Te-Won; Weinreb, Robert N.; Sejnowski, Terrence J.; Goldbaum, Michael H.

2010-01-01

Purpose To determine whether topographical measurements of the parapapillary region analyzed by machine learning classifiers can detect early to moderate glaucoma better than similarly processed measurements obtained within the disc margin and to improve methods for optimization of machine learning classifier feature selection. Methods One eye of each of 95 patients with early to moderate glaucomatous visual field damage and of each of 135 normal subjects older than 40 years participating in the longitudinal Diagnostic Innovations in Glaucoma Study (DIGS) were included. Heidelberg Retina Tomograph (HRT; Heidelberg Engineering, Dossenheim, Germany) mean height contour was measured in 36 equal sectors, both along the disc margin and in the parapapillary region (at a mean contour line radius of 1.7 mm). Each sector was evaluated individually and in combination with other sectors. Gaussian support vector machine (SVM) learning classifiers were used to interpret HRT sector measurements along the disc margin and in the parapapillary region, to differentiate between eyes with normal and glaucomatous visual fields and to compare the results with global and regional HRT parameter measurements. The area under the receiver operating characteristic (ROC) curve was used to measure diagnostic performance of the HRT parameters and to evaluate the cross-validation strategies and forward selection and backward elimination optimization techniques that were used to generate the reduced feature sets. Results The area under the ROC curve for mean height contour of the 36 sectors along the disc margin was larger than that for the mean height contour in the parapapillary region (0.97 and 0.85, respectively). Of the 36 individual sectors along the disc margin, those in the inferior region between 240° and 300°, had the largest area under the ROC curve (0.85–0.91). With SVM Gaussian techniques, the regional parameters showed the best ability to discriminate between normal eyes and eyes with glaucomatous visual field damage, followed by the global parameters, mean height contour measures along the disc margin, and mean height contour measures in the parapapillary region. The area under the ROC curve was 0.98, 0.94, 0.93, and 0.85, respectively. Cross-validation and optimization techniques demonstrated that good discrimination (99% of peak area under the ROC curve) can be obtained with a reduced number of HRT parameters. Conclusions Mean height contour measurements along the disc margin discriminated between normal and glaucomatous eyes better than measurements obtained in the parapapillary region. PMID:15326133

On analyzing free-response data on location level

NASA Astrophysics Data System (ADS)

Bandos, Andriy I.; Obuchowski, Nancy A.

2017-03-01

Free-response ROC (FROC) data are typically collected when primary question of interest is focused on the proportions of the correct detection-localization of known targets and frequencies of false positive responses, which can be multiple per subject (image). These studies are particularly relevant for CAD and related applications. The fundamental tool of the location-level FROC analysis is the FROC curve. Although there are many methods of FROC analysis, as we describe in this work, some of the standard and popular approaches, while important, are not suitable for analyzing specifically the location-level FROC performance as summarized by the FROC curve. Analysis of the FROC curve, on the other hand, might not be straightforward. Recently we developed an approach for the location-level analysis of the FROC data using the well-known tools for clustered ROC analysis. In the current work, based on previously developed concepts, and using specific examples, we demonstrate the key reasons why specifically location-level FROC performance cannot be fully addressed by the common approaches as well as illustrate the proposed solution. Specifically, we consider the two most salient FROC approaches, namely JAFROC and the area under the exponentially transformed FROC curve (AFE) and show that clearly superior FROC curves can have lower values for these indices. We describe the specific features that make these approaches inconsistent with FROC curves. This work illustrates some caveats for using the common approaches for location-level FROC analysis and provides guidelines for the appropriate assessment or comparison of FROC systems.

Application of texture analysis method for classification of benign and malignant thyroid nodules in ultrasound images.

PubMed

Abbasian Ardakani, Ali; Gharbali, Akbar; Mohammadi, Afshin

2015-01-01

The aim of this study was to evaluate computer aided diagnosis (CAD) system with texture analysis (TA) to improve radiologists' accuracy in identification of thyroid nodules as malignant or benign. A total of 70 cases (26 benign and 44 malignant) were analyzed in this study. We extracted up to 270 statistical texture features as a descriptor for each selected region of interests (ROIs) in three normalization schemes (default, 3s and 1%-99%). Then features by the lowest probability of classification error and average correlation coefficients (POE+ACC), and Fisher coefficient (Fisher) eliminated to 10 best and most effective features. These features were analyzed under standard and nonstandard states. For TA of the thyroid nodules, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-Linear Discriminant Analysis (NDA) were applied. First Nearest-Neighbour (1-NN) classifier was performed for the features resulting from PCA and LDA. NDA features were classified by artificial neural network (A-NN). Receiver operating characteristic (ROC) curve analysis was used for examining the performance of TA methods. The best results were driven in 1-99% normalization with features extracted by POE+ACC algorithm and analyzed by NDA with the area under the ROC curve ( Az) of 0.9722 which correspond to sensitivity of 94.45%, specificity of 100%, and accuracy of 97.14%. Our results indicate that TA is a reliable method, can provide useful information help radiologist in detection and classification of benign and malignant thyroid nodules.

DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans.

PubMed

Main, Keith L; Soman, Salil; Pestilli, Franco; Furst, Ansgar; Noda, Art; Hernandez, Beatriz; Kong, Jennifer; Cheng, Jauhtai; Fairchild, Jennifer K; Taylor, Joy; Yesavage, Jerome; Wesson Ashford, J; Kraemer, Helena; Adamson, Maheen M

2017-01-01

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n  = 109; Age: M  = 47.2, SD  = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

Modification and Validation of the Triglyceride-to-HDL Cholesterol Ratio as a Surrogate of Insulin Sensitivity in White Juveniles and Adults without Diabetes Mellitus: The Single Point Insulin Sensitivity Estimator (SPISE).

PubMed

Paulmichl, Katharina; Hatunic, Mensud; Højlund, Kurt; Jotic, Aleksandra; Krebs, Michael; Mitrakou, Asimina; Porcellati, Francesca; Tura, Andrea; Bergsten, Peter; Forslund, Anders; Manell, Hannes; Widhalm, Kurt; Weghuber, Daniel; Anderwald, Christian-Heinz

2016-09-01

The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio was introduced as a tool to estimate insulin resistance, because circulating lipid measurements are available in routine settings. Insulin, C-peptide, and free fatty acids are components of other insulin-sensitivity indices but their measurement is expensive. Easier and more affordable tools are of interest for both pediatric and adult patients. Study participants from the Relationship Between Insulin Sensitivity and Cardiovascular Disease [43.9 (8.3) years, n = 1260] as well as the Beta-Cell Function in Juvenile Diabetes and Obesity study cohorts [15 (1.9) years, n = 29] underwent oral-glucose-tolerance tests and euglycemic clamp tests for estimation of whole-body insulin sensitivity and calculation of insulin sensitivity indices. To refine the TG/HDL ratio, mathematical modeling was applied including body mass index (BMI), fasting TG, and HDL cholesterol and compared to the clamp-derived M-value as an estimate of insulin sensitivity. Each modeling result was scored by identifying insulin resistance and correlation coefficient. The Single Point Insulin Sensitivity Estimator (SPISE) was compared to traditional insulin sensitivity indices using area under the ROC curve (aROC) analysis and χ(2) test. The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C(0.185)/(TG(0.2) × BMI(1.338)), with fasting HDL-C (mg/dL), fasting TG concentrations (mg/dL), and BMI (kg/m(2)). A cutoff value of 6.61 corresponds to an M-value smaller than 4.7 mg · kg(-1) · min(-1) (aROC, M:0.797). SPISE showed a significantly better aROC than the TG/HDL-C ratio. SPISE aROC was comparable to the Matsuda ISI (insulin sensitivity index) and equal to the QUICKI (quantitative insulin sensitivity check index) and HOMA-IR (homeostasis model assessment-insulin resistance) when calculated with M-values. The SPISE seems well suited to surrogate whole-body insulin sensitivity from inexpensive fasting single-point blood draw and BMI in white adolescents and adults. © 2016 American Association for Clinical Chemistry.

ROC analysis of the accuracy of Noncycloplegic retinoscopy, Retinomax Autorefractor, and SureSight Vision Screener for preschool vision screening.

PubMed

Ying, Gui-shuang; Maguire, Maureen; Quinn, Graham; Kulp, Marjean Taylor; Cyert, Lynn

2011-12-28

To evaluate, by receiver operating characteristic (ROC) analysis, the accuracy of three instruments of refractive error in detecting eye conditions among 3- to 5-year-old Head Start preschoolers and to evaluate differences in accuracy between instruments and screeners and by age of the child. Children participating in the Vision In Preschoolers (VIP) Study (n = 4040), had screening tests administered by pediatric eye care providers (phase I) or by both nurse and lay screeners (phase II). Noncycloplegic retinoscopy (NCR), the Retinomax Autorefractor (Nikon, Tokyo, Japan), and the SureSight Vision Screener (SureSight, Alpharetta, GA) were used in phase I, and Retinomax and SureSight were used in phase II. Pediatric eye care providers performed a standardized eye examination to identify amblyopia, strabismus, significant refractive error, and reduced visual acuity. The accuracy of the screening tests was summarized by the area under the ROC curve (AUC) and compared between instruments and screeners and by age group. The three screening tests had a high AUC for all categories of screening personnel. The AUC for detecting any VIP-targeted condition was 0.83 for NCR, 0.83 (phase I) to 0.88 (phase II) for Retinomax, and 0.86 (phase I) to 0.87 (phase II) for SureSight. The AUC was 0.93 to 0.95 for detecting group 1 (most severe) conditions and did not differ between instruments or screeners or by age of the child. NCR, Retinomax, and SureSight had similar and high accuracy in detecting vision disorders in preschoolers across all types of screeners and age of child, consistent with previously reported results at specificity levels of 90% and 94%.

Diagnostic yield of ink-jet prints from digital radiographs for the assessment of approximal carious lesions: ROC-analysis.

PubMed

Schulze, Ralf K W; Grimm, Stefanie; Schulze, Dirk; Voss, Kai; Keller, Hans-Peter; Wedel, Matthias

2011-08-01

To investigate the diagnostic quality of different quality, individually calibrated ink-jet printers for the very challenging dental radiographic task of approximal carious lesion detection. A test-pattern evaluating resolution, contrast and homogeneity of the ink-jet prints was developed. 50 standardized dental radiographs each showing two neighbouring teeth in natural contact were printed on glossy paper with calibrated, randomly selected ink-jet printers (Canon S520 and iP4500, Epson Stylus Photo R2400). Printing size equalled the viewing size on a 17″ cathode-ray-tube monitor daily quality-tested according to German regulations. The true caries status was determined from serial sectioning and microscopic evaluation. 16 experienced observers evaluated the radiographs on a five-point confidence scale on all prints plus the viewing monitor with respect to the visibility of a carious lesion. A non-parametric Receiver-Operating Characteristics (ROC-) analysis was performed explicitly designed for the evaluation of readings stemming from identical samples but different modality. Significant differences are expressed by a critical ratio z exceeding ±2. Diagnostic accuracy was determined by the area (Az) underneath the ROC-curves. Average Az-values ranged between 0.62 (S520 and R2400) and 0.64 (monitor, iP4500), with no significant difference between modalities (P=0.172). Neither significant (range mean z: -0.40 (S520) and -0.11 (iP4500)) nor clinically relevant differences were found between printers and viewing monitor. Our results for a challenging task in dental radiography indicate that calibrated, off-the-shelf ink-jet printers are able to reproduce (dental) radiographs at quality levels sufficient for radiographic diagnosis in a typical dental working environment. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Cut-Offs and Response Criteria for the Hospital Universitario La Princesa Index (HUPI) and Their Comparison to Widely-Used Indices of Disease Activity in Rheumatoid Arthritis.

PubMed

González-Álvaro, Isidoro; Castrejón, Isabel; Ortiz, Ana M; Toledano, Esther; Castañeda, Santos; García-Vadillo, Alberto; Carmona, Loreto

2016-01-01

To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal [PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide [EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if ≤2, low disease activity if >2 and ≤5), moderate if >5 and <9 and high if ≥9. HUPI's AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28's AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis.

Usefulness of Tc-99m MDP spine SPECT imaging in differentiating malignant from benign lesions in cancer patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryu, J.S.; Moon, D.H.; Shin, M.J.

1994-05-01

Solitary or a few spinal abnormalities on planar bone scan pose a dilemma in cancer patients. The purpose of this study was to evaluate the usefulness of spine SPECT imaging in differential diagnosis of malignant and benign lesion. Subjects were 54 adult patients with solitary or a few equivocal vertebral lesions on planar bone scan. Spine SPECT imaging was obtained by a triple head SPECT system (TRIAD, Trionix). The final diagnoses were based on data from biopsy, other imaging studies, or minimum 1 year of follow up. Two blind observers reviewed the planar image first, then both planar and SPECTmore » images. The uptake patterns on SPECT images were analyzed, and the diagnostic performance was evaluated by the ROC analysis. Thirty three lesions of 22 patients were malignant, and 60 lesions of 32 patients were benign. Common characteristic patterns of malignant lesions were focal or segmental hot uptake in the body, hot uptake in the body and pedicle, and cold defect with surrounding hot uptake in the vertebra. Whereas marginal protruding hot uptakes in endplate, and hot uptakes in facet joints were benign. The ROC analysis showed that SPECT improved the diagnostic performance (the area under the ROC curve of two observers for planar image 0.903 and 0.791, for the combination of planar and SPECT : 0.950 and 0.976). In conclusion, the uptake pattern recognition in spine SPECT provides useful information for differential diagnosis of malignant and benign lesions in vertebra. Spine SPECT is a valuable complement in cancer patients with inconclusive findings on planar bone scan.« less

Cut-Offs and Response Criteria for the Hospital Universitario La Princesa Index (HUPI) and Their Comparison to Widely-Used Indices of Disease Activity in Rheumatoid Arthritis

PubMed Central

Castrejón, Isabel; Ortiz, Ana M.; Toledano, Esther; Castañeda, Santos; García-Vadillo, Alberto; Carmona, Loreto

2016-01-01

Objective To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Methods Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal [PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide [EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. Results The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if ≤2, low disease activity if >2 and ≤5), moderate if >5 and <9 and high if ≥9. HUPI’s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28’s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. Conclusions The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis. PMID:27603313

Controlling Nutritional Status (CONUT) score is a prognostic marker for gastric cancer patients after curative resection.

PubMed

Kuroda, Daisuke; Sawayama, Hiroshi; Kurashige, Junji; Iwatsuki, Masaaki; Eto, Tsugio; Tokunaga, Ryuma; Kitano, Yuki; Yamamura, Kensuke; Ouchi, Mayuko; Nakamura, Kenichi; Baba, Yoshifumi; Sakamoto, Yasuo; Yamashita, Yoichi; Yoshida, Naoya; Chikamoto, Akira; Baba, Hideo

2018-03-01

Controlling Nutritional Status (CONUT), as calculated from serum albumin, total cholesterol concentration, and total lymphocyte count, was previously shown to be useful for nutritional assessment. The current study investigated the potential use of CONUT as a prognostic marker in gastric cancer patients after curative resection. Preoperative CONUT was retrospectively calculated in 416 gastric cancer patients who underwent curative resection at Kumamoto University Hospital from 2005 to 2014. The patients were divided into two groups: CONUT-high (≥4) and CONUT-low (≤3), according to time-dependent receiver operating characteristic (ROC) analysis. The associations of CONUT with clinicopathological factors and survival were evaluated. CONUT-high patients were significantly older (p < 0.001) and had a lower body mass index (p = 0.019), deeper invasion (p < 0.001), higher serum carcinoembryonic antigen (p = 0.037), and higher serum carbohydrate antigen 19-9 (p = 0.007) compared with CONUT-low patients. CONUT-high patients had significantly poorer overall survival (OS) compared with CONUT-low patients according to univariate and multivariate analyses (hazard ratio: 5.09, 95% confidence interval 3.12-8.30, p < 0.001). In time-dependent ROC analysis, CONUT had a higher area under the ROC curve (AUC) for the prediction of 5-year OS than the neutrophil lymphocyte ratio, the Modified Glasgow Prognostic Score, or pStage. When the time-dependent AUC curve was used to predict OS, CONUT tended to maintain its predictive accuracy for long-term survival at a significantly higher level for an extended period after surgery when compared with the other markers tested. CONUT is useful for not only estimating nutritional status but also for predicting long-term OS in gastric cancer patients after curative resection.

A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.

Effectiveness of computer aided detection for solitary pulmonary nodules

NASA Astrophysics Data System (ADS)

Yan, Jiayong; Li, Wenjie; Du, Xiangying; Lu, Huihai; Xu, Jianrong; Xu, Mantao; Rong, Dongdong

2009-02-01

This study is to investigate the incremental effect of using a high performance computer-aided detection (CAD) system in detection of solitary pulmonary nodules in chest radiographs. The Kodak Chest CAD system was evaluated by a panel of six radiologists at different levels of experience. The observer study consisted of two independent phases: readings without CAD and readings with assistance of CAD. The study was conducted over a set of chest radiographs comprising 150 cancer cases and 150 cancer-free cases. The actual sensitivity of the CAD system is 72% with 3.7 false positives per case. Receiver operating characteristic (ROC) analysis was used to assess the overall observer performance. The AUZ (area under ROC curve) showed a significantly improvement (P=0.0001) from 0.844 to 0.884 after using CAD. The ROC analysis was also applied for observer performances on nodules in different sizes and visibilities. The average AUZs are improved from 0.798 to 0.835 (P=0.0003) for 5-10mm nodules, 0.853 to 0.907 (P=0.001) for 10-15mm nodules, 0.864 to 0.897 (P=0.051) for 15-20 mm nodules and 0.859 to 0.896 (P=0.0342) for 20-30mm nodules, respectively. For different visibilities, the average AUZs are improved from 0.886 to 0.915 (P=0.0337), 0.803 to 0.840 (P=0.063), 0.830 to 0.893 (P=0.0001), and 0.813 to 0.847 (P=0.152), for nodules clearly visible, hidden by ribs, partially overlap with ribs, and overlap with other structures, respectively. These results showed that observer performance could be greatly improved when the CAD system is employed as a second reader, especially for small nodules and nodules occluded by ribs.

A receiver operated curve-based evaluation of change in sensitivity and specificity of cotinine urinalysis for detecting active tobacco use.

PubMed

Balhara, Yatan Pal Singh; Jain, Raka

2013-01-01

Tobacco use has been associated with various carcinomas including lung, esophagus, larynx, mouth, throat, kidney, bladder, pancreas, stomach, and cervix. Biomarkers such as concentration of cotinine in the blood, urine, or saliva have been used as objective measures to distinguish nonusers and users of tobacco products. A change in the cut-off value of urinary cotinine to detect active tobacco use is associated with a change in sensitivity and sensitivity of detection. The current study aimed at assessing the impact of using different cut-off thresholds of urinary cotinine on sensitivity and specificity of detection of smoking and smokeless tobacco product use among psychiatric patients. All the male subjects attending the psychiatry out-patient department of the tertiary care multispecialty teaching hospital constituted the sample frame for the current study in a cross-sectionally. Quantitative urinary cotinine assay was done by using ELISA kits of Calbiotech. Inc., USA. We used the receiver operating characteristic (ROC) curve to assess the sensitivity and specificity of various cut-off values of urinary cotinine to identify active smokers and users of smokeless tobacco products. ROC analysis of urinary cotinine levels in detection of self-reported smoking provided the area under curve (AUC) of 0.434. Similarly, the ROC analysis of urinary cotinine levels in detection of self-reported smoking revealed AUC of 0.44. The highest sensitivity and specificity of 100% for smoking were detected at the urinary cut-off value greater than or equal to 2.47 ng/ml. The choice of cut-off value of urinary cotinine used to distinguish nonusers form active users of tobacco products impacts the sensitivity as well as specificity of detection.

Spinal Cord Injury Pain Instrument and painDETECT questionnaire: Convergent construct validity in individuals with Spinal Cord Injury.

PubMed

Franz, S; Schuld, C; Wilder-Smith, E P; Heutehaus, L; Lang, S; Gantz, S; Schuh-Hofer, S; Treede, R-D; Bryce, T N; Wang, H; Weidner, N

2017-11-01

Neuropathic pain (NeuP) is a frequent sequel of spinal cord injury (SCI). The SCI Pain Instrument (SCIPI) was developed as a SCI-specific NeuP screening tool. A preliminary validation reported encouraging results requiring further evaluation in terms of psychometric properties. The painDETECT questionnaire (PDQ), a commonly applied NeuP assessment tool, was primarily validated in German, but not specifically developed for SCI and not yet validated according to current diagnostic guidelines. We aimed to provide convergent construct validity and to identify the optimal item combination for the SCIPI. The PDQ was re-evaluated according to current guidelines with respect to SCI-related NeuP. Prospective monocentric study. Subjects received a neurological examination according to the International Standards for Neurological Classification of SCI. After linguistic validation of the SCIPI, the IASP-grading system served as reference to diagnose NeuP, accompanied by the PDQ after its re-evaluation as binary classifier. Statistics were evaluated through ROC-analysis, with the area under the ROC curve (AUROC) as optimality criterion. The SCIPI was refined by systematic item permutation. Eighty-eight individuals were assessed with the German SCIPI. Of 127 possible combinations, a 4-item-SCIPI (cut-off-score = 1.5/sensitivity = 0.864/specificity = 0.839) was identified as most reasonable. The SCIPI showed a strong correlation (r sp  = 0.76) with PDQ. ROC-analysis of SCIPI/PDQ (AUROC = 0.877) revealed comparable results to SCIPI/IASP (AUROC = 0.916). ROC-analysis of PDQ/IASP delivered a score threshold of 10.5 (sensitivity = 0.727/specificity = 0.903). The SCIPI is a valid easy-to-apply NeuP screening tool in SCI. The PDQ is recommended as complementary NeuP assessment tool in SCI, e.g. to monitor pain severity and/or its time-dependent course. In SCI-related pain, both SCIPI and PainDETECT show strong convergent construct validity versus the current IASP-grading system. SCIPI is now optimized from a 7-item to an easy-to-apply 4-item screening tool in German and English. We provided evidence that the scope for PainDETECT can be expanded to individuals with SCI. © 2017 European Pain Federation - EFIC®.

Variable ranking based on the estimated degree of separation for two distributions of data by the length of the receiver operating characteristic curve.

PubMed

Maswadeh, Waleed M; Snyder, A Peter

2015-05-30

Variable responses are fundamental for all experiments, and they can consist of information-rich, redundant, and low signal intensities. A dataset can consist of a collection of variable responses over multiple classes or groups. Usually some of the variables are removed in a dataset that contain very little information. Sometimes all the variables are used in the data analysis phase. It is common practice to discriminate between two distributions of data; however, there is no formal algorithm to arrive at a degree of separation (DS) between two distributions of data. The DS is defined herein as the average of the sum of the areas from the probability density functions (PDFs) of A and B that contain a≥percentage of A and/or B. Thus, DS90 is the average of the sum of the PDF areas of A and B that contain ≥90% of A and/or B. To arrive at a DS value, two synthesized PDFs or very large experimental datasets are required. Experimentally it is common practice to generate relatively small datasets. Therefore, the challenge was to find a statistical parameter that can be used on small datasets to estimate and highly correlate with the DS90 parameter. Established statistical methods include the overlap area of the two data distribution profiles, Welch's t-test, Kolmogorov-Smirnov (K-S) test, Mann-Whitney-Wilcoxon test, and the area under the receiver operating characteristics (ROC) curve (AUC). The area between the ROC curve and diagonal (ACD) and the length of the ROC curve (LROC) are introduced. The established, ACD, and LROC methods were correlated to the DS90 when applied on many pairs of synthesized PDFs. The LROC method provided the best linear correlation with, and estimation of, the DS90. The estimated DS90 from the LROC (DS90-LROC) is applied to a database, as an example, of three Italian wines consisting of thirteen variable responses for variable ranking consideration. An important highlight of the DS90-LROC method is utilizing the LROC curve methodology to test all variables one-at-a-time with all pairs of classes in a dataset. Copyright © 2015 Elsevier B.V. All rights reserved.

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

Wet Deposition Flux of Reactive Organic Carbon

NASA Astrophysics Data System (ADS)

Safieddine, S.; Heald, C. L.

2016-12-01

Reactive organic carbon (ROC) is the sum of non-methane volatile organic compounds (NMVOCs) and primary and secondary organic aerosols (OA). ROC plays a key role in driving the chemistry of the atmosphere, affecting the hydroxyl radical concentrations, methane lifetime, ozone formation, heterogeneous chemical reactions, and cloud formation, thereby impacting human health and climate. Uncertainties on the lifecycle of ROC in the atmosphere remain large. In part this can be attributed to the large uncertainties associated with the wet deposition fluxes. Little is known about the global magnitude of wet deposition as a sink of both gas and particle phase organic carbon, making this an important area for research and sensitivity testing in order to better understand the global ROC budget. In this study, we simulate the wet deposition fluxes of the reactive organic carbon of the troposphere using a global chemistry transport model, GEOS-Chem. We start by showing the current modeled global distribution of ROC wet deposition fluxes and investigate the sensitivity of these fluxes to variability in Henry's law solubility constants and spatial resolution. The average carbon oxidation state (OSc) is a useful metric that depicts the degree of oxidation of atmospheric reactive carbon. Here, we present for the first time the simulated gas and particle phase OSc of the global troposphere. We compare the OSc in the wet deposited reactive carbon flux and the dry deposited reactive carbon flux to the OSc of atmospheric ROC to gain insight into the degree of oxidation in deposited material and, more generally, the aging of organic material in the troposphere.

Predicting serious bacterial infection in young children with fever without apparent source.

PubMed

Bleeker, S E; Moons, K G; Derksen-Lubsen, G; Grobbee, D E; Moll, H A

2001-11-01

The aim of this study was to design a clinical rule to predict the presence of a serious bacterial infection in children with fever without apparent source. Information was collected from the records of children aged 1-36 mo who attended the paediatric emergency department because of fever without source (temperature > or = 38 degrees C and no apparent source found after evaluation by a general practitioner or history by a paediatrician). Serious bacterial infection included bacterial meningitis, sepsis, bacteraemia, pneumonia, urinary tract infection, bacterial gastroenteritis, osteomyelitis and ethmoiditis. Using multivariate logistic regression and the area under the receiver operating characteristic curve (ROC area), the diagnostic value of predictors for serious bacterial infection was judged, resulting in a risk stratification. Twenty-five percent of the 231 patients enrolled in the study (mean age 1.1 y) had a serious bacterial infection. Independent predictors from history and examination included duration of fever, poor micturition, vomiting, age, temperature < 36.7 degrees C or > or = 40 degrees C at examination, chest-wall retractions and poor peripheral circulation (ROC area: 0.75). Independent predictors from laboratory tests were white blood cell count, serum C-reactive protein and the presence of >70 white blood cells in urinalysis (ROC area: 0.83). The risk stratification for serious bacterial infection ranged from 6% to 92%. The probability of a serious bacterial infection in the individual patient with fever without source can be estimated more precisely by using a limited number of symptoms, signs and laboratory tests.

CNV-ROC: A cost effective, computer-aided analytical performance evaluator of chromosomal microarrays.

PubMed

Goodman, Corey W; Major, Heather J; Walls, William D; Sheffield, Val C; Casavant, Thomas L; Darbro, Benjamin W

2015-04-01

Chromosomal microarrays (CMAs) are routinely used in both research and clinical laboratories; yet, little attention has been given to the estimation of genome-wide true and false negatives during the assessment of these assays and how such information could be used to calibrate various algorithmic metrics to improve performance. Low-throughput, locus-specific methods such as fluorescence in situ hybridization (FISH), quantitative PCR (qPCR), or multiplex ligation-dependent probe amplification (MLPA) preclude rigorous calibration of various metrics used by copy number variant (CNV) detection algorithms. To aid this task, we have established a comparative methodology, CNV-ROC, which is capable of performing a high throughput, low cost, analysis of CMAs that takes into consideration genome-wide true and false negatives. CNV-ROC uses a higher resolution microarray to confirm calls from a lower resolution microarray and provides for a true measure of genome-wide performance metrics at the resolution offered by microarray testing. CNV-ROC also provides for a very precise comparison of CNV calls between two microarray platforms without the need to establish an arbitrary degree of overlap. Comparison of CNVs across microarrays is done on a per-probe basis and receiver operator characteristic (ROC) analysis is used to calibrate algorithmic metrics, such as log2 ratio threshold, to enhance CNV calling performance. CNV-ROC addresses a critical and consistently overlooked aspect of analytical assessments of genome-wide techniques like CMAs which is the measurement and use of genome-wide true and false negative data for the calculation of performance metrics and comparison of CNV profiles between different microarray experiments. Copyright © 2015 Elsevier Inc. All rights reserved.

Modeling of recovery profiles in mentally disabled and intact patients after sevoflurane anesthesia; a pharmacodynamic analysis.

PubMed

Shin, Teo Jeon; Noh, Gyu-Jeong; Koo, Yong-Seo; Han, Dong Woo

2014-11-01

Mentally disabled patients show different recovery profiles compared to normal patients after general anesthesia. However, the relationship of dose-recovery profiles of mentally disabled patients has never been compared to that of normal patients. Twenty patients (10 mentally disabled patients and 10 mentally intact patients) scheduled to dental surgery under general anesthesia was recruited. Sevoflurane was administered to maintain anesthesia during dental treatment. At the end of the surgery, sevoflurane was discontinued. End-tidal sevoflurane and recovery of consciousness (ROC) were recorded after sevoflurane discontinuation. The pharmacodynamic relation between the probability of ROC and end-tidal sevoflurane concentration was analyzed using NONMEM software (version VII). End-tidal sevoflurane concentration associated with 50% probability of ROC (C₅₀) and γ value were lower in the mentally disabled patients (C₅₀=0.37 vol %, γ=16.5 in mentally intact patients, C₅₀=0.19 vol %, γ=4.58 in mentally disabled patients). Mentality was a significant covariate of C₅₀ for ROC and γ value to pharmacodynamic model. A sigmoid Emanx model explains the pharmacodynamic relationship between end-tidal sevoflurane concentration and ROC. Mentally disabled patients may recover slower from anesthesia at lower sevoflurane concentration at ROC an compared to normal patients.

Modeling of Recovery Profiles in Mentally Disabled and Intact Patients after Sevoflurane Anesthesia; A Pharmacodynamic Analysis

PubMed Central

Shin, Teo Jeon; Noh, Gyu-Jeong; Koo, Yong-Seo

2014-01-01

Purpose Mentally disabled patients show different recovery profiles compared to normal patients after general anesthesia. However, the relationship of dose-recovery profiles of mentally disabled patients has never been compared to that of normal patients. Materials and Methods Twenty patients (10 mentally disabled patients and 10 mentally intact patients) scheduled to dental surgery under general anesthesia was recruited. Sevoflurane was administered to maintain anesthesia during dental treatment. At the end of the surgery, sevoflurane was discontinued. End-tidal sevoflurane and recovery of consciousness (ROC) were recorded after sevoflurane discontinuation. The pharmacodynamic relation between the probability of ROC and end-tidal sevoflurane concentration was analyzed using NONMEM software (version VII). Results End-tidal sevoflurane concentration associated with 50% probability of ROC (C50) and γ value were lower in the mentally disabled patients (C50=0.37 vol %, γ=16.5 in mentally intact patients, C50=0.19 vol %, γ=4.58 in mentally disabled patients). Mentality was a significant covariate of C50 for ROC and γ value to pharmacodynamic model. Conclusion A sigmoid Emanx model explains the pharmacodynamic relationship between end-tidal sevoflurane concentration and ROC. Mentally disabled patients may recover slower from anesthesia at lower sevoflurane concentration at ROC an compared to normal patients. PMID:25323901

A Model to Predict Duration of Ventilation and 30-Day Mortality in Patients with Traumatic Injuries

DTIC Science & Technology

2014-12-02

count and serum albumin had significantly high centralities and were identified the “hubs” in the network. The area under the ROC curve ( AUC -ROC) of...Pre-flight systolic blood pressure was 121 [109-143] mmHg, pulse 100 [84-116] bpm, and base deficit 0 [-2-2]. The median number of blood products...OR 9.2 [1.88-166.11]), and whole blood (OR 3.18 [1.38-7.04]) were associated with death. The combination of variables produced an AUC of 0.84 with

[The role of preoperative (18)F-FDG PET-CT in lymphatic metastasis diagnosis of cutaneous malignant melanoma on extremities and trunk].

PubMed

Zhang, X X; Fang, Y; Xu, L B; Xu, S F; Zhao, Z G; Sun, C; Ma, P Q; Liu, T; Yu, S J; Zhang, W J

2018-05-23

Objective: To evaluate the clinical value of preoperative (18)F-Fludeoxyglucose ((18)F-FDG PET-CT) in lymphatic metastasis diagnosis of cutaneous melanoma on extremities and trunk. Methods: 112 patients with cutaneous melanoma pathologically of extremities and trunk from January 2006 to December 2016, who received (18)F-FDG PET-CT examination preoperatively, were retrospectively reviewed. The correlations between the maximal diameters of lymph nodes, the maximal standard uptake value (SUV) and the diagnostic impression grades of PET-CT examination, and the final pathological diagnosis were analyzed. The correlations between Breslow thickness of primary lesions and the diagnostic impression of PET-CT examination were also analyzed. All the above were analyzed with Receiver Operating Characteristic (ROC) curve to get the cut-off value. Based on the final results of pathological diagnosis of lymph nodes as the golden standard, the statistically significant indicators of ROC curve analysis were used to evaluate the diagnostic effect, as well as to calculate the sensitivity, specificity and accuracy. With gender, age, maximal diameter of lymph nodes, maximal SUV, diagnosis impressions, and Breslow thickness as the independent variables and pathological diagnosis results of lymph nodes as the dependent variable, two-class stepwise Logistic regression analysis was used to determine the independence of diagnostic indicators. ROC curve analysis and log rank test were used to analyze the relationship between Breslow thickness and patient survival. Results: To evaluate melanoma patients' lymph node status, the results of ROC curve analysis showed that the area under the curve of lymph node maximal diameter, maximal SUV, diagnosis impression of PET-CT examinations were 0.789, 0.786 and 0.816, respectively (all P <0.05). The cut-off values were 0.85 cm, 1.45 and 2.5, respectively. The sensitivity of the cut-off values to determine the status of lymph nodes in melanoma patients were 71.4%, 64.9% and 72.1% respectively, and the specificities were 85.2%, 88.7% and 87.0% respectively. Multivariate Logistic regression analysis showed that PET-CT diagnosis impressions had independent diagnostic significance for the lymph node status of melanoma patients ( OR =11.296, 95% CI : 2.550~50.033). The area under the curve of Breslow thickness evaluating PET-CT diagnostic impression is 0.664 ( P =0.042) and the cut-off value was 4.25 mm. The survival rate of the patients with Breslow thickness ≥ 4.25 mm was lower than that in the group <4.25 mm ( P =0.006). Conclusions: (18)F-FDG PET-CT can help to evaluate metastases and make treatment decisions for cutaneous melanoma of extremities and trunk, especially for patients whose primary lesion's Breslow thickness has reached more than 4.25 mm. For the patients whose maximal SUV of regional lymph node is higher than 1.45 and short diameter of the largest lymph node is larger than 0.85cm, the possibility of metastases should be considered.

Gender and urban-rural difference in anthropometric indices predicting dyslipidemia in Chinese primary school children: a cross-sectional study.

PubMed

Zheng, Wei; Zhao, Ai; Xue, Yong; Zheng, Yingdong; Chen, Yun; Mu, Zhishen; Wang, Peiyu; Zhang, Yumei

2016-04-30

Childhood dyslipidemia is a critical factor of lifelong health. Therefore, screening and controlling dyslipidemia from childhood is a practical healthy strategy. However, few studies have examined the performance of anthropometric predictors of dyslipidemia in Chinese children, let alone the potential gender and urban-rural disparity. Thus, we evaluated anthropometric indices predicting dyslipidemia by genders and living areas in Chinese children. Data were from a health and nutrition survey conducted in seven urban areas and two rural areas in China between 2011 and 2012. The serum lipid levels of the participants were compared between genders and living areas. The body mass index z-score (BMI z-score), waist-hip ratio (WHR), waist-height ratio (WHtR), and mid-upper arm height ratio (MaHtR) were used as predictors. The receiver operating characteristic (ROC) analysis was performed to investigate the ability of anthropometric indices predicting dyslipidemia. A total of 773 participants (average age = 9.3 ± 1.7 y) were included. The prevalence of dyslipidemia was 10.9%. Anthropometric indices were all significantly related to blood lipid profiles in boys after adjustment for age. The areas under the ROC curves (ACUs) were significantly larger than 0.5 in boys (ranged between 0.66-0.73), and were larger in rural boys (ranged between 0.68 and 0.94). MaHtR and WHR were associated with the highest specificity (93.8%) and highest sensitivity (100%), respectively. Using anthropometric indices, screening for dyslipidemia may be more appropriate in boys than in girls in China, especially in rural boys. The BMI z-score, WHR, WHtR, and MaHtR were all significantly associated with dyslipidemia in boys; using WHR and MaHtR as indicators achieved the highest sensitivity and specificity, respectively.

Development and Validation of a Short Version of the Anterior Cruciate Ligament Return to Sport After Injury (ACL-RSI) Scale

PubMed Central

Webster, Kate E.; Feller, Julian A.

2018-01-01

Background: The Anterior Cruciate Ligament Return to Sport After Injury (ACL-RSI) scale was developed to measure an athlete’s psychological readiness to return to sport after anterior cruciate ligament (ACL) injury and reconstruction surgery. The scale is being used with increasing frequency in both research and clinical settings. Purpose: To generate and validate a short version of the ACL-RSI scale. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: The ACL-RSI scale was administered to 535 patients who had undergone ACL reconstruction surgery. Reliability (Cronbach alpha) was determined and factor analysis of the full scale was undertaken along with a process of item selection and elimination. A second group of 250 patients participated in a predictive validation analysis. This group completed the ACL-RSI scale at 6 months and reported return-to-sport outcomes 12 months following ACL reconstruction surgery. The predictive validity of both scales (full and short versions) was assessed by use of receiver operating characteristic (ROC) curve statistics. Results: The scale was found to have high internal consistency (Cronbach alpha, 0.96), which suggested that item redundancy was present. After an item selection process, the scale was reduced to a 6-item format. Cronbach alpha for the short version was 0.92, and factor analysis confirmed the presence of 1 factor accounting for 71% of the total variance. Scores for the short version were significantly different between patients who had and those who had not returned to sport. Six-month ACL-RSI scores for both the full and short versions had fair to good predictive ability for 12-month return-to-sport outcomes (full version: area under ROC curve, 0.77 [95% CI, 0.7-0.8]; short version: area under ROC curve, 0.75 [95% CI, 0.7-0.8]). Conclusion: A 6-item short version of the ACL-RSI scale was developed from a large cohort of patients undergoing ACL reconstruction. The short version appears to be as robust as the full version for discriminating between and predicting return-to-sport outcomes. The short version of the ACL-RSI may be of use in busy clinical settings to help identify athletes who may find return to sport challenging. PMID:29662909

Correlation between laboratory coagulation testing and thromboelastometry is modified during management of trauma patients.

PubMed

David, Jean-Stéphane; Durand, Maeva; Levrat, Albrice; Lefevre, Mathilde; Rugeri, Lucia; Geay-Baillat, Marie-Odile; Inaba, Kenji; Bouzat, Pierre

2016-08-01

Thromboelastometry (ROTEM, Pentapharm GmbH, Munich, Germany) is increasingly being used to make a diagnosis of coagulopathy and to guide hemostatic therapy (HT). Although ROTEM parameters and standard laboratory test (SLT) correlated well before administration of HT, it is not known if this correlation persists after hemostatic resuscitation. A retrospective analysis of prospectively collected data from a trauma registry (2011-2014) was performed. All patients having a ROTEM analysis were included. ROTEM parameters (clotting time and clot amplitude at 5 minutes) were determined after activation with tissue factor (EXTEM) or platelet inhibition with cytochalasin D (FIBTEM). Spearman rank correlation coefficient was calculated for the correlation between SLT and thromboelastometry parameters, and thresholds were determined with receiver operating characteristic (ROC) curve analysis for the diagnosis of an international normalized ratio (INR) greater than 1.5, fibrinogen 1.5 g/L or less, and platelet count of less than 100.10/L. Of the 358 patients included, 533 thromboelastometry results were obtained (335 at admission, 198 during care). Correlation between INR and EXTEM-clotting time was good at admission (r = 0.617) in the whole cohort but decreased in the subgroup of patients having an Injury Severity Score of less than 25 (r = 0.399) or a base excess of less than 6 mmol/L (r = 0.489). During care, correlation was impaired after the administration of fibrinogen concentrates in the whole cohort (r = 0.430), as well as in the subgroup of patients having an Injury Severity Score greater than 24 (r = 0.465). As well, for the diagnosis of increased INR, sensitivity and the area under the ROC curve decreased from 75% and 0.894 (no treatment) to 20% and 0.653 (fibrinogen concentrate). Areas under the ROC curve for the prediction of a fibrinogen or platelet decrease were not significantly altered regardless of the treatment group. A decrease in the correlation between SLTs and ROTEM parameters was observed at admission or during care, which could be in relation with injury severity, base deficit, or the administration of blood products, particularly fibrinogen concentrate. Further work will be necessary to better understand which tool is the most suitable for guiding HT. Therapeutic study, level IV; diagnostic study, level IV.

Validity of the posttraumatic stress disorders (PTSD) checklist in pregnant women.

PubMed

Gelaye, Bizu; Zheng, Yinnan; Medina-Mora, Maria Elena; Rondon, Marta B; Sánchez, Sixto E; Williams, Michelle A

2017-05-12

The PTSD Checklist-civilian (PCL-C) is one of the most commonly used self-report measures of PTSD symptoms, however, little is known about its validity when used in pregnancy. This study aims to evaluate the reliability and validity of the PCL-C as a screen for detecting PTSD symptoms among pregnant women. A total of 3372 pregnant women who attended their first prenatal care visit in Lima, Peru participated in the study. We assessed the reliability of the PCL-C items using Cronbach's alpha. Criterion validity and performance characteristics of PCL-C were assessed against an independent, blinded Clinician-Administered PTSD Scale (CAPS) interview using measures of sensitivity, specificity and receiver operating characteristics (ROC) curves. We tested construct validity using exploratory and confirmatory factor analytic approaches. The reliability of the PCL-C was excellent (Cronbach's alpha =0.90). ROC analysis showed that a cut-off score of 26 offered optimal discriminatory power, with a sensitivity of 0.86 (95% CI: 0.78-0.92) and a specificity of 0.63 (95% CI: 0.62-0.65). The area under the ROC curve was 0.75 (95% CI: 0.71-0.78). A three-factor solution was extracted using exploratory factor analysis and was further complemented with three other models using confirmatory factor analysis (CFA). In a CFA, a three-factor model based on DSM-IV symptom structure had reasonable fit statistics with comparative fit index of 0.86 and root mean square error of approximation of 0.09. The Spanish-language version of the PCL-C may be used as a screening tool for pregnant women. The PCL-C has good reliability, criterion validity and factorial validity. The optimal cut-off score obtained by maximizing the sensitivity and specificity should be considered cautiously; women who screened positive may require further investigation to confirm PTSD diagnosis.

The Weiss Functional Impairment Rating Scale-Parent Form for assessing ADHD: evaluating diagnostic accuracy and determining optimal thresholds using ROC analysis.

PubMed

Thompson, Trevor; Lloyd, Andrew; Joseph, Alain; Weiss, Margaret

2017-07-01

The Weiss Functional Impairment Rating Scale-Parent Form (WFIRS-P) is a 50-item scale that assesses functional impairment on six clinically relevant domains typically affected in attention-deficit/hyperactivity disorder (ADHD). As functional impairment is central to ADHD, the WFIRS-P offers potential as a tool for assessing functional impairment in ADHD. These analyses were designed to examine the overall performance of WFIRS-P in differentiating ADHD and non-ADHD cases using receiver operating characteristics (ROC) analysis. This is the first attempt to empirically determine the level of functional impairment that differentiates ADHD children from normal controls. This observational study comprised 5-19-year-olds with physician-diagnosed ADHD (n = 476) and non-ADHD controls (n = 202). ROC analysis evaluated the ability of WFIRS-P to discriminate between ADHD and non-ADHD, and identified a WFIRS-P cut-off score that optimises correct classification. Data were analysed for the complete sample, for males versus females and for participants in two age groups (5-12 versus 13-19 years). Area under the curve (AUC) was 0.91 (95% confidence interval 0.88-0.93) for the overall WFIRS-P score, suggesting highly accurate classification of ADHD distinct from non-ADHD. Sensitivity (0.83) and specificity (0.85) were maximal for a mean overall WFIRS-P score of 0.65, suggesting that this is an appropriate threshold for differentiation. DeLong's test found no significant differences in AUCs for males versus females or 5-12 versus 13-19 years, suggesting that WFIRS-P is an accurate classifier of ADHD across gender and age. When assessing function, WFIRS-P appears to provide a simple and effective basis for differentiating between individuals with/without ADHD in terms of functional impairment. Disease-specific applications of QOL research.

ROC curve analyses of eyewitness identification decisions: An analysis of the recent debate.

PubMed

Rotello, Caren M; Chen, Tina

2016-01-01

How should the accuracy of eyewitness identification decisions be measured, so that best practices for identification can be determined? This fundamental question is under intense debate. One side advocates for continued use of a traditional measure of identification accuracy, known as the diagnosticity ratio , whereas the other side argues that receiver operating characteristic curves (ROCs) should be used instead because diagnosticity is confounded with response bias. Diagnosticity proponents have offered several criticisms of ROCs, which we show are either false or irrelevant to the assessment of eyewitness accuracy. We also show that, like diagnosticity, Bayesian measures of identification accuracy confound response bias with witnesses' ability to discriminate guilty from innocent suspects. ROCs are an essential tool for distinguishing memory-based processes from decisional aspects of a response; simulations of different possible identification tasks and response strategies show that they offer important constraints on theory development.

ROC analysis of diagnostic performance in liver scintigraphy.

PubMed

Fritz, S L; Preston, D F; Gallagher, J H

1981-02-01

Studies on the accuracy of liver scintigraphy for the detection of metastases were assembled from 38 sources in the medical literature. An ROC curve was fitted to the observed values of sensitivity and specificity using an algorithm developed by Ogilvie and Creelman. This ROC curve fitted the data better than average sensitivity and specificity values in each of four subsets of the data. For the subset dealing with Tc-99m sulfur colloid scintigraphy, performed for detection of suspected metastases and containing data on 2800 scans from 17 independent series, it was not possible to reject the hypothesis that interobserver variation was entirely due to the use of different decision thresholds by the reporting clinicians. Thus the ROC curve obtained is a reasonable baseline estimate of the performance potentially achievable in today's clinical setting. Comparison of new reports with these data is possible, but is limited by the small sample sizes in most reported series.

Receiver-operating-characteristic analysis of an automated program for analyzing striatal uptake of 123I-ioflupane SPECT images: calibration using visual reads.

PubMed

Kuo, Phillip Hsin; Avery, Ryan; Krupinski, Elizabeth; Lei, Hong; Bauer, Adam; Sherman, Scott; McMillan, Natalie; Seibyl, John; Zubal, George

2013-03-01

A fully automated objective striatal analysis (OSA) program that quantitates dopamine transporter uptake in subjects with suspected Parkinson's disease was applied to images from clinical (123)I-ioflupane studies. The striatal binding ratios or alternatively the specific binding ratio (SBR) of the lowest putamen uptake was computed, and receiver-operating-characteristic (ROC) analysis was applied to 94 subjects to determine the best discriminator using this quantitative method. Ninety-four (123)I-ioflupane SPECT scans were analyzed from patients referred to our clinical imaging department and were reconstructed using the manufacturer-supplied reconstruction and filtering parameters for the radiotracer. Three trained readers conducted independent visual interpretations and reported each case as either normal or showing dopaminergic deficit (abnormal). The same images were analyzed using the OSA software, which locates the striatal and occipital structures and places regions of interest on the caudate and putamen. Additionally, the OSA places a region of interest on the occipital region that is used to calculate the background-subtracted SBR. The lower SBR of the 2 putamen regions was taken as the quantitative report. The 33 normal (bilateral comma-shaped striata) and 61 abnormal (unilateral or bilateral dopaminergic deficit) studies were analyzed to generate ROC curves. Twenty-nine of the scans were interpreted as normal and 59 as abnormal by all 3 readers. For 12 scans, the 3 readers did not unanimously agree in their interpretations (discordant). The ROC analysis, which used the visual-majority-consensus interpretation from the readers as the gold standard, yielded an area under the curve of 0.958 when using 1.08 as the threshold SBR for the lowest putamen. The sensitivity and specificity of the automated quantitative analysis were 95% and 89%, respectively. The OSA program delivers SBR quantitative values that have a high sensitivity and specificity, compared with visual interpretations by trained nuclear medicine readers. Such a program could be a helpful aid for readers not yet experienced with (123)I-ioflupane SPECT images and if further adapted and validated may be useful to assess disease progression during pharmaceutical testing of therapies.

Detection of Human Monkeypox in the Republic of the Congo Following Intensive Community Education

PubMed Central

Reynolds, Mary G.; Emerson, Ginny L.; Pukuta, Elisabeth; Karhemere, Stomy; Muyembe, Jean J.; Bikindou, Alain; McCollum, Andrea M.; Moses, Cynthia; Wilkins, Kimberly; Zhao, Hui; Damon, Inger K.; Karem, Kevin L.; Li, Yu; Carroll, Darin S.; Mombouli, Jean V.

2013-01-01

Monkeypox is an acute viral infection with a clinical course resembling smallpox. It is endemic in northern and central Democratic Republic of the Congo (DRC), but it is reported only sporadically in neighboring Republic of the Congo (ROC). In October 2009, interethnic violence in northwestern DRC precipitated the movement of refugees across the Ubangi River into ROC. The influx of refugees into ROC heightened concerns about monkeypox in the area, because of the possibility that the virus could be imported, or that incidence could increase caused by food insecurity and over reliance on bush meat. As part of a broad-based campaign to improve health standards in refugee settlement areas, the United Nations International Children's Emergency Fund (UNICEF) sponsored a program of intensive community education that included modules on monkeypox recognition and prevention. In the 6 months immediately following the outreach, 10 suspected cases of monkeypox were reported to health authorities. Laboratory testing confirmed monkeypox virus infection in two individuals, one of whom was part of a cluster of four suspected cases identified retrospectively. Anecdotes collected at the time of case reporting suggest that the outreach campaign contributed to detection of suspected cases of monkeypox. PMID:23400570

Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework.

PubMed

Rahmati, Omid; Tahmasebipour, Naser; Haghizadeh, Ali; Pourghasemi, Hamid Reza; Feizizadeh, Bakhtiar

2017-02-01

Despite the importance of soil erosion in sustainable development goals in arid and semi-arid areas, the study of the geo-environmental conditions and factors influencing gully erosion occurrence is rarely undertaken. As effort to this challenge, the main objective of this study is to apply an integrated approach of Geographic Object-Based Image Analysis (GEOBIA) together with high-spatial resolution imagery (SPOT-5) for detecting gully erosion features at the Kashkan-Poldokhtar watershed, Iran. We also aimed to apply a Conditional Probability (CP) model for establishing the spatial relationship between gullies and the Geo-Environmental Factors (GEFs). The gully erosion inventory map prepared using GEOBIA and field surveying was randomly partitioned into two subsets: (1) part 1 that contains 70% was used in the training phase of the CP model; (2) part 2 is a validation dataset (30%) for validation of the model and to confirm its accuracy. Prediction performances of the GEOBIA and CP model were checked by overall accuracy and Receiver Operating Characteristics (ROC) curve methods, respectively. In addition, the influence of all GEFs on gully erosion was evaluated by performing a sensitivity analysis model. The validation findings illustrated that overall accuracy for GEOBIA approach and the area under the ROC curve for the CP model were 92.4% and 89.9%, respectively. Also, based on sensitivity analysis, soil texture, drainage density, and lithology represent significantly effects on the gully erosion occurrence. This study has shown that the integrated framework can be successfully used for modeling gully erosion occurrence in a data-poor environment. Copyright © 2016 Elsevier B.V. All rights reserved.

Relative to open surgery, minimally-invasive renal and ureteral pediatric surgery offers no improvement in 30-day complications, yet requires longer operative time: Data from the National Surgical Quality Improvement Program Pediatrics.

PubMed

Colaco, Marc; Hester, Austin; Visser, William; Rasper, Alison; Terlecki, Ryan

2018-05-01

Performance of minimally-invasive surgery (MIS) is increasing relative to open surgery. We sought to compare the contemporary rates of short-term complications of open versus laparoscopic renal and ureteral surgery in pediatric patients. A retrospective cross-sectional analysis of the National Surgical Quality Improvement Program Pediatrics database was performed of all cases in 2014 identified using CPT procedure codes for nephrectomy, partial nephrectomy (PN), ureteroneocystostomy (UNC), and pyeloplasty, and reviewed for postoperative complications. Univariate analysis was performed to determine 30-day complications, with comparison between open and MIS approaches. Receiver operator curve (ROC) analysis was performed to determine differences in body surface area (BSA) and age for open versus MIS. Review identified 207 nephrectomies, 72 PN, 920 UNC, and 625 pyeloplasties. MIS was associated with older age and larger BSA except for cases of UNC. Apart from PN, operative durations were longer with MIS. However, only PN was associated with significantly longer length of hospital stay (LOS). There was no difference in incidence of all other 30-day complications. When evaluating BSA via ROC, the area under the curve (AUC) was found to be 0.730 and was significant. Children with a BSA greater than 0.408 m 2 were more likely to have MIS (sensitivity, 66.9%; specificity, 69.3%). Regarding age, the AUC was 0.732. Children older than 637.5 days were more likely to have MIS (sensitivity, 72.8%; specificity, 63.3%). Pediatric MIS is associated with longer operative time for nephrectomy, but shorter LOS following PN. Surgical approach was not associated with difference in short-term complications.

A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors

NASA Astrophysics Data System (ADS)

Wu, Binlin; Mukherjee, Sushmita; Jain, Manu

2016-03-01

Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma.Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability.

Clinical prognostic rules for severe acute respiratory syndrome in low- and high-resource settings.

PubMed

Cowling, Benjamin J; Muller, Matthew P; Wong, Irene O L; Ho, Lai-Ming; Lo, Su-Vui; Tsang, Thomas; Lam, Tai Hing; Louie, Marie; Leung, Gabriel M

2006-07-24

An accurate prognostic model for patients with severe acute respiratory syndrome (SARS) could provide a practical clinical decision aid. We developed and validated prognostic rules for both high- and low-resource settings based on data available at the time of admission. We analyzed data on all 1755 and 291 patients with SARS in Hong Kong (derivation cohort) and Toronto (validation cohort), respectively, using a multivariable logistic scoring method with internal and external validation. Scores were assigned on the basis of patient history in a basic model, and a full model additionally incorporated radiological and laboratory results. The main outcome measure was death. Predictors for mortality in the basic model included older age, male sex, and the presence of comorbid conditions. Additional predictors in the full model included haziness or infiltrates on chest radiography, less than 95% oxygen saturation on room air, high lactate dehydrogenase level, and high neutrophil and low platelet counts. The basic model had an area under the receiver operating characteristic (ROC) curve of 0.860 in the derivation cohort, which was maintained on external validation with an area under the ROC curve of 0.882. The full model improved discrimination with areas under the ROC curve of 0.877 and 0.892 in the derivation and validation cohorts, respectively. The model performs well and could be useful in assessing prognosis for patients who are infected with re-emergent SARS.

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

PubMed

Hicks, Lucy C; Huang, Juzheng; Kumar, Sacheen; Powles, Sam T; Orchard, Timothy R; Hanna, George B; Williams, Horace R T

2015-09-01

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Is ozonation environmentally benign for reverse osmosis concentrate treatment? Four-level analysis on toxicity reduction based on organic matter fractionation.

PubMed

Weng, Jingxia; Jia, Huichao; Wu, Bing; Pan, Bingcai

2018-01-01

Ozonation is a promising option to treat reverse osmosis concentrate (ROC). However, a systematic understanding and assessment of ozonation on toxicity reduction is insufficient. In this study, ROC sampled from a typical industrial park wastewater treatment plant of China was fractionated into hydrophobic acid (HOA), hydrophobic base (HOB), hydrophobic neutral (HON), and hydrophilic fraction (HI). Systematic bioassays covering bacteria, algae, fish, and human cell lines were conducted to reveal the role of ozonation in toxicity variation of the four ROC fractions. HOA in the raw ROC exhibited the highest toxicity, followed by HON and HI. Ozonation significantly reduced total organic carbon (TOC) and UV 254 values in HOA, HON, and HI and their toxicity except in HOB. Correlation analysis indicated that chemical data (TOC and UV 254 ) of HOA and HON correlated well with their toxicities; however, poor correlations were observed for HOB and HI, suggesting that a battery of toxicity assays is necessary. This study indicates that TOC reduction during ozonation could not fully reflect the toxicity issue, and toxicity assessment is required in conjunction with the chemical data to evaluate the effectiveness of ozonation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time-varying surface electromyography topography as a prognostic tool for chronic low back pain rehabilitation.

PubMed

Hu, Yong; Kwok, Jerry Weilun; Tse, Jessica Yuk-Hang; Luk, Keith Dip-Kei

2014-06-01

Nonsurgical rehabilitation therapy is a commonly used strategy to treat chronic low back pain (LBP). The selection of the most appropriate therapeutic options is still a big challenge in clinical practices. Surface electromyography (sEMG) topography has been proposed to be an objective assessment of LBP rehabilitation. The quantitative analysis of dynamic sEMG would provide an objective tool of prognosis for LBP rehabilitation. To evaluate the prognostic value of quantitative sEMG topographic analysis and to verify the accuracy of the performance of proposed time-varying topographic parameters for identifying the patients who have better response toward the rehabilitation program. A retrospective study of consecutive patients. Thirty-eight patients with chronic nonspecific LBP and 43 healthy subjects. The accuracy of the time-varying quantitative sEMG topographic analysis for monitoring LBP rehabilitation progress was determined by calculating the corresponding receiver-operating characteristic (ROC) curves. Physiologic measure was the sEMG during lumbar flexion and extension. Patients who suffered from chronic nonspecific LBP without the history of back surgery and any medical conditions causing acute exacerbation of LBP during the clinical test were enlisted to perform the clinical test during the 12-week physiotherapy (PT) treatment. Low back pain patients were classified into two groups: "responding" and "nonresponding" based on the clinical assessment. The responding group referred to the LBP patients who began to recover after the PT treatment, whereas the nonresponding group referred to some LBP patients who did not recover or got worse after the treatment. The results of the time-varying analysis in the responding group were compared with those in the nonresponding group. In addition, the accuracy of the analysis was analyzed through ROC curves. The time-varying analysis showed discrepancies in the root-mean-square difference (RMSD) parameters between the responding and nonresponding groups. The relative area (RA) and relative width (RW) of RMSD at flexion and extension in the responding group were significantly lower than those in the nonresponding group (p<.05). The areas under the ROC curve of RA and RW of RMSD at flexion and extension were greater than 0.7 and were statistically significant. The quantitative time-varying analysis of sEMG topography showed significant difference between the healthy and LBP groups. The discrepancies in quantitative dynamic sEMG topography of LBP group from normal group, in terms of RA and RW of RMSD at flexion and extension, were able to identify those LBP subjects who would respond to a conservative rehabilitation program focused on functional restoration of lumbar muscle. Copyright © 2014 Elsevier Inc. All rights reserved.

Predictors of patients who will develop prolonged occult hypoperfusion following blunt trauma.

PubMed

Schulman, Andrew M; Claridge, Jeffrey A; Carr, Gordon; Diesen, Diana L; Young, Jeffrey S

2004-10-01

Prolonged occult hypoperfusion or POH (serum lactate >2.4 mmol/L persisting >12 hours from admission) represents a reversible risk factor for adverse outcomes following traumatic injury. We hypothesized that patients at increased risk for POH could be identified at the time of admission. Prospective data from adult trauma admissions between January 1, 1998 and December 31, 2000 were analyzed. Potential risk factors for POH were determined by univariate analysis (p < or =0.10= significant). Significant factors were tested in a logistic regression model (LR) (p < or =0.05= significant). The predictive ability of the LR was tested by receiver operating curve (ROC) analysis (p < or =0.05= significant). Three hundred seventy-eight patients were analyzed, 129 with POH. Injury Severity Score (ISS), emergency department Glasgow Coma Scale score, hypotension, and the individual Abbreviated Injury Scale score (AIS) for Head (H), Abdominal/Pelvic Viscera (A) and Pelvis/Bony Extremity (P) were significantly associated with POH. LR demonstrated that ISS, A-AIS > or =3 and P-AIS > or =3 were independent predictors of POH (p <0.05). ROC analysis of the LR equation was statistically significant (Area=0.69, p <0.001). We identified factors at admission that placed patients at higher risk for developing POH. Select patients may benefit from rapid, aggressive monitoring and resuscitation, possibly preventing POH and its associated morbidity and mortality.

Differentiating invasive and pre-invasive lung cancer by quantitative analysis of histopathologic images

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Sun, Hongliu; Chan, Heang-Ping; Chughtai, Aamer; Wei, Jun; Hadjiiski, Lubomir; Kazerooni, Ella

2018-02-01

We are developing automated radiopathomics method for diagnosis of lung nodule subtypes. In this study, we investigated the feasibility of using quantitative methods to analyze the tumor nuclei and cytoplasm in pathologic wholeslide images for the classification of pathologic subtypes of invasive nodules and pre-invasive nodules. We developed a multiscale blob detection method with watershed transform (MBD-WT) to segment the tumor cells. Pathomic features were extracted to characterize the size, morphology, sharpness, and gray level variation in each segmented nucleus and the heterogeneity patterns of tumor nuclei and cytoplasm. With permission of the National Lung Screening Trial (NLST) project, a data set containing 90 digital haematoxylin and eosin (HE) whole-slide images from 48 cases was used in this study. The 48 cases contain 77 regions of invasive subtypes and 43 regions of pre-invasive subtypes outlined by a pathologist on the HE images using the pathological tumor region description provided by NLST as reference. A logistic regression model (LRM) was built using leave-one-case-out resampling and receiver operating characteristic (ROC) analysis for classification of invasive and pre-invasive subtypes. With 11 selected features, the LRM achieved a test area under the ROC curve (AUC) value of 0.91+/-0.03. The results demonstrated that the pathologic invasiveness of lung adenocarcinomas could be categorized with high accuracy using pathomics analysis.

Differentiating pheochromocytoma from lipid-poor adrenocortical adenoma by CT texture analysis: feasibility study.

PubMed

Zhang, Gu-Mu-Yang; Shi, Bing; Sun, Hao; Jin, Zheng-Yu; Xue, Hua-Dan

2017-09-01

To investigate the feasibility of using CT texture analysis (CTTA) to differentiate pheochromocytoma from lipid-poor adrenocortical adenoma (lp-ACA). Ninety-eight pheochromocytomas and 66 lp-ACAs were included in this retrospective study. CTTA was performed on unenhanced and enhanced images. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different for the objective. Diagnostic accuracies were evaluated using the cutoff values of texture parameters with the highest AUCs. Compared to lp-ACAs, pheochromocytomas had significantly higher mean gray-level intensity (Mean), entropy, and mean of positive pixels (MPP), but lower skewness and kurtosis on unenhanced images (P < 0.001). On enhanced images, these texture-quantifiers followed a similar trend where Mean, entropy, and MPP were higher, but skewness and kurtosis were lower in pheochromocytomas. Standard deviation (SD) was also significantly higher in pheochromocytomas on enhanced images. Mean and MPP quantified from no filtration on unenhanced CT images yielded the highest AUC of 0.86 ± 0.03 (95% CI 0.81-0.91) at a cutoff value of 34.0 for Mean and MPP, respectively (sensitivity = 79.6%, specificity = 83.3%, accuracy = 81.1%). It was feasible to use CTTA to differentiate pheochromocytoma from lp-ACA.

Development and prospective multicenter evaluation of the long noncoding RNA MALAT-1 as a diagnostic urinary biomarker for prostate cancer

PubMed Central

Lu, Ji; Shi, Xiaolei; Zhu, Yasheng; Zhang, Wei; Jing, Taile; Zhang, Chao; Shen, Jian; Xu, Chuanliang; Wang, Huiqing; Wang, Haifeng; Wang, Yang; Liu, Bin; Li, Yaoming; Fang, Ziyu; Guo, Fei; Qiao, Meng; Wu, Chengyao; Wei, Qiang; Xu, Danfeng; Shen, Dan; Lu, Xin; Gao, Xu; Hou, Jianguo; Sun, Yinghao

2014-01-01

The current strategy for diagnosing prostate cancer (PCa) is mainly based on the serum prostate-specific antigen (PSA) test. However, PSA has low specificity and has led to numerous unnecessary biopsies. We evaluated the effectiveness of urinary metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1), a long noncoding RNA, for predicting the risk of PCa before biopsy. The MALAT-1 score was tested in a discovery phase and a multi-center validation phase. The predictive power of the MALAT-1 score was evaluated by the area under receiver operating characteristic (ROC) curve (AUC) and by decision curve analysis. As an independent predictor of PCa, the MALAT-1 score was significantly higher in men with a positive biopsy than in those with a negative biopsy. The ROC analysis showed a higher AUC for the MALAT-1 score (0.670 and 0.742) vs. the total PSA (0.545 and 0.601) and percent free PSA (0.622 and 0.627) in patients with PSA values of 4.0-10 ng/ml. According to the decision curve analysis, using a probability threshold of 25%, the MALAT-1 model would prevent 30.2%-46.5% of unnecessary biopsies in PSA 4–10 ng/ml cohorts, without missing any high-grade cancers. Our results demonstrate that urine MALAT-1 is a promising biomarker for predicting prostate cancer risk. PMID:25526029

Responsiveness of SF-36 and Lower Extremity Functional Scale for assessing outcomes in traumatic injuries of lower extremities.

PubMed

Pan, Shin-Liang; Liang, Huey-Wen; Hou, Wen-Hsuan; Yeh, Tian-Shin

2014-11-01

To assess the responsiveness of one generic questionnaire, Medical Outcomes Study Short Form-36 (SF-36), and one region-specific outcome measure, Lower Extremity Functional Scale (LEFS), in patients with traumatic injuries of lower extremities. A prospective and observational study of patients after traumatic injuries of lower extremities. Assessments were performed at baseline and 3 months later. In-patients and out-patients in two university hospitals in Taiwan. A convenience sample of 109 subjects were evaluated and 94 (86%) were followed. Not applicable. Assessments of responsiveness with distribution-based approach (effect size, standardized response mean [SRM], minimal detectable change) and anchor-based approach (receiver's operating curve analysis, ROC analysis). LEFS and physical component score (PCS) of SF-36 were all responsive to global improvement, with fair-to-good accuracy in discriminating between participants with and without improvement. The area under curve gained by ROC analysis for LEFS and SF-36 PCS was similar (0.65 vs. 0.70, p=0.26). Our findings revealed comparable responsiveness of LEFS and PCS of SF-36 in a sample of subjects with traumatic injuries of lower limbs. Either type of functional measure would be suitable for use in clinical trials where improvement in function was an endpoint of interest. Copyright © 2014 Elsevier Ltd. All rights reserved.

Computed tomographic (CT) neuroradiological evaluation of intra-axial and extra-axial lesions: a receiver operating characteristic (ROC) study of film and 1K workstation

NASA Astrophysics Data System (ADS)

El-Saden, Suzie; Hademenos, George J.; Zhu, Wei; Sayre, James W.; Glenn, Brad; Steidler, Jim; Kode, L.; King, Brian; Quinones, Diana; Valentino, Daniel J.; Bentsen, John R.

1995-04-01

Digital display workstations are now commonly used for cross-sectional image viewing; however, few receiver operating characteristic (ROC) studies have been performed to evaluate the diagnostic efficiency of hard copy versus a workstation display for neuroradiology applications. We have performed an ROC study of film and 1K workstation based on the diagnostic performance of neuroradiology fellows to detect subtle intra- axial (high density (HD) and low density (LD)) and extra-axial (fluid, blood) lesions presented on computed tomographic (CT) images. An ROC analysis of the interpretation of approximately 200 CT images (1/2 normals and 1/2 abnormals) was performed by five experienced observers. The total number of abnormal images were equally divided among the three represented types of lesions (HD, LD, and extra-axial lesions). The images comprising the extra-axial lesion group were further subdivided into the following three distinct types: subdural hemorrhage, subarachnoid hemorrhage, and epidural hemorrhage. A fraction of the abnormal images were represented by more than one type of lesion, e.g., one abnormal image could contain both a HD and LD lesion. The digitized CT images were separated into four groups and read on the standard light box and a 1K workstation monitor equipped with simple image processing functions. Confidence ratings were scaled on a range from 0 (least confident) to 4 (most confident). Reader order sequences were randomized for each reader and for each modality. Each observer read from a total of eight different groups with a four-week intermission following the fourth group. The randomly assigned image number, lesion type and approximate location, incidental findings and comments, and confidence ratings were reported in individual worksheets for each image. ROC curves that were generated and analyzed for the various subgroups are presented in addition to the overall generalized jackknifed estimates of the grouped data. Also, 95% confidence intervals are presented for the differences in the area under the ROC curves. Although there were no statistically significant differences in the diagnostic accuracy between the original CT slice with HD and LD lesions viewed on the light box and on the 1K display workstation, the observers tended to record extra- axial lesions more frequently and with more confidence on the 1K display in comparison to the light box primarily due to the added advantage of adjusting the display window levels.

Optimal threshold estimation for binary classifiers using game theory.

PubMed

Sanchez, Ignacio Enrique

2016-01-01

Many bioinformatics algorithms can be understood as binary classifiers. They are usually compared using the area under the receiver operating characteristic ( ROC ) curve. On the other hand, choosing the best threshold for practical use is a complex task, due to uncertain and context-dependent skews in the abundance of positives in nature and in the yields/costs for correct/incorrect classification. We argue that considering a classifier as a player in a zero-sum game allows us to use the minimax principle from game theory to determine the optimal operating point. The proposed classifier threshold corresponds to the intersection between the ROC curve and the descending diagonal in ROC space and yields a minimax accuracy of 1-FPR. Our proposal can be readily implemented in practice, and reveals that the empirical condition for threshold estimation of "specificity equals sensitivity" maximizes robustness against uncertainties in the abundance of positives in nature and classification costs.

Evaluation of Screening for Retinopathy of Prematurity by ROPtool or a Lay Reader.

PubMed

Abbey, Ashkan M; Besirli, Cagri G; Musch, David C; Andrews, Chris A; Capone, Antonio; Drenser, Kimberly A; Wallace, David K; Ostmo, Susan; Chiang, Michael; Lee, Paul P; Trese, Michael T

2016-02-01

To determine if (1) tortuosity assessment by a computer program (ROPtool, developed at the University of North Carolina, Chapel Hill, and Duke University, and licensed by FocusROP) that traces retinal blood vessels and (2) assessment by a lay reader are comparable with assessment by a panel of 3 retinopathy of prematurity (ROP) experts for remote clinical grading of vascular abnormalities such as plus disease. Validity and reliability analysis of diagnostic tools. Three hundred thirty-five fundus images of prematurely born infants. Three hundred thirty-five fundus images of prematurely born infants were obtained by neonatal intensive care unit nurses. A panel of 3 ROP experts graded 84 images showing vascular dilatation, tortuosity, or both and 251 images showing no evidence of vascular abnormalities. These images were sent electronically to an experienced lay reader who independently graded them for vascular abnormalities. The images also were analyzed using the ROPtool, which assigns a numerical value to the level of vascular abnormality and tortuosity present in each of 4 quadrants or sectors. The ROPtool measurements of vascular abnormalities were graded and compared with expert panel grades with a receiver operating characteristic (ROC) curve. Grades between human readers were cross-tabulated. The area under the ROC curve was calculated for the ROPtool, and sensitivity and specificity were computed for the lay reader. Measurements of vascular abnormalities by ROPtool and grading of vascular abnormalities by 3 ROP experts and 1 experienced lay reader. The ROC curve for ROPtool's tortuosity assessment had an area under the ROC curve of 0.917. Using a threshold value of 4.97 for the second most tortuous quadrant, ROPtool's sensitivity was 91% and its specificity was 82%. Lay reader sensitivity and specificity were 99% and 73%, respectively, and had high reliability (κ, 0.87) in repeated measurements. ROPtool had very good accuracy for detection of vascular abnormalities suggestive of plus disease when compared with expert physician graders. The lay reader's results showed excellent sensitivity and good specificity when compared with those of the expert graders. These options for remote reading of images to detect vascular abnormalities deserve consideration in the quest to use telemedicine with remote reading for efficient delivery of high-quality care and to detect infants requiring bedside examination. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

[Value of sepsis single-disease manage system in predicting mortality in patients with sepsis].

PubMed

Chen, J; Wang, L H; Ouyang, B; Chen, M Y; Wu, J F; Liu, Y J; Liu, Z M; Guan, X D

2018-04-03

Objective: To observe the effect of sepsis single-disease manage system on the improvement of sepsis treatment and the value in predicting mortality in patients with sepsis. Methods: A retrospective study was conducted. Patients with sepsis admitted to the Department of Surgical Intensive Care Unit of Sun Yat-Sen University First Affiliated Hospital from September 22, 2013 to May 5, 2015 were enrolled in this study. Sepsis single-disease manage system (Rui Xin clinical data manage system, China data, China) was used to monitor 25 clinical quality parameters, consisting of timeliness, normalization and outcome parameters. Based on whether these quality parameters could be completed or not, the clinical practice was evaluated by the system. The unachieved quality parameter was defined as suspicious parameters, and these suspicious parameters were used to predict mortality of patients with receiver operating characteristic curve (ROC). Results: A total of 1 220 patients with sepsis were enrolled, included 805 males and 415 females. The mean age was (59±17) years, and acute physiology and chronic health evaluation (APACHE Ⅱ) scores was 19±8. The area under ROC curve of total suspicious numbers for predicting 28-day mortality was 0.70; when the suspicious parameters number was more than 6, the sensitivity was 68.0% and the specificity was 61.0% for predicting 28-day mortality. In addition, the area under ROC curve of outcome suspicious number for predicting 28-day mortality was 0.89; when the suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 78.0% for predicting 28-day mortality. Moreover, the area under ROC curve of total suspicious number for predicting 90-day mortality was 0.73; when the total suspicious parameters number was more than 7, the sensitivity was 60.0% and the specificity was 74.0% for predicting 90-day mortality. Finally, the area under ROC curve of outcome suspicious numbers for predicting 90-day mortality was 0.92; when suspicious outcome parameters numbers was more than 1, the sensitivity was 88.0% and the specificity was 81.0% for predicting 90-day mortality. Conclusion: The single center study suggests that this sepsis single-disease manage system could be used to monitor the completion of clinical practice for intensivist in managing sepsis, and the number of quality parameters failed to complete could be used to predict the mortality of the patients.

[Support vector machine?assisted diagnosis of human malignant gastric tissues based on dielectric properties].

PubMed

Zhang, Sa; Li, Zhou; Xin, Xue-Gang

2017-12-20

To achieve differential diagnosis of normal and malignant gastric tissues based on discrepancies in their dielectric properties using support vector machine. The dielectric properties of normal and malignant gastric tissues at the frequency ranging from 42.58 to 500 MHz were measured by coaxial probe method, and the Cole?Cole model was used to fit the measured data. Receiver?operating characteristic (ROC) curve analysis was used to evaluate the discrimination capability with respect to permittivity, conductivity, and Cole?Cole fitting parameters. Support vector machine was used for discriminating normal and malignant gastric tissues, and the discrimination accuracy was calculated using k?fold cross? The area under the ROC curve was above 0.8 for permittivity at the 5 frequencies at the lower end of the measured frequency range. The combination of the support vector machine with the permittivity at all these 5 frequencies combined achieved the highest discrimination accuracy of 84.38% with a MATLAB runtime of 3.40 s. The support vector machine?assisted diagnosis is feasible for human malignant gastric tissues based on the dielectric properties.

Parameters for screening music performance anxiety.

PubMed

Barbar, Ana E; Crippa, José A; Osório, Flávia L

2014-09-01

To assess the discriminative capacity of the Kenny Music Performance Anxiety Inventory (K-MPAI), in its version adapted for Brazil, in a sample of 230 Brazilian adult musicians. The Social Phobia Inventory (SPIN) was used to assess the presence of social anxiety indicators, adopting it as the gold standard. The Mann-Whitney U test and the receiver operating characteristic (ROC) curve were used for statistical analysis, with p ≤ 0.05 set as the significance level. Subjects with social anxiety indicators exhibited higher mean total K-MPAI scores, as well as higher individual scores on 62% of its items. The area under the ROC curve was 0.734 (p = 0.001), and considered appropriate. Within the possible cutoff scores presented, the score -15 had the best balance of sensitivity and specificity values. However, the score -7 had greater specificity and accuracy. The K-MPAI showed appropriate discriminant validity, with a marked association between music performance anxiety and social anxiety. The cutoff scores presented in the study have both clinical and research value, allowing screening for music performance anxiety and identification of possible cases.

Variable corneal compensation improves discrimination between normal and glaucomatous eyes with the scanning laser polarimeter.

PubMed

Tannenbaum, Dana P; Hoffman, Douglas; Lemij, Hans G; Garway-Heath, David F; Greenfield, David S; Caprioli, Joseph

2004-02-01

The presently available scanning laser polarimeter (SLP) has a fixed corneal compensator (FCC) that neutralizes corneal birefringence only in eyes with birefringence that matches the population mode. A prototype variable corneal compensator (VCC) provides neutralization of individual corneal birefringence based on individual macular retardation patterns. The aim of this study was to evaluate the relative ability of the SLP with the FCC and with the VCC to discriminate between normal and glaucomatous eyes. Prospective, nonrandomized, comparative case series. Algorithm-generating set consisting of 56 normal eyes and 55 glaucomatous eyes and an independent data set consisting of 83 normal eyes and 56 glaucomatous eyes. Sixteen retardation measurements were obtained with the SLP with the FCC and the VCC from all subjects. Dependency of parameters on age, gender, ethnic origin, and eye side was sought. Logistic regression was used to evaluate how well the various parameters could detect glaucoma. Discriminant functions were generated, and the area under the receiver operating characteristic (ROC) curve was determined. Discrimination between normal and glaucomatous eyes on the basis of single parameters was significantly better with the VCC than with the FCC for 6 retardation parameters: nasal average (P = 0.0003), superior maximum (P = 0.0003), ellipse average (P = 0.002), average thickness (P = 0.003), superior average (P = 0.010), and inferior average (P = 0.010). Discriminant analysis identified the optimal combination of parameters for the FCC and for the VCC. When the discriminant functions were applied to the independent data set, areas under the ROC curve were 0.84 for the FCC and 0.90 for the VCC (P<0.021). When the discriminant functions were applied to a subset of patients with early visual field loss, areas under the ROC curve were 0.82 for the FCC and 0.90 for the VCC (P<0.016). Individual correction for corneal birefringence with the VCC significantly improved the ability of the SLP to distinguish between normal and glaucomatous eyes and enabled detection of patients with early glaucoma.

Predicting a roadkill hotspots based on spatial distribution of Korean water deer (Hydropotes inermis argyropus) using Maxent model in South Korea Expressway : In Case of Cheongju-Sangju Expressway

NASA Astrophysics Data System (ADS)

Park, Hyomin; Lee, Sangdon

2016-04-01

Road construction has direct and indirect effects on ecosystems. Especially wildlife-vehicle conflicts (roadkills) caused by roads are a considerable threat for population of many species. This study aims to identify the effects of topographic characteristics and spatial distribution of Korean water deer (Hydropotes inermis). Korean water deer is indigenous and native species in Korea that listed LC (least concern) by IUCN redlist categories. Korean water deer population is growing every year occupying for most of roadkills (>70%) in Korean express highway. In order to predict a distribution of the Korean water deer, we selected factors that most affected water deer's habitat. Major habitats of waterdeer are known as agricultural area, forest area and water. Based on this result, eight factors were selected (land cover map, vegetation map, age class of forest, diameter class of tree, population, slope of study site, elevation of study site, distance of river), and made a thematic map by using GIS program (ESRI, Arc GIS 10.3.1 ver.). To analyze the affected factors of waterdeer distribution, GPS data and thematic map of study area were entered into Maxent model (Maxent 3.3.3.k.). Results of analysis were verified by the AUC (Area Unit Curve) of ROC (Receiver Operating Characteristic). The ROC curve used the sensitivity and specificity as a reference for determining the prediction efficiency of the model and AUC area of ROC curve was higher prediction efficiency closer to '1.' Selecting factors that affected the distribution of waterdeer were land cover map, diameter class of tree and elevation of study site. The value of AUC was 0.623. To predict the water deer's roadkills hot spot on Cheongju-Sangju Expressway, the thematic map was prepared based on GPS data of roadkill spots. As a result, the topographic factors that affected waterdeer roadkill were land cover map, actual vegetation map and age class of forest and the value of AUC was 0.854. Through this study, we could identify the site and hot spots that water deer frequently expected to use based on quantitative data on the spatial and topographic factors. Therefore, we can suggest ways to minimize roadkills by selecting the hot spots and by suggesting construction of eco-corridors. This study will significantly enhance human-wildlife conflicts by identifying key habitat areas for wild mammals.

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

NASA Astrophysics Data System (ADS)

Marghany, Maged

2016-10-01

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

Lactate clearance cut off for early mortality prediction in adult sepsis and septic shock patients

NASA Astrophysics Data System (ADS)

Sinto, R.; Widodo, D.; Pohan, H. T.

2018-03-01

Previous lactate clearance cut off for early mortality prediction in sepsis and septic shock patient was determined by consensus from small sample size-study. We investigated the best lactate clearance cut off and its ability to predict early mortality in sepsis and septic shock patients. This cohort study was conducted in Intensive Care Unit of CiptoMangunkusumo Hospital in 2013. Patients’ lactate clearance and eight other resuscitationendpoints were recorded, and theoutcome was observed during the first 120 hours. The clearance cut off was determined using receiver operating characteristic (ROC) analysis, and its ability was investigated with Cox’s proportional hazard regression analysis using other resuscitation endpoints as confounders. Total of 268 subjects was included, of whom 70 (26.11%) subjects died within the first 120 hours. The area under ROC of lactate clearance to predict early mortality was 0.78 (95% % confidence interval [CI] 0.71-0.84) with best cut off was <7.5% (sensitivity and specificity 88.99% and 81.4% respectively). Compared with group achieving lactate clearance target, group not achieving lactate clearance target had to increase early mortality risk (adjusted hazard ratio 13.42; 95%CI 7.19-25.07). In conclusion, the best lactate clearance cut off as anearly mortality predictor in sepsis and septic shock patients is 7.5%.

Factors associated with an inadequate hypoglycemia in the insulin tolerance test in Japanese patients with suspected or proven hypopituitarism.

PubMed

Takahashi, Kiyohiko; Nakamura, Akinobu; Miyoshi, Hideaki; Nomoto, Hiroshi; Kameda, Hiraku; Cho, Kyu Yong; Nagai, So; Shimizu, Chikara; Taguri, Masataka; Terauchi, Yasuo; Atsumi, Tatsuya

2017-04-29

We attempted to identify the predictors of an inadequate hypoglycemia in insulin tolerance test (ITT), defined as a blood glucose level higher than 2.8 mmol/L after insulin injection, in Japanese patients with suspected or proven hypopituitarism. A total of 78 patients who had undergone ITT were divided into adequate and inadequate hypoglycemia groups. The relationships between the subjects' clinical parameters and inadequate hypoglycemia in ITT were analyzed. Stepwise logistic regression analysis identified high systolic blood pressure (SBP) and high homeostasis model assessment of insulin resistance (HOMA-IR) as being independent factors associated with inadequate hypoglycemia in ITT. Receiver operating characteristic (ROC) curve analysis revealed the cutoff value for inadequate hypoglycemia was 109 mmHg for SBP and 1.4 for HOMA-IR. The areas under ROC curve for SBP and HOMA-IR were 0.72 and 0.86, respectively. We confirmed that high values of SBP and HOMA-IR were associated with inadequate hypoglycemia in ITT, regardless of the degree of reduction of pituitary hormone levels. Furthermore, the strongest predictor of inadequate hypoglycemia was obtained by using the cutoff value of HOMA-IR. Our results suggest that HOMA-IR is a useful pre-screening tool for ITT in these populations.

Quantity of Candida Colonies in Saliva: 
A Diagnostic Evaluation for Oral Candidiasis.

PubMed

Zhou, Pei Ru; Hua, Hong; Liu, Xiao Song

To investigate the relationship between the quantity of Candida colonies in saliva and oral candidiasis (OC), as well as to identify the threshold for distinguishing oral candidiasis from healthy carriage. A diagnostic test was conducted in 197 patients with different oral problems. The diagnosis of OC was established based on clinical features. Whole saliva samples from the subjects were cultured for Candida species. Receiver operating characteristic (ROC) curve analysis was used in this study. OC patients had significantly more Candida colony-forming units per millilitre saliva (795 cfu/ml) than asymptomatic carriers (40 cfu/ml; P < 0.05). Among different types of candidiasis, the quantity of Candida colonies differed. The number of Candida colonies in pseudomembranous type was significantly higher than that in the erythematous type (P < 0.05). Candida albicans was the predominant species of Candida. The cut-off point with the best fit for OC diagnosis was calculated to be 266 cfu/ml. The sensitivity and specificity were 0.720 and 0.825, respectively. Analysis of the ROC curve indicated that Candida colonies had a high diagnostic value for OC, as demonstrated by the area under the curve (AUC = 0.873). Based on this study, the value of 270 cfu/ml was considered a threshold for distinguishing OC from carriage.

Increased Levels of Circulating Anti-ANXA1 IgG Antibody in Patients with Non-Small Cell Lung Cancer.

PubMed

Liang, Tingting; Han, Zhifeng; Zhao, Huan; Zhang, Xuan; Wang, Yao

2018-06-01

Our previous studies revealed that concentrations of circulating antibodies to annexin A1 (ANXA1) were increased in non-small lung cancer (NSCLC). This study was thus designed to replicate this initial finding with an independent sample set. An enzyme-linked immunosorbent assay (ELISA) was developed in-house to examine plasma antiANXA1 IgG levels in 220 patients with NSCLC and 200 control subjects. Mann-Whitney U test showed that patients with NSCLC had significantly higher anti-ANXA1 IgG levels than control subjects (Z = -4.02, p < 0.001); male patients appeared to mainly contribute to the increased antibody level (Z = -3.09, p = 0.002). Receiver operating characteristic (ROC) curve analysis showed an overall area under the ROC curve (AUC) of 0.61 (95% CI: 0.56 - 0.67), with sensitivity of 8% against a specificity of 95.0%. Spearman's correlation analysis failed to show a significant correlation between the anti-ANXA1 IgG levels and the expression of three tumor-associated antigens including p53 (r = 0.156, p = 0.027), Ki67 (r = -0.048, p = 0.489), and EGFR (r = 0.02, p = 0.782). Increased levels of circulating anti-ANXA1 IgG antibody may have a prognostic value for NSCLC.

Carboxyhemoglobin Formation in Preterm Infants Is Related to the Subsequent Development of Bronchopulmonary Dysplasia

PubMed Central

Tokuriki, Shuko; Okuno, Takashi; Ohta, Genrei

2015-01-01

Objective. To evaluate the usefulness of carboxyhemoglobin (CO-Hb) levels as a biomarker to predict the development and severity of bronchopulmonary dysplasia (BPD). Methods. Twenty-five infants born at <33 wk of gestational age or with a birth weight of <1,500 g were enrolled. CO-Hb levels were measured between postnatal days 5 and 8, 12 and 15, 19 and 22, and 26 and 29. Urinary levels of 8-hydroxydeoxyguanosine (8-OHdG), advanced oxidation protein products, and Nε-(hexanoyl) lysine were measured between postnatal days 5 and 8 and 26 and 29. Receiver operating characteristic (ROC) analysis was used to compare the biomarkers' predictive values. Results. Compared with infants in the no-or-mild BPD group, infants with moderate-to-severe BPD exhibited higher CO-Hb levels during the early postnatal period and higher 8-OHdG levels between postnatal days 5 and 8. Using ROC analysis to predict the development of moderate-to-severe BPD, the area under the curve (AUC) for CO-Hb levels between postnatal days 5 and 8 was higher than AUCs for the urinary markers. Conclusions. CO-Hb levels during the early postnatal period may serve as a practical marker for evaluating oxidative stress and the severity of subsequently developing BPD. PMID:26294808

Carboxyhemoglobin Formation in Preterm Infants Is Related to the Subsequent Development of Bronchopulmonary Dysplasia.

PubMed

Tokuriki, Shuko; Okuno, Takashi; Ohta, Genrei; Ohshima, Yusei

2015-01-01

To evaluate the usefulness of carboxyhemoglobin (CO-Hb) levels as a biomarker to predict the development and severity of bronchopulmonary dysplasia (BPD). Twenty-five infants born at <33 wk of gestational age or with a birth weight of <1,500 g were enrolled. CO-Hb levels were measured between postnatal days 5 and 8, 12 and 15, 19 and 22, and 26 and 29. Urinary levels of 8-hydroxydeoxyguanosine (8-OHdG), advanced oxidation protein products, and Nε-(hexanoyl) lysine were measured between postnatal days 5 and 8 and 26 and 29. Receiver operating characteristic (ROC) analysis was used to compare the biomarkers' predictive values. Compared with infants in the no-or-mild BPD group, infants with moderate-to-severe BPD exhibited higher CO-Hb levels during the early postnatal period and higher 8-OHdG levels between postnatal days 5 and 8. Using ROC analysis to predict the development of moderate-to-severe BPD, the area under the curve (AUC) for CO-Hb levels between postnatal days 5 and 8 was higher than AUCs for the urinary markers. CO-Hb levels during the early postnatal period may serve as a practical marker for evaluating oxidative stress and the severity of subsequently developing BPD.

Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively.

PubMed

Lahner, D; Kabon, B; Marschalek, C; Chiari, A; Pestel, G; Kaider, A; Fleischmann, E; Hetz, H

2009-09-01

Fluid management guided by oesophageal Doppler monitor has been reported to improve perioperative outcome. Stroke volume variation (SVV) is considered a reliable clinical predictor of fluid responsiveness. Consequently, the aim of the present trial was to evaluate the accuracy of SVV determined by arterial pulse contour (APCO) analysis, using the FloTrac/Vigileo system, to predict fluid responsiveness as measured by the oesophageal Doppler. Patients undergoing major abdominal surgery received intraoperative fluid management guided by oesophageal Doppler monitoring. Fluid boluses of 250 ml each were administered in case of a decrease in corrected flow time (FTc) to <350 ms. Patients were connected to a monitoring device, obtaining SVV by APCO. Haemodynamic variables were recorded before and after fluid bolus application. Fluid responsiveness was defined as an increase in stroke volume index >10%. The ability of SVV to predict fluid responsiveness was assessed by calculation of the area under the receiver operating characteristic (ROC) curve. Twenty patients received 67 fluid boluses. Fifty-two of the 67 fluid boluses administered resulted in fluid responsiveness. SVV achieved an area under the ROC curve of 0.512 [confidence interval (CI) 0.32-0.70]. A cut-off point for fluid responsiveness was found for SVV > or =8.5% (sensitivity: 77%; specificity: 43%; positive predictive value: 84%; and negative predictive value: 33%). This prospective, interventional observer-blinded study demonstrates that SVV obtained by APCO, using the FloTrac/Vigileo system, is not a reliable predictor of fluid responsiveness in the setting of major abdominal surgery.

Analysis of deep inferior epigastric perforator (DIEP) arteries by using MDCTA: Comparison between 2 post-processing techniques.

PubMed

Saba, Luca; Atzeni, Matteo; Ribuffo, Diego; Mallarini, Giorgio; Suri, Jasjit S

2012-08-01

Our purpose was to compare two post-processing techniques, Maximum-Intensity-Projection (MIP) and Volume Rendering (VR) for the study of perforator arteries. Thirty patients who underwent Multi-Detector-Row CT Angiography (MDCTA) between February 2010 and May 2010 were retrospectively analyzed. For each patient and for each reconstruction method, the image quality was evaluated and the inter- and intra-observer agreement was calculated according to the Cohen statistics. The Hounsfield Unit (HU) value in the common femoral artery was quantified and the correlation (Pearson Statistic) between image quality and HU value was explored. The Pearson r between the right and left common femoral artery was excellent (r=0.955). The highest image quality score was obtained using MIP for both observers (total value 75, with a mean value 2.67 for observer 1 and total value of 79 and a mean value of 2.82 for observer 2). The highest agreement between the two observers was detected using the MIP protocol with a Cohen kappa value of 0.856. The ROC area under the curve (Az) for the VR is 0.786 (0.086 SD; p value=0.0009) whereas the ROC area under the curve (Az) for the MIP is 0.0928 (0.051 SD; p value=0.0001). MIP showed the optimal inter- and intra-observer agreement and the highest quality scores and therefore should be used as post-processing techniques in the analysis of perforating arteries. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A study on risk factors and diagnostic efficiency of posthepatectomy liver failure in the nonobstructive jaundice.

PubMed

Wang, He; Lu, Shi-Chun; He, Lei; Dong, Jia-Hong

2018-02-01

Liver failure remains as the most common complication and cause of death after hepatectomy, and continues to be a challenge for doctors.t test and χ test were used for single factor analysis of data-related variables, then results were introduced into the model to undergo the multiple factors logistic regression analysis. Pearson correlation analysis was performed for related postoperative indexes, and a diagnostic evaluation was performed using the receiver operating characteristic (ROC) of postoperative indexes.Differences in age, body mass index (BMI), portal vein hypertension, bile duct cancer, total bilirubin, alkaline phosphatase (ALP), gamma-glutamyl transpeptidase (GGT), operation time, cumulative portal vein occlusion time, intraoperative blood volume, residual liver volume (RLV)/entire live rvolume, ascites volume at postoperative day (POD)3, supplemental albumin amount at POD3, hospitalization time after operation, and the prothrombin activity (PTA) were statistically significant. Furthermore, there were significant differences in total bilirubin and the supplemental albumin amount at POD3. ROC analysis of the average PTA, albumin amounts, ascites volume at POD3, and their combined diagnosis were performed, which had diagnostic value for postoperative liver failure (area under the curve (AUC): 0.895, AUC: 0.798, AUC: 0.775, and AUC: 0.903).Preoperative total bilirubin level and the supplemental albumin amount at POD3 were independent risk factors. PTA can be used as the index of postoperative liver failure, and the combined diagnosis of the indexes can improve the early prediction of postoperative liver failure.

Decision making for pancreatic resection in patients with intraductal papillary mucinous neoplasms.

PubMed

Xu, Bin; Ding, Wei-Xing; Jin, Da-Yong; Wang, Dan-Song; Lou, Wen-Hui

2013-03-07

To identify a practical approach for preoperative decision-making in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. Between March 1999 and November 2006, the clinical characteristics, pathological data and computed tomography/magnetic resonance imaging (CT/MRI) of 54 IPMNs cases were retrieved and analyzed. The relationships between the above data and decision-making for pancreatic resection were analyzed using SPSS 13.0 software. Univariate analysis of risk factors for malignant or invasive IPMNs was performed with regard to the following variables: carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9) and the characteristics from CT/MRI images. Receiver operating characteristic (ROC) curve analysis for pancreatic resection was performed using significant factors from the univariate analysis. CT/MRI images, including main and mixed duct IPMNs, tumor size > 30 mm or a solid component appearance in the lesion, and preoperative serum CA19-9 > 37 U/mL had good predictive value for determining pancreatic resection (P < 0.05), but with limitations. Combining the above factors (CT/MRI images and CA19-9) improved the accuracy and sensitivity for determining pancreatic resection in IPMNs. Using ROC analysis, the area under the curve reached 0.893 (P < 0.01, 95%CI: 0.763-1.023), with a sensitivity, specificity, positive predictive value and negative predictive value of 95.2%, 83.3%, 95.2% and 83.3%, respectively. Combining preoperative CT/MRI images and CA19-9 level may provide useful information for surgical decision-making in IPMNs.

Differences in the associations of anthropometric measures with insulin resistance and type 2 diabetes mellitus between Korean and US populations: Comparisons of representative nationwide sample data.

PubMed

Yoon, Yeong Sook; Choi, Han Seok; Kim, Jin Kuk; Kim, Yu Il; Oh, Sang Woo

Variation among ethnic groups in the association between obesity and insulin resistance (IR)/diabetes has been suggested, but studies reported inconsistent results. We evaluated ethnic differences in the association between obesity and insulin resistance (IR)/diabetes. We conducted a cross-sectional analysis using Korea (n=18,845) and the USA (n=4657) National Health and Nutrition Examination Survey(NHANES) 2007-2010. We performed statistical comparisons of AUC-ROC (area under the curve in a receiver operating characteristic curve) values for body mass index (BMI), waist circumference (WC) and homeostasis model assessment of insulin resistance (HOMA-IR) to predict IR or diabetes among different ethnic groups. AUC-ROC values for BMI and WC for predicting IR were highest in Whites (0.8324 and 0.8468) and lowest in Koreans (0.7422 and 0.7367). Whites showed the highest AUC-ROC values for BMI (0.6869) and WC (0.7421) for predicting diabetes, while the AUC-ROC for HOMA-IR was highest in Koreans (0.8861). Linear regression showed significant interactions between ethnicity and the main effects (all P<0.0001). Increases in BMI were associated with a larger increase in HOMA-IR in Whites (β=0.0719) and WC in Hispanics (β=0.0324), while BMI was associated with a larger increase in fasting glucose in Koreans (β=0.8279) and WC in Blacks (β=0.4037). In addition, the slope for fasting glucose with increasing HOMA-IR was steeper in Koreans (β=16.5952, P<0.001) than in other groups. The ability of BMI and WC to predict IR and diabetes was highest in Whites, while the ability of HOMA-IR to predict diabetes was highest in Koreans. Copyright © 2015 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

Accuracy of Automated Flow Cytometry-Based Leukocyte Counts To Rule Out Urinary Tract Infection in Febrile Children: a Prospective Cross-Sectional Study

PubMed Central

Duong, Hong Phuoc; Wissing, Karl Martin; Tram, Nathalie; Mascart, Georges; Lepage, Philippe

2016-01-01

Automated flow cytometry of urine remains an incompletely validated method to rule out urinary tract infection (UTI) in children. This cross-sectional analytical study was performed to compare the predictive values of flow cytometry and a dipstick test as initial diagnostic tests for UTI in febrile children and prospectively included 1,106 children (1,247 episodes). Urine culture was used as the gold standard test for diagnosing UTI. The performance of screening tests to diagnose UTI were established using receiver operating characteristic (ROC) analysis. Among these 1,247 febrile episodes, 221 UTIs were diagnosed (17.7% [95% confidence interval {CI}, 15.6 to 19.8%]). The area under the ROC curve for flow cytometry white blood cell (WBC) counts (0.99 [95% CI, 0.98 to 0.99]) was significantly superior to that for red blood cell (0.74 [95% CI, 0.70 to 0.78]) and bacterial counts (0.89 [95% CI, 0.87 to 0.92]) (P < 0.001). Urinary WBC counts also had a significantly higher area under the ROC curve than that of the leukocyte esterase (LE) dipstick (0.92 [95% CI, 0.90 to 0.94]), nitrite dipstick (0.83 [95% CI, 0.80 to 0.87]), or the combination of positive LE and/or nitrite dipstick (0.91 [95% CI, 0.89 to 0.93]) test (P < 0.001). The presence of ≥35 WBC/μl of urine was the best cutoff point, yielding both a high sensitivity (99.5% [95% CI, 99 to 100%]) and an acceptable specificity (80.6% [95% CI, 78 to 83%]). Using this cutoff point would have reduced the number of samples sent to the laboratory for culture by 67%. In conclusion, the determination of urinary WBC counts by flow cytometry provides optimal performance as an initial diagnostic test for UTI in febrile children. PMID:27682127

Validation of the Predictive Value of Modeled Human Chorionic Gonadotrophin Residual Production in Low-Risk Gestational Trophoblastic Neoplasia Patients Treated in NRG Oncology/Gynecologic Oncology Group-174 Phase III Trial.

PubMed

You, Benoit; Deng, Wei; Hénin, Emilie; Oza, Amit; Osborne, Raymond

2016-01-01

In low-risk gestational trophoblastic neoplasia, chemotherapy effect is monitored and adjusted with serum human chorionic gonadotrophin (hCG) levels. Mathematical modeling of hCG kinetics may allow prediction of methotrexate (MTX) resistance, with production parameter "hCGres." This approach was evaluated using the GOG-174 (NRG Oncology/Gynecologic Oncology Group-174) trial database, in which weekly MTX (arm 1) was compared with dactinomycin (arm 2). Database (210 patients, including 78 with resistance) was split into 2 sets. A 126-patient training set was initially used to estimate model parameters. Patient hCG kinetics from days 7 to 45 were fit to: [hCG(time)] = hCG7 * exp(-k * time) + hCGres, where hCGres is residual hCG tumor production, hCG7 is the initial hCG level, and k is the elimination rate constant. Receiver operating characteristic (ROC) analyses defined putative hCGRes predictor of resistance. An 84-patient test set was used to assess prediction validity. The hCGres was predictive of outcome in both arms, with no impact of treatment arm on unexplained variability of kinetic parameter estimates. The best hCGres cutoffs to discriminate resistant versus sensitive patients were 7.7 and 74.0 IU/L in arms 1 and 2, respectively. By combining them, 2 predictive groups were defined (ROC area under the curve, 0.82; sensitivity, 93.8%; specificity, 70.5%). The predictive value of hCGres-based groups regarding resistance was reproducible in test set (ROC area under the curve, 0.81; sensitivity, 88.9%; specificity, 73.1%). Both hCGres and treatment arm were associated with resistance by logistic regression analysis. The early predictive value of the modeled kinetic parameter hCGres regarding resistance seems promising in the GOG-174 study. This is the second positive evaluation of this approach. Prospective validation is warranted.

Diagnosis of adrenal insufficiency.

PubMed

Dorin, Richard I; Qualls, Clifford R; Crapo, Lawrence M

2003-08-05

The cosyntropin stimulation test is the initial endocrine evaluation of suspected primary or secondary adrenal insufficiency. To critically review the utility of the cosyntropin stimulation test for evaluating adrenal insufficiency. The MEDLINE database was searched from 1966 to 2002 for all English-language papers related to the diagnosis of adrenal insufficiency. Studies with fewer than 5 persons with primary or secondary adrenal insufficiency or with fewer than 10 persons as normal controls were excluded. For secondary adrenal insufficiency, only studies that stratified participants by integrated tests of adrenal function were included. Summary receiver-operating characteristic (ROC) curves were generated from all studies that provided sensitivity and specificity data for 250-microg and 1-microg cosyntropin tests; these curves were then compared by using area under the curve (AUC) methods. All estimated values are given with 95% CIs. At a specificity of 95%, sensitivities were 97%, 57%, and 61% for summary ROC curves in tests for primary adrenal insufficiency (250-microg cosyntropin test), secondary adrenal insufficiency (250-microg cosyntropin test), and secondary adrenal insufficiency (1-microg cosyntropin test), respectively. The area under the curve for primary adrenal insufficiency was significantly greater than the AUC for secondary adrenal insufficiency for the high-dose cosyntropin test (P < 0.001), but AUCs for the 250-microg and 1-microg cosyntropin tests did not differ significantly (P > 0.5) for secondary adrenal insufficiency. At a specificity of 95%, summary ROC analysis for the 250-microg cosyntropin test yielded a positive likelihood ratio of 11.5 (95% CI, 8.7 to 14.2) and a negative likelihood ratio of 0.45 (CI, 0.30 to 0.60) for the diagnosis of secondary adrenal insufficiency. Cortisol response to cosyntropin varies considerably among healthy persons. The cosyntropin test performs well in patients with primary adrenal insufficiency, but the lower sensitivity in patients with secondary adrenal insufficiency necessitates use of tests involving stimulation of the hypothalamus if the pretest probability is sufficiently high. The operating characteristics of the 250-microg and 1-microg cosyntropin tests are similar.

The "surprise question" for predicting death in seriously ill patients: a systematic review and meta-analysis.

PubMed

Downar, James; Goldman, Russell; Pinto, Ruxandra; Englesakis, Marina; Adhikari, Neill K J

2017-04-03

The surprise question - "Would I be surprised if this patient died in the next 12 months?" - has been used to identify patients at high risk of death who might benefit from palliative care services. Our objective was to systematically review the performance characteristics of the surprise question in predicting death. We searched multiple electronic databases from inception to 2016 to identify studies that prospectively screened patients with the surprise question and reported on death at 6 to 18 months. We constructed models of hierarchical summary receiver operating characteristics (sROCs) to determine prognostic performance. Sixteen studies (17 cohorts, 11 621 patients) met the selection criteria. For the outcome of death at 6 to 18 months, the pooled prognostic characteristics were sensitivity 67.0% (95% confidence interval [CI] 55.7%-76.7%), specificity 80.2% (73.3%-85.6%), positive likelihood ratio 3.4 (95% CI 2.8-4.1), negative likelihood ratio 0.41 (95% CI 0.32-0.54), positive predictive value 37.1% (95% CI 30.2%-44.6%) and negative predictive value 93.1% (95% CI 91.0%-94.8%). The surprise question had worse discrimination in patients with noncancer illness (area under sROC curve 0.77 [95% CI 0.73-0.81]) than in patients with cancer (area under sROC curve 0.83 [95% CI 0.79-0.87; p = 0.02 for difference]). Most studies had a moderate to high risk of bias, often because they had a low or unknown participation rate or had missing data. The surprise question performs poorly to modestly as a predictive tool for death, with worse performance in noncancer illness. Further studies are needed to develop accurate tools to identify patients with palliative care needs and to assess the surprise question for this purpose. © 2017 Canadian Medical Association or its licensors.

Validation of Thwaites Index for diagnosing tuberculous meningitis in a Colombian population.

PubMed

Saavedra, Juan Sebastián; Urrego, Sebastián; Toro, María Eugenia; Uribe, Carlos Santiago; García, Jenny; Hernández, Olga; Arango, Juan Carlos; Pérez, Ángela Beatriz; Franco, Andrés; Vélez, Isabel Cristina; Del Corral, Helena

2016-11-15

To determine the diagnostic accuracy of Thwaites Index (TI) in a Colombian population to distinguish meningeal tuberculosis (MTB) from bacterial meningitis (BM) and from non-tuberculous meningitis. Exploratory analyses were conducted to assess the TI's validity for patients with human immunodeficiency virus (HIV) and children above six-years-old. The study included 527 patients, the TI was calculated and results compared with those of a reference standard established by expert neurologists. Sensitivity, specificity, area under the curve of receiver-operator characteristics (AUC-ROC) and likelihood ratios were calculated. The AUC-ROC to distinguish MTB from non-tuberculous meningitis was 0.72 (95% CI: 0.67-0.77) for HIV negative adults. AUC-ROC was 0.62 (95% CI: 0.50-0.74) for HIV positive adults and 0.83 (95% CI: 0.68-0.97) for children. For distinguishing MTB from BM the AUC-ROC was 0.78 (95% CI: 0.73-0.83); furthermore, the AUC-ROC was 0.57 (95% CI: 0.31-0.83) for HIV positive adults and 0.86 (95% CI: 0.73-0.99) for children. The TI was sensitive but not specific when used to distinguish MTB from BM in HIV negative adults. In HIV positive adults the index had low diagnostic accuracy. Moreover, the TI showed discrimination capability for children over 6years; however, research with larger samples is required in these. Copyright © 2016 Elsevier B.V. All rights reserved.

Teaching a Machine to Feel Postoperative Pain: Combining High-Dimensional Clinical Data with Machine Learning Algorithms to Forecast Acute Postoperative Pain

PubMed Central

Tighe, Patrick J.; Harle, Christopher A.; Hurley, Robert W.; Aytug, Haldun; Boezaart, Andre P.; Fillingim, Roger B.

2015-01-01

Background Given their ability to process highly dimensional datasets with hundreds of variables, machine learning algorithms may offer one solution to the vexing challenge of predicting postoperative pain. Methods Here, we report on the application of machine learning algorithms to predict postoperative pain outcomes in a retrospective cohort of 8071 surgical patients using 796 clinical variables. Five algorithms were compared in terms of their ability to forecast moderate to severe postoperative pain: Least Absolute Shrinkage and Selection Operator (LASSO), gradient-boosted decision tree, support vector machine, neural network, and k-nearest neighbor, with logistic regression included for baseline comparison. Results In forecasting moderate to severe postoperative pain for postoperative day (POD) 1, the LASSO algorithm, using all 796 variables, had the highest accuracy with an area under the receiver-operating curve (ROC) of 0.704. Next, the gradient-boosted decision tree had an ROC of 0.665 and the k-nearest neighbor algorithm had an ROC of 0.643. For POD 3, the LASSO algorithm, using all variables, again had the highest accuracy, with an ROC of 0.727. Logistic regression had a lower ROC of 0.5 for predicting pain outcomes on POD 1 and 3. Conclusions Machine learning algorithms, when combined with complex and heterogeneous data from electronic medical record systems, can forecast acute postoperative pain outcomes with accuracies similar to methods that rely only on variables specifically collected for pain outcome prediction. PMID:26031220

Contrast-Enhanced Ultrasound in the Diagnosis of Gallbladder Diseases: A Multi-Center Experience

PubMed Central

Liu, Lin-Na; Xu, Hui-Xiong; Lu, Ming-De; Xie, Xiao-Yan; Wang, Wen-Ping; Hu, Bing; Yan, Kun; Ding, Hong; Tang, Shao-Shan; Qian, Lin-Xue; Luo, Bao-Ming; Wen, Yan-Ling

2012-01-01

Objective To assess the usefulness of contrast–enhanced ultrasound (CEUS) in differentiating malignant from benign gallbladder (GB) diseases. Methods This study had institutional review board approval. 192 patients with GB diseases from 9 university hospitals were studied. After intravenous bonus injection of a phospholipid-stabilized shell microbubble contrast agent, lesions were scanned with low acoustic power CEUS. A multiple logistic regression analysis was performed to identify diagnostic clues from 17 independent variables that enabled differentiation between malignant and benign GB diseases. Receiver operating characteristic (ROC) curve analysis was performed. Results Among the 17 independent variables, multiple logistic regression analysis showed that the following 4 independent variables were associated with the benign nature of the GB diseases, including the patient age, intralesional blood vessel depicted on CEUS, contrast washout time, and wall intactness depicted on CEUS (all P<0.05). ROC analysis showed that the patient age, intralesional vessels on CEUS, and the intactness of the GB wall depicted on CEUS yielded an area under the ROC curve (Az) greater than 0.8 in each and Az for the combination of the 4 significant independent variables was 0.915 [95% confidence interval (CI): 0.857–0.974]. The corresponding Az, sensitivity, and specificity for the age were 0.805 (95% CI: 0.746–0.863), 92.2%%, and 59.6%; for the intralesional vessels on CEUS were 0.813 (95% CI: 0.751–0.875), 59.8%, and 98.0%; and for the GB wall intactness were 0.857 (95% CI: 0.786–0.928), 78.4%, and 92.9%. The cut-off values for benign GB diseases were patient age <53.5 yrs, dotted intralesional vessels on CEUS and intact GB wall on CEUS. Conclusion CEUS is valuable in differentiating malignant from benign GB diseases. Branched or linear intralesional vessels and destruction of GB wall on CEUS are the CEUS features highly suggestive of GB malignancy and the patient age >53.5 yrs is also a clue for GB malignancy. PMID:23118996

Measurement properties of the Dizziness Handicap Inventory by cross-sectional and longitudinal designs

PubMed Central

2009-01-01

Background The impact of dizziness on quality of life is often assessed by the Dizziness Handicap Inventory (DHI), which is used as a discriminate and evaluative measure. The aim of the present study was to examine reliability and validity of a translated Norwegian version (DHI-N), also examining responsiveness to important change in the construct being measured. Methods Two samples (n = 92 and n = 27) included participants with dizziness of mainly vestibular origin. A cross-sectional design was used to examine the factor structure (exploratory factor analysis), internal consistency (Cronbach's α), concurrent validity (Pearson's product moment correlation r), and discriminate ability (ROC curve analysis). Longitudinal designs were used to examine test-retest reliability (intraclass correlation coefficient (ICC) statistics, smallest detectable difference (SDD)), and responsiveness (Pearson's product moment correlation, ROC curve analysis; area under the ROC curve (AUC), and minimally important change (MIC)). The DHI scores range from 0 to 100. Results Factor analysis revealed a different factor structure than the original DHI, resulting in dismissal of subscale scores in the DHI-N. Acceptable internal consistency was found for the total scale (α = 0.95). Concurrent correlations between the DHI-N and other related measures were moderate to high, highest with Vertigo Symptom Scale-short form-Norwegian version (r = 0.69), and lowest with preferred gait (r = - 0.36). The DHI-N demonstrated excellent ability to discriminate between participants with and without 'disability', AUC being 0.89 and best cut-off point = 29 points. Satisfactory test-retest reliability was demonstrated, and the change for an individual should be ≥ 20 DHI-N points to exceed measurement error (SDD). Correlations between change scores of DHI-N and other self-report measures of functional health and symptoms were high (r = 0.50 - 0.57). Responsiveness of the DHI-N was excellent, AUC = 0.83, discriminating between self-perceived 'improved' versus 'unchanged' participants. The MIC was identified as 11 DHI-N points. Conclusions The DHI-N total scale demonstrated satisfactory measurement properties. This is the first study that has addressed and demonstrated responsiveness to important change of the DHI, and provided values of SDD and MIC to help interpret change scores. PMID:20025754

The Diesel Exhaust in Miners Study: II. Exposure monitoring surveys and development of exposure groups.

PubMed

Coble, Joseph B; Stewart, Patricia A; Vermeulen, Roel; Yereb, Daniel; Stanevich, Rebecca; Blair, Aaron; Silverman, Debra T; Attfield, Michael

2010-10-01

Air monitoring surveys were conducted between 1998 and 2001 at seven non-metal mining facilities to assess exposure to respirable elemental carbon (REC), a component of diesel exhaust (DE), for an epidemiologic study of miners exposed to DE. Personal exposure measurements were taken on workers in a cross-section of jobs located underground and on the surface. Air samples taken to measure REC were also analyzed for respirable organic carbon (ROC). Concurrent measurements to assess exposure to nitric oxide (NO) and nitrogen dioxide (NO₂), two gaseous components of DE, were also taken. The REC measurements were used to develop quantitative estimates of average exposure levels by facility, department, and job title for the epidemiologic analysis. Each underground job was assigned to one of three sets of exposure groups from specific to general: (i) standardized job titles, (ii) groups of standardized job titles combined based on the percentage of time in the major underground areas, and (iii) larger groups based on similar area carbon monoxide (CO) air concentrations. Surface jobs were categorized based on their use of diesel equipment and proximity to DE. A total of 779 full-shift personal measurements were taken underground. The average REC exposure levels for underground jobs with five or more measurements ranged from 31 to 58 μg m⁻³ at the facility with the lowest average exposure levels and from 313 to 488 μg m⁻³ at the facility with the highest average exposure levels. The average REC exposure levels for surface workers ranged from 2 to 6 μg m⁻³ across the seven facilities. There was much less contrast in the ROC compared with REC exposure levels measured between surface and underground workers within each facility, as well as across the facilities. The average ROC levels underground ranged from 64 to 195 μg m⁻³, while on the surface, the average ROC levels ranged from 38 to 71 μg m⁻³ by facility, an ∼2- to 3-fold difference. The average NO and NO₂ levels underground ranged from 0.20 to 1.49 parts per million (ppm) and from 0.10 to 0.60 ppm, respectively, and were ∼10 times higher than levels on the surface, which ranged from 0.02 to 0.11 ppm and from 0.01 to 0.06 ppm, respectively. The ROC, NO, and NO₂ concentrations underground were correlated with the REC levels (r = 0.62, 0.71, and 0.62, respectively). A total of 80% of the underground jobs were assigned an exposure estimate based on measurements taken for the specific job title or for other jobs with a similar percentage of time spent in the major underground work areas. The average REC exposure levels by facility were from 15 to 64 times higher underground than on the surface. The large contrast in exposure levels measured underground versus on the surface, along with the differences between the mining facilities and between underground jobs within the facilities resulted in a wide distribution in the exposure estimates for evaluation of exposure-response relationships in the epidemiologic analyses.

The Diesel Exhaust in Miners Study: II. Exposure Monitoring Surveys and Development of Exposure Groups

PubMed Central

Coble, Joseph B.; Stewart, Patricia A.; Vermeulen, Roel; Yereb, Daniel; Stanevich, Rebecca; Blair, Aaron; Silverman, Debra T.; Attfield, Michael

2010-01-01

Air monitoring surveys were conducted between 1998 and 2001 at seven non-metal mining facilities to assess exposure to respirable elemental carbon (REC), a component of diesel exhaust (DE), for an epidemiologic study of miners exposed to DE. Personal exposure measurements were taken on workers in a cross-section of jobs located underground and on the surface. Air samples taken to measure REC were also analyzed for respirable organic carbon (ROC). Concurrent measurements to assess exposure to nitric oxide (NO) and nitrogen dioxide (NO2), two gaseous components of DE, were also taken. The REC measurements were used to develop quantitative estimates of average exposure levels by facility, department, and job title for the epidemiologic analysis. Each underground job was assigned to one of three sets of exposure groups from specific to general: (i) standardized job titles, (ii) groups of standardized job titles combined based on the percentage of time in the major underground areas, and (iii) larger groups based on similar area carbon monoxide (CO) air concentrations. Surface jobs were categorized based on their use of diesel equipment and proximity to DE. A total of 779 full-shift personal measurements were taken underground. The average REC exposure levels for underground jobs with five or more measurements ranged from 31 to 58 μg m−3 at the facility with the lowest average exposure levels and from 313 to 488 μg m−3 at the facility with the highest average exposure levels. The average REC exposure levels for surface workers ranged from 2 to 6 μg m−3 across the seven facilities. There was much less contrast in the ROC compared with REC exposure levels measured between surface and underground workers within each facility, as well as across the facilities. The average ROC levels underground ranged from 64 to 195 μg m−3, while on the surface, the average ROC levels ranged from 38 to 71 μg m−3 by facility, an ∼2- to 3-fold difference. The average NO and NO2 levels underground ranged from 0.20 to 1.49 parts per million (ppm) and from 0.10 to 0.60 ppm, respectively, and were ∼10 times higher than levels on the surface, which ranged from 0.02 to 0.11 ppm and from 0.01 to 0.06 ppm, respectively. The ROC, NO, and NO2 concentrations underground were correlated with the REC levels (r = 0.62, 0.71, and 0.62, respectively). A total of 80% of the underground jobs were assigned an exposure estimate based on measurements taken for the specific job title or for other jobs with a similar percentage of time spent in the major underground work areas. The average REC exposure levels by facility were from 15 to 64 times higher underground than on the surface. The large contrast in exposure levels measured underground versus on the surface, along with the differences between the mining facilities and between underground jobs within the facilities resulted in a wide distribution in the exposure estimates for evaluation of exposure–response relationships in the epidemiologic analyses. PMID:20876232

ROC analysis of lesion descriptors in breast ultrasound images

NASA Astrophysics Data System (ADS)

Andre, Michael P.; Galperin, Michael; Phan, Peter; Chiu, Peter

2003-05-01

Breast biopsy serves as the key diagnostic tool in the evaluation of breast masses for malignancy, yet the procedure affects patients physically and emotionally and may obscure results of future mammograms. Studies show that high quality ultrasound can distinguish a benign from malignant lesions with accuracy, however, it has proven difficult to teach and clinical results are highly variable. The purpose of this study is to develop a means to optimize an automated Computer Aided Imaging System (CAIS) to assess Level of Suspicion (LOS) of a breast mass. We examine the contribution of 15 object features to lesion classification by calculating the Wilcoxon area under the ROC curve, AW, for all combinations in a set of 146 masses with known findings. For each interval A, the frequency of appearance of each feature and its combinations with others was computed as a means to find an "optimum" feature vector. The original set of 15 was reduced to 6 (area, perimeter, diameter ferret Y, relief, homogeneity, average energy) with an improvement from Aw=0.82-/+0.04 for the original 15 to Aw=0.93-/+0.02 for the subset of 6, p=0.03. For comparison, two sub-specialty mammography radiologists also scored the images for LOS resulting in Az of 0.90 and 0.87. The CAIS performed significantly higher, p=0.02.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Diagnostic value of blood-derived microRNAs for schizophrenia: results of a meta-analysis and validation.

PubMed

Liu, Sha; Zhang, Fuquan; Wang, Xijin; Shugart, Yin Yao; Zhao, Yingying; Li, Xinrong; Liu, Zhifen; Sun, Ning; Yang, Chunxia; Zhang, Kerang; Yue, Weihua; Yu, Xin; Xu, Yong

2017-11-10

There is an increasing interest in searching biomarkers for schizophrenia (SZ) diagnosis, which overcomes the drawbacks inherent with the subjective diagnostic methods. MicroRNA (miRNA) fingerprints have been explored for disease diagnosis. We performed a meta-analysis to examine miRNA diagnostic value for SZ and further validated the meta-analysis results. Using following terms: schizophrenia/SZ, microRNA/miRNA, diagnosis, sensitivity and specificity, we searched databases restricted to English language and reviewed all articles published from January 1990 to October 2016. All extracted data were statistically analyzed and the results were further validated with peripheral blood mononuclear cells (PBMNCs) isolated from patients and healthy controls using RT-qPCR and receiver operating characteristic (ROC) analysis. A total of 6 studies involving 330 patients and 202 healthy controls were included for meta-analysis. The pooled sensitivity, specificity and diagnostic odds ratio were 0.81 (95% CI: 0.75-0.86), 0.81 (95% CI: 0.72-0.88) and 18 (95% CI: 9-34), respectively; the positive and negative likelihood ratio was 4.3 and 0.24 respectively; the area under the curve in summary ROC was 0.87 (95% CI: 0.84-0.90). Validation revealed that miR-181b-5p, miR-21-5p, miR-195-5p, miR-137, miR-346 and miR-34a-5p in PBMNCs had high diagnostic sensitivity and specificity in the context of schizophrenia. In conclusion, blood-derived miRNAs might be promising biomarkers for SZ diagnosis.

Improving fMRI reliability in presurgical mapping for brain tumours.

PubMed

Stevens, M Tynan R; Clarke, David B; Stroink, Gerhard; Beyea, Steven D; D'Arcy, Ryan Cn

2016-03-01

Functional MRI (fMRI) is becoming increasingly integrated into clinical practice for presurgical mapping. Current efforts are focused on validating data quality, with reliability being a major factor. In this paper, we demonstrate the utility of a recently developed approach that uses receiver operating characteristic-reliability (ROC-r) to: (1) identify reliable versus unreliable data sets; (2) automatically select processing options to enhance data quality; and (3) automatically select individualised thresholds for activation maps. Presurgical fMRI was conducted in 16 patients undergoing surgical treatment for brain tumours. Within-session test-retest fMRI was conducted, and ROC-reliability of the patient group was compared to a previous healthy control cohort. Individually optimised preprocessing pipelines were determined to improve reliability. Spatial correspondence was assessed by comparing the fMRI results to intraoperative cortical stimulation mapping, in terms of the distance to the nearest active fMRI voxel. The average ROC-r reliability for the patients was 0.58±0.03, as compared to 0.72±0.02 in healthy controls. For the patient group, this increased significantly to 0.65±0.02 by adopting optimised preprocessing pipelines. Co-localisation of the fMRI maps with cortical stimulation was significantly better for more reliable versus less reliable data sets (8.3±0.9 vs 29±3 mm, respectively). We demonstrated ROC-r analysis for identifying reliable fMRI data sets, choosing optimal postprocessing pipelines, and selecting patient-specific thresholds. Data sets with higher reliability also showed closer spatial correspondence to cortical stimulation. ROC-r can thus identify poor fMRI data at time of scanning, allowing for repeat scans when necessary. ROC-r analysis provides optimised and automated fMRI processing for improved presurgical mapping. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Filling in the blanks. An estimation of illicit cannabis growers' profits in Belgium.

PubMed

Vanhove, Wouter; Surmont, Tim; Van Damme, Patrick; De Ruyver, Brice

2014-05-01

As a result of increased pressure on cannabis cultivation in The Netherlands, the number of confiscated indoor cannabis plantations in Belgium is rising. Although increases are reported for all plantations sizes, half of the seized plantations contain less than 50 plants. In this study, factors and variables that influence costs and benefits of indoor cannabis cultivation are investigated as well as how these costs and benefits vary between different cannabis grower types. Real-situation data of four growers were used to perform financial analyses. Costs included fixed and variable material costs, as well as opportunity costs. Gross revenue per grow cycle was calculated based on most recent forensic findings for illicit Belgian cannabis plantations and was adjusted for the risk of getting caught. Finally, gross revenues and return on costs (ROC) were calculated over 1 year (4 cycles). Financial analysis shows that in all cases gross revenues as well as ROC are considerable, even after a single growth cycle. Highest profitability was found for large-scale (600 plants, ROC=6.8) and mid-scale plantations (150 plants, ROC=6.0). However, industrial plantations (23,000 plants, ROC=1.4) and micro-scale plantations (5 plants, ROC=2.8) are also highly remunerative. Shift of police focus away from micro-scale growers, least likely to be involved in criminal gangs, to large-scale and industrial scale plantations would influence costs as a result of changing risks of getting caught. However, sensitivity analysis shows that this does not significantly influence the conclusions on profitability of different types of indoor cannabis growers. Seizure and confiscation of profits are important elements in the integral and integrated policy approach required for tackling illicit indoor cannabis plantations. The large return of costs evidenced in the present study, underpin the policy relevance of confiscating those illicit profits as part of enforcement. Copyright © 2014 Elsevier B.V. All rights reserved.

Target Detection and Identification Using Canonical Correlations Analysis and Subspace Partitioning

DTIC Science & Technology

2008-04-01

Fig. 2. ROCs for DCC, DCC-P, NNLS, and NNLSP (Present chemical=t1, background= t56 , SNR= 5 dB) alarm, or 1−specificity, and PD is the probability of...discrimination values are given in each ROC plot. In Fig. 2, we use t56 as the background, and t1 as the target chemical. The SNR is 5 dB. For each

Can technical characteristics predict clinical performance in PET/CT imaging? A correlation study for thyroid cancer diagnosis

NASA Astrophysics Data System (ADS)

Kallergi, Maria; Menychtas, Dimitrios; Georgakopoulos, Alexandros; Pianou, Nikoletta; Metaxas, Marinos; Chatziioannou, Sofia

2013-03-01

The purpose of this study was to determine whether image characteristics could be used to predict the outcome of ROC studies in PET/CT imaging. Patients suspected for recurrent thyroid cancer underwent a standard whole body (WB) examination and an additional high-resolution head-and-neck (HN) F18-FDG PET/CT scan. The value of the latter was determined with an ROC study, the results of which showed that the WB+HN combination was better than WB alone for thyroid cancer detection and diagnosis. Following the ROC experiment, the WB and HN images of confirmed benign or malignant thyroid disease were analyzed and first and second order textural features were determined. Features included minimum, mean, and maximum intensity, as well as contrast in regions of interest encircling the thyroid lesions. Lesion size and standard uptake values (SUV) were also determined. Bivariate analysis was applied to determine relationships between WB and HN features and between observer ROC responses and the various feature values. The two sets showed significant associations in the values of SUV, contrast, and lesion size. They were completely different when the intensities were considered; no relationship was found between the WB minimum, maximum, and mean ROI values and their HN counterparts. SUV and contrast were the strongest predictors of ROC performance on PET/CT examinations of thyroid cancer. The high resolution HN images seem to enhance these relationships but without a single dramatic effect as was projected from the ROC results. A combination of features from both WB and HN datasets may possibly be a more robust predictor of ROC performance.

Laser confocal measurement system for curvature radius of lenses based on grating ruler

NASA Astrophysics Data System (ADS)

Tian, Jiwei; Wang, Yun; Zhou, Nan; Zhao, Weirui; Zhao, Weiqian

2015-02-01

In the modern optical measurement field, the radius of curvature (ROC) is one of the fundamental parameters of optical lens. Its measurement accuracy directly affects the other optical parameters, such as focal length, aberration and so on, which significantly affect the overall performance of the optical system. To meet the demand of measurement instruments for radius of curvature (ROC) with high accuracy in the market, we develop a laser confocal radius measurement system with grating ruler. The system uses the peak point of the confocal intensity curve to precisely identify the cat-eye and confocal positions and then measure the distance between these two positions by using the grating ruler, thereby achieving the high-precision measurement for the ROC. The system has advantages of high focusing sensitivity and anti-environment disturbance ability. And the preliminary theoretical analysis and experiments show that the measuring repeatability can be up to 0.8 um, which can provide an effective way for the accurate measurement of ROC.

Exploring the clonal evolution of CD133/aldehyde-dehydrogenase-1 (ALDH1)-positive cancer stem-like cells from primary to recurrent high-grade serous ovarian cancer (HGSOC). A study of the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) Consortium.

PubMed

Ruscito, Ilary; Cacsire Castillo-Tong, Dan; Vergote, Ignace; Ignat, Iulia; Stanske, Mandy; Vanderstichele, Adriaan; Ganapathi, Ram N; Glajzer, Jacek; Kulbe, Hagen; Trillsch, Fabian; Mustea, Alexander; Kreuzinger, Caroline; Benedetti Panici, Pierluigi; Gourley, Charlie; Gabra, Hani; Kessler, Mirjana; Sehouli, Jalid; Darb-Esfahani, Silvia; Braicu, Elena Ioana

2017-07-01

High-grade serous ovarian cancer (HGSOC) causes 80% of all ovarian cancer (OC) deaths. In this setting, the role of cancer stem-like cells (CSCs) is still unclear. In particular, the evolution of CSC biomarkers from primary (pOC) to recurrent (rOC) HGSOCs is unknown. Aim of this study was to investigate changes in CD133 and aldehyde dehydrogenase-1 (ALDH1) CSC biomarker expression in pOC and rOC HGSOCs. Two-hundred and twenty-four pOC and rOC intrapatient paired tissue samples derived from 112 HGSOC patients were evaluated for CD133 and ALDH1 expression using immunohistochemistry (IHC); pOCs and rOCs were compared for CD133 and/or ALDH1 levels. Expression profiles were also correlated with patients' clinicopathological and survival data. Some 49.1% of the patient population (55/112) and 37.5% (42/112) pOCs were CD133+ and ALDH1+ respectively. CD133+ and ALDH1+ samples were detected in 33.9% (38/112) and 36.6% (41/112) rOCs. CD133/ALDH1 coexpression was observed in 23.2% (26/112) and 15.2% (17/112) of pOCs and rOCs respectively. Pairwise analysis showed a significant shift of CD133 staining from higher (pOCs) to lower expression levels (rOCs) (p < 0.0001). Furthermore, all CD133 + pOC patients were International Federation of Gynaecology and Obstetrics (FIGO)-stage III/IV (p < 0.0001) and had significantly worse progression-free interval (PFI) (p = 0.04) and overall survival (OS) (p = 0.02). On multivariate analysis, CD133/ALDH1 coexpression in pOCs was identified as independent prognostic factor for PFI (HR: 1.64; 95% CI: 1.03-2.60; p = 0.036) and OS (HR: 1.71; 95% CI: 1.01-2.88; p = 0.045). Analysis on 52 pts patients with known somatic BRCA status revealed that BRCA mutations did not influence CSC biomarker expression. The study showed that CD133/ALDH1 expression impacts HGSOC patients' survival and first suggests that CSCs might undergo phenotypic change during the disease course similarly to non stem-like cancer cells, providing also a first evidence that there is no correlation between CSCs and BRCA status. Copyright © 2017 Elsevier Ltd. All rights reserved.

Data Mining Applied to Analysis of Contraceptive Methods Among College Students.

PubMed

Simões, Priscyla Waleska; Cesconetto, Samuel; Dalló, Eduardo Daminelli; de Souza Pires, Maria Marlene; Comunello, Eros; Borges Tomaz, Felipe; Xavier, Eduardo Pícolo; da Rosa Brunel Alves, Pedro Antonio; Ceretta, Luciane Bisognin; Manenti, Sandra Aparecida

2017-01-01

The aim of this study was to use the Data Mining to analyze the profile of the use of contraceptive methods in a university population. We used a database about sexuality performed on a university population in southern Brazil. The results obtained by the generated rules are largely in line with the literature and epidemiology worldwide, showing significant points of vulnerability in the university population. Validation measures of the study, as such, accuracy, sensitivity, specificity, and area under the ROC curve were higher or at least similar as compared to recent studies using the same methodology.

Area and volume ratios for prediction of visual outcome in idiopathic macular hole.

PubMed

Geng, Xing-Yun; Wu, Hui-Qun; Jiang, Jie-Hui; Jiang, Kui; Zhu, Jun; Xu, Yi; Dong, Jian-Cheng; Yan, Zhuang-Zhi

2017-01-01

To predict the visual outcome in patients undergoing macular hole surgery by two novel three-dimensional morphological parameters on optical coherence tomography (OCT): area ratio factor (ARF) and volume ratio factor (VRF). A clinical case series was conducted, including 54 eyes of 54 patients with an idiopathic macular hole (IMH). Each patient had an OCT examination before and after surgery. Morphological parameters of the macular hole, such as minimum diameter, base diameter, and height were measured. Then, the macular hole index (MHI), tractional hole index (THI), and hole form factor (HFF) were calculated. Meanwhile, novel postoperative macular hole (MH) factors, ARF and VRF were calculated by three-dimensional morphology. Bivariate correlations were performed to acquire asymptotic significance values between the steady best corrected visual acuity (BCVA) after surgery and 2D/3D arguments of MH by the Pearson method with two-tailed test. All significant factors were analyzed by the receiver operating characteristic (ROC) curve analysis of SPSS software which were responsible for vision recovery. ROC curves analyses were performed to further discuss the different parameters on the prediction of visual outcome. The mean and standard deviation values of patients' age, symptoms duration, and follow-up time were 64.8±8.9y (range: 28-81), 18.6±11.5d (range: 2-60), and 11.4±0.4mo (range: 6-24), respectively. Steady-post-BCVA analyzed with bivariate correlations was found to be significantly correlated with base diameter ( r =0.521, P <0.001), minimum diameter ( r =0.514, P <0.001), MHI ( r =-0.531, P <0.001), THI ( r =-0.386, P =0.004), HFF ( r =-0.508, P <0.001), and ARF ( r =-0.532, P <0.001). Other characteristic parameters such as age, duration of surgery, height, diameter hole index, and VRF were not statistically significant with steady-post-BCVA. According to area under the curve (AUC) values, values of ARF, MHI, HFF, minimum diameter, THI, and base diameter are 0.806, 0.772, 0.750, 0.705, 0.690, and 0.686, respectively. However, Steady-post-BCVA analysis with bivariate correlations for VRF was no statistical significance. Results of ROC curve analysis indicated that the MHI value, HFF, and ARF was greater than 0.427, 1.027 and 1.558 respectively which could correlate with better visual acuity. Compared with MHI and HFF, ARF could effectively express three-dimensional characteristics of macular hole and achieve better sensitivity and specificity. Thus, ARF could be the most effective parameter to predict the visual outcome in macular hole surgery.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity

PubMed Central

Wittenberg, Leah A.; Jonsson, Nina J.; Chan, RV Paul; Chiang, Michael F.

2014-01-01

Presence of plus disease in retinopathy of prematurity (ROP) is an important criterion for identifying treatment-requiring ROP. Plus disease is defined by a standard published photograph selected over 20 years ago by expert consensus. However, diagnosis of plus disease has been shown to be subjective and qualitative. Computer-based image analysis, using quantitative methods, has potential to improve the objectivity of plus disease diagnosis. The objective was to review the published literature involving computer-based image analysis for ROP diagnosis. The PubMed and Cochrane library databases were searched for the keywords “retinopathy of prematurity” AND “image analysis” AND/OR “plus disease.” Reference lists of retrieved articles were searched to identify additional relevant studies. All relevant English-language studies were reviewed. There are four main computer-based systems, ROPtool (AU ROC curve, plus tortuosity 0.95, plus dilation 0.87), RISA (AU ROC curve, arteriolar TI 0.71, venular diameter 0.82), Vessel Map (AU ROC curve, arteriolar dilation 0.75, venular dilation 0.96), and CAIAR (AU ROC curve, arteriole tortuosity 0.92, venular dilation 0.91), attempting to objectively analyze vessel tortuosity and dilation in plus disease in ROP. Some of them show promise for identification of plus disease using quantitative methods. This has potential to improve the diagnosis of plus disease, and may contribute to the management of ROP using both traditional binocular indirect ophthalmoscopy and image-based telemedicine approaches. PMID:21366159

Influencing clinicians and healthcare managers: can ROC be more persuasive?

NASA Astrophysics Data System (ADS)

Taylor-Phillips, S.; Wallis, M. G.; Duncan, A.; Gale, A. G.

2010-02-01

Receiver Operating Characteristic analysis provides a reliable and cost effective performance measurement tool, without using full clinical trials. However, when ROC analysis shows that performance is statistically superior in one condition than another it is difficult to relate this result to effects in practice, or even to determine whether it is clinically significant. In this paper we present two concurrent analyses: using ROC methods alongside single threshold recall rate data, and suggest that reporting both provides complimentary data. Four mammographers read 160 difficult cases (41% malignant) twice, with and without prior mammograms. Lesion location and probability of malignancy was reported for each case and analyzed using JAFROC. Concurrently each participant chose recall or return to screen for each case. JAFROC analysis showed that the presence of prior mammograms improved performance (p<.05). Single threshold data showed a trend towards a 26% increase in the number of false positive recalls without prior mammograms (p=.056). If this trend were present throughout the NHS Breast Screening Programme then discarding prior mammograms would correspond to an increase in recall rate from 4.6% to 5.3%, and 12,414 extra women recalled annually for assessment. Whilst ROC methods account for all possible thresholds of recall and have higher power, providing a single threshold example of false positive, false negative, and recall rates when reporting results could be more influential for clinicians. This paper discusses whether this is a useful additional method of presenting data, or whether it is misleading and inaccurate.

Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study

PubMed Central

Meyer, Swanhild U.; Kaiser, Sebastian; Wagner, Carola; Thirion, Christian; Pfaffl, Michael W.

2012-01-01

Background Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. Methodology/Principal Findings We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. Conclusions/Significance We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments. PMID:22723911

The value of intratumoral heterogeneity of (18)F-FDG uptake to differentiate between primary benign and malignant musculoskeletal tumours on PET/CT.

PubMed

Nakajo, Masatoyo; Nakajo, Masayuki; Jinguji, Megumi; Fukukura, Yoshihiko; Nakabeppu, Yoshiaki; Tani, Atsushi; Yoshiura, Takashi

2015-01-01

The cumulative standardized uptake value (SUV)-volume histogram (CSH) was reported to be a novel way to characterize heterogeneity in intratumoral tracer uptake. This study investigated the value of fluorine-18 fludeoxyglucose ((18)F-FDG) intratumoral heterogeneity in comparison with SUV to discriminate between primary benign and malignant musculoskeletal (MS) tumours. The subjects comprised 85 pathologically proven MS tumours. The area under the curve of CSH (AUC-CSH) was used as a heterogeneity index, with lower values corresponding with increased heterogeneity. As 22 tumours were indiscernible on (18)F-FDG positron emission tomography, maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and AUC-CSH were obtained in 63 positive tumours. The Mann-Whitney U test and receiver operating characteristic (ROC) analysis were used for analyses. The difference between benign (n = 35) and malignant tumours (n = 28) was significant in AUC-CSH (p = 0.004), but not in SUVmax (p = 0.168) and SUVmean (p = 0.879). The sensitivity, specificity and accuracy for diagnosing malignancy were 61%, 66% and 64% for SUVmax (optical threshold value, >6.9), 54%, 60% and 57% for SUVmean (optical threshold value, >3) and 61%, 86% and 75% for AUC-CSH (optical threshold value, ≤0.42), respectively. The area under the ROC curve was significantly higher in AUC-CSH (0.71) than SUVmax (0.60) (p = 0.018) and SUVmean (0.51) (p = 0.005). The heterogeneity index, AUC-CSH, has a higher diagnostic accuracy than SUV analysis in differentiating between primary benign and malignant MS tumours, although it is not sufficiently high enough to obviate histological analysis. AUC-CSH can assess the heterogeneity of (18)F-FDG uptake in primary benign and malignant MS tumours, with significantly greater heterogeneity associated with malignant MS tumours. AUC-CSH is more diagnostically accurate than SUV analysis in differentiating between benign and malignant MS tumours.

Diagnostic value of the plasmatic ADM level for early ectopic pregnancy.

PubMed

Yan, Qi; Lu, Qi; Tao, Yu; Wang, Yu-Dong; Zhao, Wen-Xia

2015-01-01

To analyze the plasmatic ADM level in early pregnancy and to investigate the diagnostic value of ADM in early ectopic pregnancy (EP). 70 patients with EP who had menopause for 5~8 weeks were included as study group, while 155 women with normal intrauterine pregnancy were also included as control group. The correlation between ADM level and menopause weeks was statistically analyzed and ROC curve was used to identify the diagnostic value of ADM. (1) In 155 cases of normal intrauterine pregnancy, the plasmatic ADM level was increased with menopause weeks in linear relationship, and the correlation coefficient (R) was 0.991 (P<0.05). In 70 patients with EP, no significant increase was found with menopause weeks and no linear relationship can be found between ADM level and menopause weeks in EP group. The correlation coefficient (R) was 0.744 (P>0.05). (2) The multiple of median of plasmatic ADM level in EP group of menopause for 8 weeks was obviously lower than the intrauterine control group (P<0.01). (3) ROC curve was used to analyze the cut-off value of ADM level in the diagnosis of EP, and the area under the ROC curve was 0.523 (P>0.05) regardless of menopause weeks, however, the area under the ROC curve was 0.702 (P<0.05) at 8 weeks after menopause with sensitivity of 53.50% and specificity of 85.00%. Different from normal intrauterine pregnancy, plasmatic ADM level in early EP was relatively lower and no significant increase was found with menopause weeks; further studies are still needed for plasmatic ADM level as an indicator in the early diagnosis of EP.

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.

PubMed

Golkarian, Ali; Naghibi, Seyed Amir; Kalantar, Bahareh; Pradhan, Biswajeet

2018-02-17

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity

PubMed Central

Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B.; Bringas-Vega, Maria L.; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A.

2018-01-01

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented. PMID:29379411

Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity.

PubMed

Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B; Bringas-Vega, Maria L; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A

2017-01-01

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

A comparison of confidence/credible interval methods for the area under the ROC curve for continuous diagnostic tests with small sample size.

PubMed

Feng, Dai; Cortese, Giuliana; Baumgartner, Richard

2017-12-01

The receiver operating characteristic (ROC) curve is frequently used as a measure of accuracy of continuous markers in diagnostic tests. The area under the ROC curve (AUC) is arguably the most widely used summary index for the ROC curve. Although the small sample size scenario is common in medical tests, a comprehensive study of small sample size properties of various methods for the construction of the confidence/credible interval (CI) for the AUC has been by and large missing in the literature. In this paper, we describe and compare 29 non-parametric and parametric methods for the construction of the CI for the AUC when the number of available observations is small. The methods considered include not only those that have been widely adopted, but also those that have been less frequently mentioned or, to our knowledge, never applied to the AUC context. To compare different methods, we carried out a simulation study with data generated from binormal models with equal and unequal variances and from exponential models with various parameters and with equal and unequal small sample sizes. We found that the larger the true AUC value and the smaller the sample size, the larger the discrepancy among the results of different approaches. When the model is correctly specified, the parametric approaches tend to outperform the non-parametric ones. Moreover, in the non-parametric domain, we found that a method based on the Mann-Whitney statistic is in general superior to the others. We further elucidate potential issues and provide possible solutions to along with general guidance on the CI construction for the AUC when the sample size is small. Finally, we illustrate the utility of different methods through real life examples.

Automated detection of prostate cancer in digitized whole-slide images of H and E-stained biopsy specimens

NASA Astrophysics Data System (ADS)

Litjens, G.; Ehteshami Bejnordi, B.; Timofeeva, N.; Swadi, G.; Kovacs, I.; Hulsbergen-van de Kaa, C.; van der Laak, J.

2015-03-01

Automated detection of prostate cancer in digitized H and E whole-slide images is an important first step for computer-driven grading. Most automated grading algorithms work on preselected image patches as they are too computationally expensive to calculate on the multi-gigapixel whole-slide images. An automated multi-resolution cancer detection system could reduce the computational workload for subsequent grading and quantification in two ways: by excluding areas of definitely normal tissue within a single specimen or by excluding entire specimens which do not contain any cancer. In this work we present a multi-resolution cancer detection algorithm geared towards the latter. The algorithm methodology is as follows: at a coarse resolution the system uses superpixels, color histograms and local binary patterns in combination with a random forest classifier to assess the likelihood of cancer. The five most suspicious superpixels are identified and at a higher resolution more computationally expensive graph and gland features are added to refine classification for these superpixels. Our methods were evaluated in a data set of 204 digitized whole-slide H and E stained images of MR-guided biopsy specimens from 163 patients. A pathologist exhaustively annotated the specimens for areas containing cancer. The performance of our system was evaluated using ten-fold cross-validation, stratified according to patient. Image-based receiver operating characteristic (ROC) analysis was subsequently performed where a specimen containing cancer was considered positive and specimens without cancer negative. We obtained an area under the ROC curve of 0.96 and a 0.4 specificity at a 1.0 sensitivity.

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

PET-CT Animal Model for Surveillance of Embedded Metal Fragments

DTIC Science & Technology

2012-12-15

area under the curve ( AUC ) were calculated. Significance level was set at p < .05. Histopathology was assessed by a pathologist, blinded to...were determined. Receiver Operating Characteristic (ROC) curve and the area under the curve ( AUC ) were calculated. Significance...False negatives 10 Principal Investigator (Shinn, Antoinette, Marie) USU Project Number: N11-C18 The area under the curve ( AUC ) was 0.938

Using social media for community consultation and public disclosure in exception from informed consent trials.

PubMed

Stephens, Shannon W; Williams, Carolyn; Gray, Randal; Kerby, Jeffrey D; Wang, Henry E; Bosarge, Patrick L

2016-06-01

The US Food and Drug Administration and the Department of Health and Human Services outline regulations allowing an exception from informed consent (EFIC) for research conducted in an emergency setting. Acute care clinical trials using EFIC must include community consultation and public disclosure (CC/PD) activities. We describe our experience using social media to facilitate the CC/PD process in two trauma resuscitation clinical trials. We conducted local CC/PD activities for two multicenter trauma clinical trials, Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) and Prehospital Tranexamic Acid Use for Traumatic Brain Injury (ROC-TXA). As part of the CC/PD process, we developed research study advertisements using the social media Web site Facebook. The Facebook advertisements directed users to a regional study Web site that contained trial information. We targeted the advertisements to specific demographic users, in specific geographic areas. We analyzed the data using descriptive statistics. During the study periods, the PROPPR Facebook advertisement was displayed 5,001,520 times (12 displays per target population) with 374 individuals selecting the advertisement. The ROC-TXA Facebook advertisement was displayed 3,806,448 times (8 per target population) with 790 individuals selecting the advertisement. Respondents to both Facebook advertisements were mostly male (52.6%), with the highest proportion between the ages 15 years and 24 years (28.2%). Collectively, 26.9% of individuals that clicked on the Facebook advertisement spent more than 3 minutes on the study Web site (3-49 minutes). Commonly accessed Web pages were "contact us" (PROPPR, 5.5%; ROC-TXA, 7.7%), "study-specific FAQs" (PROPPR, 2.4%; ROC-TXA, 6.7%), and "opt out of research" (PROPPR, 2.5%; ROC-TXA, 3.8%). Of 51 total individuals viewing the opt out of research information (PROPPR, 19; ROC-TXA, 32), time spent on that specific page was modest (PROPPR, 62 seconds; ROC-TXA, 55 seconds), with no individuals requesting to opt out of either study participation. In clinical trauma trials using EFIC, social media may provide a viable option for facilitating the CC/PD process.

Mortality prediction using TRISS methodology in the Spanish ICU Trauma Registry (RETRAUCI).

PubMed

Chico-Fernández, M; Llompart-Pou, J A; Sánchez-Casado, M; Alberdi-Odriozola, F; Guerrero-López, F; Mayor-García, M D; Egea-Guerrero, J J; Fernández-Ortega, J F; Bueno-González, A; González-Robledo, J; Servià-Goixart, L; Roldán-Ramírez, J; Ballesteros-Sanz, M Á; Tejerina-Alvarez, E; Pino-Sánchez, F I; Homar-Ramírez, J

2016-10-01

To validate Trauma and Injury Severity Score (TRISS) methodology as an auditing tool in the Spanish ICU Trauma Registry (RETRAUCI). A prospective, multicenter registry evaluation was carried out. Thirteen Spanish Intensive Care Units (ICUs). Individuals with traumatic disease and available data admitted to the participating ICUs. Predicted mortality using TRISS methodology was compared with that observed in the pilot phase of the RETRAUCI from November 2012 to January 2015. Discrimination was evaluated using receiver operating characteristic (ROC) curves and the corresponding areas under the curves (AUCs) (95% CI), with calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. A value of p<0.05 was considered significant. Predicted and observed mortality. A total of 1405 patients were analyzed. The observed mortality rate was 18% (253 patients), while the predicted mortality rate was 16.9%. The area under the ROC curve was 0.889 (95% CI: 0.867-0.911). Patients with blunt trauma (n=1305) had an area under the ROC curve of 0.887 (95% CI: 0.864-0.910), and those with penetrating trauma (n=100) presented an area under the curve of 0.919 (95% CI: 0.859-0.979). In the global sample, the HL test yielded a value of 25.38 (p=0.001): 27.35 (p<0.0001) in blunt trauma and 5.91 (p=0.658) in penetrating trauma. TRISS methodology underestimated mortality in patients with low predicted mortality and overestimated mortality in patients with high predicted mortality. TRISS methodology in the evaluation of severe trauma in Spanish ICUs showed good discrimination, with inadequate calibration - particularly in blunt trauma. Copyright © 2015 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.

Prediction of high-risk areas for visceral leishmaniasis using socioeconomic indicators and remote sensing data

PubMed Central

2014-01-01

Spatial heterogeneity in the incidence of visceral leishmaniasis (VL) is an important aspect to be considered in planning control actions for the disease. The objective of this study was to predict areas at high risk for visceral leishmaniasis (VL) based on socioeconomic indicators and remote sensing data. We applied classification and regression trees to develop and validate prediction models. Performance of the models was assessed by means of sensitivity, specificity and area under the ROC curve. The model developed was able to discriminate 15 subsets of census tracts (CT) with different probabilities of containing CT with high risk of VL occurrence. The model presented, respectively, in the validation and learning samples, sensitivity of 79% and 52%, specificity of 75% and 66%, and area under the ROC curve of 83% and 66%. Considering the complex network of factors involved in the occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VL might be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated by the unsatisfactory predictive performance of the model developed. PMID:24885128

To t-Test or Not to t-Test? A p-Values-Based Point of View in the Receiver Operating Characteristic Curve Framework.

PubMed

Vexler, Albert; Yu, Jihnhee

2018-04-13

A common statistical doctrine supported by many introductory courses and textbooks is that t-test type procedures based on normally distributed data points are anticipated to provide a standard in decision-making. In order to motivate scholars to examine this convention, we introduce a simple approach based on graphical tools of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. In this context, we propose employing a p-values-based method, taking into account the stochastic nature of p-values. We focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we extend the EPV concept to be considered in terms of the ROC curve technique. This provides expressive evaluations and visualizations of a wide spectrum of testing mechanisms' properties. We show that the conventional power characterization of tests is a partial aspect of the presented EPV/ROC technique. We desire that this explanation of the EPV/ROC approach convinces researchers of the usefulness of the EPV/ROC approach for depicting different characteristics of decision-making procedures, in light of the growing interest regarding correct p-values-based applications.

Automated analysis of heidelberg retina tomograph optic disc images by glaucoma probability score.

PubMed

Coops, Annemiek; Henson, David Barry; Kwartz, Anna J; Artes, Paul Habib

2006-12-01

To compare the diagnostic performance of the Heidelberg Retinal Tomograph's (HRT; Heidelberg Engineering GmbH, Dossenheim, Germany) glaucoma probability score (GPS), an automated, contour line-independent method of optic disc analysis with that of the Moorfields regression analysis (MRA). HRT images were obtained from one eye of 121 patients with glaucoma (median age, 70.2 years; median mean deviation [MD], -3.6 dB, range, +2.0 to -9.9 dB) and 95 healthy control subjects (median age, 59.7 years; median MD -0.1 dB, range +2.5 to -3.7). The diagnostic performances of GPS and MRA were evaluated by including borderline classifications, either as test negatives (most specific criteria) or as test positives (least specific criteria). Agreement between global and sectoral data of both analyses was established. Logistic regression analyses were performed to evaluate the effect of covariates such as optic disc size and age on the classification outcomes of both the GPS and the MRA. In 8 (7%) patients with glaucoma and 10 (11%) control subjects, the GPS failed to provide a complete global and sectoral optic disc classification. Although we could not identify a single distinct cause of this failure in the glaucoma group, failures in the control subjects occurred most often (7/10) with small and crowded optic discs. In subjects who were successfully classified at least globally by the GPS (117 patients with glaucoma, 88 control subjects), the diagnostic performances of GPS and MRA were similar (areas under the receiver operating characteristic [ROC] curve of 0.78 and 0.77, respectively; P > 0.1). With the GPS, sensitivity and specificity were 59% and 91% (most specific criteria) and 78% and 63% (least specific criteria), respectively. Combining GPS and MRA did not increase diagnostic performance significantly (ROC area of combined classifiers, 0.81). Both GPS and MRA were affected by disc size. In patients with glaucoma as well as healthy control subjects, the odds of a positive GPS classification (borderline or outside normal limits) increased by 21% (95% confidence interval [CI], 12%-30%) for each 0.1 mm2 increase in optic disc area. With the MRA, the corresponding increase was 15% (95% CI, 7%-23%). Optic disc area alone accounted for approximately 30% and 22% of the explained variance with the GPS and MRA, respectively (P < 0.001). The proportional-odds logistic regression confirmed that optic disc size affected mainly the tradeoff between true- and false-positive classifications (criterion) rather than the absolute performance of the analyses (area under the ROC curve). There was some evidence of an age effect with the MRA, which showed a 53% (95% CI, 16%-102%) increase in the odds of a positive test (borderline or outside normal limits) associated with each decade of age (P = 0.002), but no age effects were observed with the GPS (P > 0.1). The diagnostic performance of the contour line-independent GPS analysis is similar to that of the MRA. However, clinicians should be aware of the strong size dependence of both GPS and MRA. In large optic discs, both GPS and MRA are likely to produce many false-positive classifications. Correspondingly, the sensitivity to early damage is likely to be low in small optic discs. There is a need for automated classification systems that explicitly address the size dependence of current analyses.

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.

PubMed

Perandini, Simone; Soardi, Gian Alberto; Motton, Massimiliano; Rossi, Arianna; Signorini, Manuel; Montemezzi, Stefania

2016-09-01

The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models. A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines. ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC). Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

Waist circumference and insulin resistance: a cross-sectional study of Japanese men

PubMed Central

Tabata, Shinji; Yoshimitsu, Shinichiro; Hamachi, Tadamichi; Abe, Hiroshi; Ohnaka, Keizo; Kono, Suminori

2009-01-01

Background Visceral obesity is positively related to insulin resistance. The nature of the relationship between waist circumference and insulin resistance has not been known in Japanese populations. This study examined the relationship between waist circumference and insulin resistance and evaluated the optimal cutoff point for waist circumference in relation to insulin resistance in middle-aged Japanese men. Methods Study subjects included 4800 Japanese men aged 39 to 60 years. Insulin resistance was evaluated by the homeostasis model assessment of insulin resistance (HOMA-IR). The relationship of waist circumference with HOMA-IR was assessed by use of adjusted means of HOMA-IR and odds ratios of elevated HOMA-IR defined as the highest quintile (≥2.00). Receiver operating characteristics (ROC) curve analysis using Youden index and the area under curve (AUC) was employed to determine optimal cutoffs of waist circumference in relation to HOMA-IR. Results Adjusted geometric means of HOMA-IR and prevalence odds of elevated HOMA-IR were progressively higher with increasing levels of waist circumference. In the ROC curve analysis, the highest value of Youden index was obtained for a cutoff point of 85 cm in waist circumference across different values of HOMA-IR. Multiple logistic regression analysis also indicated that the AUC was consistently the largest for a waist circumference of 85 cm. Conclusion Waist circumference is linearly related to insulin resistance, and 85 cm in waist circumference is an optimal cutoff in predicting insulin resistance in middle-aged Japanese men. PMID:19138424

Effects of the ankle-brachial blood pressure index and skin perfusion pressure on mortality in hemodialysis patients.

PubMed

Otani, Yumi; Otsubo, Shigeru; Kimata, Naoki; Takano, Mari; Abe, Takayuki; Okajima, Tomoki; Miwa, Naoko; Tsuchiya, Ken; Nitta, Kosaku; Akiba, Takashi

2013-01-01

Clinically, the ankle-brachial blood pressure index (ABI) and skin perfusion pressure (SPP) are used to screen for subclinical peripheral artery disease. However, the association between the SPP and mortality in hemodialysis patients has not been previously reported. We investigated these factors and compared the ABI and SPP in patients receiving hemodialysis. A total of 102 patients receiving maintenance hemodialysis were enrolled in this study. The ABI was determined using an ABI-form (Colin, Japan). The SPP was measured using a SensiLase(TM) PAD3000 (Kaneka, Osaka, Japan). The mean follow-up period was 3.2 ± 1.4 years. A multivariate Cox analysis identified a low ABI (p=0.019) and a low SPP (p=0.047) as being independent predictors of mortality. A receiver operating characteristic (ROC) analysis of the ABI revealed a cutoff point of 1.1 and an area under the curve (AUC) of 0.79, with a sensitivity of 90% and a specificity of 62%. A ROC analysis of the SPP revealed a cutoff point of 54.0 mmHg and an AUC of 0.71, with a sensitivity of 55% and a specificity of 84%. Both low ABI and SPP values were found to be independent risk factors for mortality among hemodialysis patients. The cutoff point for ABI as a predictor of mortality was 1.1, while that for SPP was 54.0 mmHg.

Validation and factor structure of the Thai version of the EURO-D scale for depression among older psychiatric patients.

PubMed

Jirapramukpitak, Tawanchai; Darawuttimaprakorn, Niphon; Punpuing, Sureeporn; Abas, Melanie

2009-11-01

To assess the concurrent and the construct validity of the Euro-D in older Thai persons. Eight local psychiatrists used the major depressive episode section of the Mini International Neuropsychiatric Interview to interview 150 consecutive psychiatric clinic attendees. A trained interviewer administered the Euro-D. We used receiver operating characteristic (ROC) analysis to assess the overall discriminability of the Euro-D scale and principal components factor analysis to assess its construct validity. The area under the ROC curve for the Euro-D with respect to major depressive episode was 0.78 [95% confidence interval (CI) 0.70-0.90] indicating moderately good discriminability. At a cut-point of 5/6 the sensitivity for major depressive episodes is 84.3%, specificity 58.6%, and kappa 0.37 (95% CI 0.22-0.52) indicating fair concordance. However, at the 3/4 cut-point recommended from European studies there is high sensitivity (94%) but poor specificity (34%). The principal components analysis suggested four factors. The first two factors conformed to affective suffering (depression, suicidality and tearfulness) and motivation (interest, concentration and enjoyment). Sleep and appetite constituted a separate factor, whereas pessimism loaded on its own factor. Among Thai psychiatric clinic attendees Euro-D is moderately valid for major depression. A much higher cut-point may be required than that which is usually advocated. The Thai version also shares two common factors as reported from most of previous studies.

A knowledge-driven probabilistic framework for the prediction of protein-protein interaction networks.

PubMed

Browne, Fiona; Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

2010-03-01

This study applied a knowledge-driven data integration framework for the inference of protein-protein interactions (PPI). Evidence from diverse genomic features is integrated using a knowledge-driven Bayesian network (KD-BN). Receiver operating characteristic (ROC) curves may not be the optimal assessment method to evaluate a classifier's performance in PPI prediction as the majority of the area under the curve (AUC) may not represent biologically meaningful results. It may be of benefit to interpret the AUC of a partial ROC curve whereby biologically interesting results are represented. Therefore, the novel application of the assessment method referred to as the partial ROC has been employed in this study to assess predictive performance of PPI predictions along with calculating the True positive/false positive rate and true positive/positive rate. By incorporating domain knowledge into the construction of the KD-BN, we demonstrate improvement in predictive performance compared with previous studies based upon the Naive Bayesian approach. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

PubMed Central

Neves, Marco A. C.; Totrov, Maxim; Abagyan, Ruben

2012-01-01

Flexible docking and scoring using the Internal Coordinate Mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91% and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC= 82.2 and ROC(2%)= 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target. PMID:22569591

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

PubMed

Wang, Jing; Wu, Chen-Jiang; Bao, Mei-Ling; Zhang, Jing; Wang, Xiao-Ning; Zhang, Yu-Dong

2017-10-01

To investigate whether machine learning-based analysis of MR radiomics can help improve the performance PI-RADS v2 in clinically relevant prostate cancer (PCa). This IRB-approved study included 54 patients with PCa undergoing multi-parametric (mp) MRI before prostatectomy. Imaging analysis was performed on 54 tumours, 47 normal peripheral (PZ) and 48 normal transitional (TZ) zone based on histological-radiological correlation. Mp-MRI was scored via PI-RADS, and quantified by measuring radiomic features. Predictive model was developed using a novel support vector machine trained with: (i) radiomics, (ii) PI-RADS scores, (iii) radiomics and PI-RADS scores. Paired comparison was made via ROC analysis. For PCa versus normal TZ, the model trained with radiomics had a significantly higher area under the ROC curve (Az) (0.955 [95% CI 0.923-0.976]) than PI-RADS (Az: 0.878 [0.834-0.914], p < 0.001). The Az between them was insignificant for PCa versus PZ (0.972 [0.945-0.988] vs. 0.940 [0.905-0.965], p = 0.097). When radiomics was added, performance of PI-RADS was significantly improved for PCa versus PZ (Az: 0.983 [0.960-0.995]) and PCa versus TZ (Az: 0.968 [0.940-0.985]). Machine learning analysis of MR radiomics can help improve the performance of PI-RADS in clinically relevant PCa. • Machine-based analysis of MR radiomics outperformed in TZ cancer against PI-RADS. • Adding MR radiomics significantly improved the performance of PI-RADS. • DKI-derived Dapp and Kapp were two strong markers for the diagnosis of PCa.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry.

PubMed

Silva, Fabrício R; Vidotti, Vanessa G; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S; Costa, Vital P

2013-01-01

To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.

Hair analysis for detection of triptans occasionally used or overused by migraine patients-a pilot study.

PubMed

Ferrari, Anna; Baraldi, Carlo; Licata, Manuela; Vandelli, Daniele; Marchesi, Filippo; Palazzoli, Federica; Verri, Patrizia; Rustichelli, Cecilia; Giuliani, Enrico; Silingardi, Enrico

2016-09-01

The aim of this study is to evaluate the detection rate of almotriptan, eletriptan, frovatriptan, sumatriptan, rizatriptan, and zolmitriptan in the hair of migraineurs taking these drugs; the degree of agreement between type of self-reported triptan and triptan found in hair; if the concentrations in hair were related to the reported cumulative doses of triptans; and whether hair analysis was able to distinguish occasional use from the overuse of these drugs. Out of 300 headache patients consecutively enrolled, we included 147 migraine patients who reported to have taken at least one dose of one triptan in the previous 3 months; 51 % of the patients overused triptans. A detailed pharmacological history and a sample of hair were collected for each patient. Hair samples were analyzed by liquid chromatography-electrospray tandem mass spectrometry (LC-MS/MS) by a method that we developed. All the triptans could be detected in the hair of the patients. The agreement between type of self-reported triptan and type of triptan found in hair was from fair to good for frovatriptan and zolmitriptan and excellent for almotriptan, eletriptan, sumatriptan, and rizatriptan (P < 0.01, Cohen's kappa). The correlation between the reported quantities of triptan and hair concentrations was statistically significant for almotriptan, eletriptan, rizatriptan, and sumatriptan (P < 0.01, Spearman's rank correlation coefficient). The accuracy of hair analysis in distinguishing occasionally users from overusers was high for almotriptan (ROC AUC = 0.9092), eletriptan (ROC AUC = 0.8721), rizatriptan (ROC AUC = 0.9724), and sumatriptan (ROC AUC = 0.9583). Hair analysis can be a valuable system to discriminate occasional use from triptan overuse.

Measurements of diagnostic examination performance and correlation analysis using microvascular leakage, cerebral blood volume, and blood flow derived from 3T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in glial tumor grading.

PubMed

Server, Andrés; Graff, Bjørn A; Orheim, Tone E Døli; Schellhorn, Till; Josefsen, Roger; Gadmar, Øystein B; Nakstad, Per H

2011-06-01

To assess the diagnostic accuracy of microvascular leakage (MVL), cerebral blood volume (CBV) and blood flow (CBF) values derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging (DSC-MR imaging) for grading of cerebral glial tumors, and to estimate the correlation between vascular permeability/perfusion parameters and tumor grades. A prospective study of 79 patients with cerebral glial tumors underwent DSC-MR imaging. Normalized relative CBV (rCBV) and relative CBF (rCBF) from tumoral (rCBVt and rCBFt), peri-enhancing region (rCBVe and rCBFe), and the value in the tumor divided by the value in the peri-enhancing region (rCBVt/e and rCBFt/e), as well as MVL, expressed as the leakage coefficient K(2) were calculated. Hemodynamic variables and tumor grades were analyzed statistically and with Pearson correlations. Receiver operating characteristic (ROC) curve analyses were also performed for each of the variables. The differences in rCBVt and the maximum MVL (MVL(max)) values were statistically significant among all tumor grades. Correlation analysis using Pearson was as follows: rCBVt and tumor grade, r = 0.774; rCBFt and tumor grade, r = 0.417; MVL(max) and tumor grade, r = 0.559; MVL(max) and rCBVt, r = 0.440; MVL(max) and rCBFt, r = 0.192; and rCBVt and rCBFt, r = 0.605. According to ROC analyses for distinguishing tumor grade, rCBVt showed the largest areas under ROC curve (AUC), except for grade III from IV. Both rCBVt and MVL(max) showed good discriminative power in distinguishing all tumor grades. rCBVt correlated strongly with tumor grade; the correlation between MVL(max) and tumor grade was moderate.

14-3-3η Autoantibodies: Diagnostic Use in Early Rheumatoid Arthritis.

PubMed

Maksymowych, Walter P; Boire, Gilles; van Schaardenburg, Dirkjan; Wichuk, Stephanie; Turk, Samina; Boers, Maarten; Siminovitch, Katherine A; Bykerk, Vivian; Keystone, Ed; Tak, Paul Peter; van Kuijk, Arno W; Landewé, Robert; van der Heijde, Desiree; Murphy, Mairead; Marotta, Anthony

2015-09-01

To describe the expression and diagnostic use of 14-3-3η autoantibodies in early rheumatoid arthritis (RA). 14-3-3η autoantibody levels were measured using an electrochemiluminescent multiplexed assay in 500 subjects (114 disease-modifying antirheumatic drug-naive patients with early RA, 135 with established RA, 55 healthy, 70 autoimmune, and 126 other non-RA arthropathy controls). 14-3-3η protein levels were determined in an earlier analysis. Two-tailed Student t tests and Mann-Whitney U tests compared differences among groups. Receiver-operator characteristic (ROC) curves were generated and diagnostic performance was estimated by area under the curve (AUC), as well as specificity, sensitivity, and likelihood ratios (LR) for optimal cutoffs. Median serum 14-3-3η autoantibody concentrations were significantly higher (p < 0.0001) in patients with early RA (525 U/ml) when compared with healthy controls (235 U/ml), disease controls (274 U/ml), autoimmune disease controls (274 U/ml), patients with osteoarthritis (259 U/ml), and all controls (265 U/ml). ROC curve analysis comparing early RA with healthy controls demonstrated a significant (p < 0.0001) AUC of 0.90 (95% CI 0.85-0.95). At an optimal cutoff of ≥ 380 U/ml, the ROC curve yielded a sensitivity of 73%, a specificity of 91%, and a positive LR of 8.0. Adding 14-3-3η autoantibodies to 14-3-3η protein positivity enhanced the identification of patients with early RA from 59% to 90%; addition of 14-3-3η autoantibodies to anticitrullinated protein antibodies (ACPA) and/or rheumatoid factor (RF) increased identification from 72% to 92%. Seventy-two percent of RF- and ACPA-seronegative patients were positive for 14-3-3η autoantibodies. 14-3-3η autoantibodies, alone and in combination with the 14-3-3η protein, RF, and/or ACPA identified most patients with early RA.

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score.

PubMed

Cuppen, Bvj; Fritsch-Stork, Rde; Eekhout, I; de Jager, W; Marijnissen, A C; Bijlsma, Jwj; Custers, M; van Laar, J M; Lafeber, Fpjg; Welsing, Pmj

2018-01-01

In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value. In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI). For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11). No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

Hyperglycemic clamp and oral glucose tolerance test for 3-year prediction of clinical onset in persistently autoantibody-positive offspring and siblings of type 1 diabetic patients.

PubMed

Balti, Eric V; Vandemeulebroucke, Evy; Weets, Ilse; Van De Velde, Ursule; Van Dalem, Annelien; Demeester, Simke; Verhaeghen, Katrijn; Gillard, Pieter; De Block, Christophe; Ruige, Johannes; Keymeulen, Bart; Pipeleers, Daniel G; Decochez, Katelijn; Gorus, Frans K

2015-02-01

In preparation of future prevention trials, we aimed to identify predictors of 3-year diabetes onset among oral glucose tolerance test (OGTT)- and hyperglycemic clamp-derived metabolic markers in persistently islet autoantibody positive (autoAb(+)) offspring and siblings of patients with type 1 diabetes (T1D). The design is a registry-based study. Functional tests were performed in a hospital setting. Persistently autoAb(+) first-degree relatives of patients with T1D (n = 81; age 5-39 years). We assessed 3-year predictive ability of OGTT- and clamp-derived markers using receiver operating characteristics (ROC) and Cox regression analysis. Area under the curve of clamp-derived first-phase C-peptide release (AUC(5-10 min); min 5-10) was determined in all relatives and second-phase release (AUC(120-150 min); min 120-150) in those aged 12-39 years (n = 62). Overall, the predictive ability of AUC(5-10 min) was better than that of peak C-peptide, the best predictor among OGTT-derived parameters (ROC-AUC [95%CI]: 0.89 [0.80-0.98] vs 0.81 [0.70-0.93]). Fasting blood glucose (FBG) and AUC(5-10 min) provided the best combination of markers for prediction of diabetes within 3 years; (ROC-AUC [95%CI]: 0.92 [0.84-1.00]). In multivariate Cox regression analysis, AUC(5-10 min)) (P = .001) was the strongest independent predictor and interacted significantly with all tested OGTT-derived parameters. AUC(5-10 min) below percentile 10 of controls was associated with 50-70% progression to T1D regardless of age. Similar results were obtained for AUC(120-150 min). Clamp-derived first-phase C-peptide release can be used as an efficient and simple screening strategy in persistently autoAb(+) offspring and siblings of T1D patients to predict impending diabetes.

Identification of benign and malignant thyroid nodules by in vivo iodine concentration measurement using single-source dual energy CT

PubMed Central

Gao, Shun-Yu; Zhang, Xiao-Yan; Wei, Wei; Li, Xiao-Ting; Li, Yan-Ling; Xu, Min; Sun, Ying-Shi; Zhang, Xiao-Peng

2016-01-01

Abstract This study proposed to determine whether in vivo iodine concentration measurement by single-source dual energy (SSDE) CT can improve differentiation between benign and malignant thyroid nodules. In total, 53 patients presenting with thyroid nodules underwent SSDE CT scanning. Iodine concentrations were measured for each nodule and normal thyroid tissue using the GSI-viewer image analysis software. A total of 26 thyroid nodules were malignant in 26 patients and confirmed by surgery; 33 nodules from 27 patients were benign, with 10 confirmed by surgery and others after follow-up. Iodine concentrations with plain CT were significantly lower in malignant than benign nodules (0.47 ± 0.20 vs 1.17 ± 0.38 mg/mL, P = 0.00). Receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.93; with a cutoff of 0.67, iodine concentration showed 92.3% sensitivity and 88.5% specificity in diagnosing malignancy. Iodine concentration obtained by enhanced and plain CT were significantly higher in malignant than benign nodules (9.05 ± 3.35 vs 3.46 ± 2.24 mg/mL, P = 0.00). ROC curve analysis showed an AUC of 0.93; with a cutoff value of 3.37, iodine concentration displayed 78% sensitivity, 95% specificity in diagnosing malignancy. Combining unenhanced with enhanced iodine concentrations, the diagnostic equation was: Y = –8.641 × unenhanced iodine concentration + 0.663 × iodine concentration. ROC curve showed an AUC of 0.98 (95% CI, 0.94, 1.00). With Y ≥ –2 considered malignancy, diagnostic sensitivity and specificity were 96%, 96.3%, respectively. This study concluded that SSDE CT can detect the differences in iodine uptake and blood supply between benign and malignant thyroid lesions. PMID:27684811

Predictive value of modeled AUC(AFP-hCG), a dynamic kinetic parameter characterizing serum tumor marker decline in patients with nonseminomatous germ cell tumor.

PubMed

You, Benoit; Fronton, Ludivine; Boyle, Helen; Droz, Jean-Pierre; Girard, Pascal; Tranchand, Brigitte; Ribba, Benjamin; Tod, Michel; Chabaud, Sylvie; Coquelin, Henri; Fléchon, Aude

2010-08-01

The early decline profile of alpha-fetoprotein (AFP) and human chorionic gonadotropin (hCG) in patients with nonseminomatous germ cell tumors (NSGCT) treated with chemotherapy may be related to the risk of relapse. We assessed the predictive values of areas under the curve of hCG (AUC(hCG)) and AFP (AUC(AFP)) of modeled concentration-time equations on progression-free survival (PFS). Single-center retrospective analysis of hCG and AFP time-points from 65 patients with IGCCCG intermediate-poor risk NSGCT treated with 4 cycles of bleomycin-etoposide-cisplatin (BEP). To determine AUC(hCG) and AUC(AFP) for D0-D42, AUCs for D0-D7 were calculated using the trapezoid rule and AUCs for D7-D42 were calculated using the mathematic integrals of equations modeled with NONMEM. Combining AUC(AFP) and AUC(hCG) enabled us to define 2 predictive groups: namely, patients with favorable and unfavorable AUC(AFP-hCG). Survival analyses and ROC curves assessed the predictive values of AUC(AFP-hCG) groups regarding progression-free survival (PFS) and compared them with those of half-life (HL) and time-to-normalization (TTN). Mono-exponential models best fit the patterns of marker decreases. Patients with a favorable AUC(AFP-hCG) had a significantly better PFS (100% vs 71.5%, P = .014). ROC curves confirmed the encouraging predictive accuracy of AUC(AFP-hCG) against HL or TTN regarding progression risk (ROC AUCs = 79.6 vs 71.9 and 70.2 respectively). Because of the large number of patients with missing data, multivariate analysis could not be performed. AUC(AFP-hCG) is a dynamic parameter characterizing tumor marker decline in patients with NSGCT during BEP treatment. Its value as a promising predictive factor should be validated. Copyright 2010 Elsevier Inc. All rights reserved.

SU-F-R-14: PET Based Radiomics to Predict Outcomes in Patients with Hodgkin Lymphoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, J; Aristophanous, M; Akhtari, M

Purpose: To identify PET-based radiomics features associated with high refractory/relapsed disease risk for Hodgkin lymphoma patients. Methods: A total of 251 Hodgkin lymphoma patients including 19 primary refractory and 9 relapsed patients were investigated. All patients underwent an initial pre-treatment diagnostic FDG PET/CT scan. All cancerous lymph node regions (ROIs) were delineated by an experienced physician based on thresholding each volume of disease in the anatomical regions to SUV>2.5. We extracted 122 image features and evaluated the effect of ROI selection (the largest ROI, the ROI with highest mean SUV, merged ROI, and a single anatomic region [e.g. mediastinum]) onmore » classification accuracy. Random forest was used as a classifier and ROC analysis was used to assess the relationship between selected features and patient’s outcome status. Results: Each patient had between 1 and 9 separate ROIs, with much intra-patient variability in PET features. The best model, which used features from a single anatomic region (the mediastinal ROI, only volumes>5cc: 169 patients with 12 primary refractory) had a classification accuracy of 80.5% for primary refractory disease. The top five features, based on Gini index, consist of shape features (max 3D-diameter and volume) and texture features (correlation and information measure of correlation1&2). In the ROC analysis, sensitivity and specificity of the best model were 0.92 and 0.80, respectively. The area under the ROC (AUC) and the accuracy were 0.86 and 0.86, respectively. The classification accuracy was less than 60% for other ROI models or when ROIs less than 5cc were included. Conclusion: This study showed that PET-based radiomics features from the mediastinal lymph region are associated with primary refractory disease and therefore may play an important role in predicting outcomes in Hodgkin lymphoma patients. These features could be additive beyond baseline tumor and clinical characteristics, and may warrant more aggressive treatment.« less

Management of benign papilloma without atypia diagnosed at ultrasound-guided core needle biopsy: Scoring system for predicting malignancy.

PubMed

Ahn, Soo Kyung; Han, Wonshik; Moon, Hyeong-Gon; Kim, Min Kyoon; Noh, Dong-Young; Jung, Bong-Wha; Kim, Sung-Won; Ko, Eunyoung

2018-01-01

The management of benign intraductal papilloma diagnosed on core needle biopsy (CNB) remains unclear. This study was designed to evaluate factors predicting malignancy in patients diagnosed with benign papilloma without atypia at ultrasound-guided CNB and to develop a scoring system predicting malignancy based on clinical, radiological and pathological factors on further excisional biopsy. The study enrolled patients diagnosed with benign papillomas (including benign and atypical papillary lesions) at CNB. Multivariate analysis was used to identify relevant clinical, radiological and pathological factors that may predict malignancy. A total of 520 CNBs were diagnosed with benign or atypical papilloma. Of these, 452 were benign papilloma without atypia. Of the 250 lesions subsequently excised surgically from 234 women, 17 (6.8%) were diagnosed with malignancy. Multivariate analysis revealed that bloody nipple discharge, size on imaging ≥15 mm, BI-RADS≥4b, peripheral location and palpability were independent predictors of malignancy. A scoring system was developed based on logistic regression models and beta coefficients for each variable. The area under the ROC curve was 0.947 (95% CI: 0.913-0.981, p < 0.001) and a negative predictive value was 100%. In a validation set of 62 patients, an area under the ROC curve was 0.926 (95% CI: 0.857-0.995, p < 0.001). A scoring system predicting malignancy in patients diagnosed by CNB with benign papilloma without atypia was developed. This system was able to identify a subset of patients with lesions likely to be benign, indicating that imaging follow-up rather than surgical excision may be appropriate. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

[Predictive factors of functional decline at hospital discharge in elderly patients hospitalised due to acute illness].

PubMed

Condorhuamán-Alvarado, Patricia Ysabel; Menéndez-Colino, Rocío; Mauleón-Ladrero, Coro; Díez-Sebastián, Jesús; Alarcón, Teresa; González-Montalvo, Juan Ignacio

To compare baseline characteristics and those found during hospitalisation as predictors of functional decline at discharge (FDd) in elderly patients hospitalised due to acute illness. A review was made of the computerized records of patients admitted to a Geriatric Acute Unit of a tertiary hospital over a 10 year period. A record was made of demographic, clinical, functional and health-care variables. Functional decline at discharge (FDd) was defined by the difference between the previous Barthel Index (pBI) and the discharge Barthel Index (dBI). The percentage of FDd (%FDd=(pBI-dBI/pBI)×100) was calculated. The variables associated with greater %FDd in the bivariate analysis were included in multivariate logistic regression models. The predictive capacity of each model was assessed using the area under the ROC curve. The factors associated with greater %FDd were advanced age, female gender, to live in a nursing home, cognitive impairment, better baseline functional status and worse functional status at admission, number of diagnoses, and prolonged stay. The area under the ROC curve for the predictive models of %FDd was 0.638 (95% CI: 0.615-0.662) based on the previous situation, 0.756 (95% CI: 0.736-0.776) based on the situation during admission, and 0.952 (95% CI: 0.944-0.959) based on a combination of these factors. The overall assessment of patient characteristics, both during admission and baseline, may have greater value in prediction of FDd than analysis of factors separately in elderly patients hospitalised due to acute illness. Copyright © 2017. Publicado por Elsevier España, S.L.U.

Predicting Voice Disorder Status From Smoothed Measures of Cepstral Peak Prominence Using Praat and Analysis of Dysphonia in Speech and Voice (ADSV).

PubMed

Sauder, Cara; Bretl, Michelle; Eadie, Tanya

2017-09-01

The purposes of this study were to (1) determine and compare the diagnostic accuracy of a single acoustic measure, smoothed cepstral peak prominence (CPPS), to predict voice disorder status from connected speech samples using two software systems: Analysis of Dysphonia in Speech and Voice (ADSV) and Praat; and (2) to determine the relationship between measures of CPPS generated from these programs. This is a retrospective cross-sectional study. Measures of CPPS were obtained from connected speech recordings of 100 subjects with voice disorders and 70 nondysphonic subjects without vocal complaints using commercially available ADSV and freely downloadable Praat software programs. Logistic regression and receiver operating characteristic (ROC) analyses were used to evaluate and compare the diagnostic accuracy of CPPS measures. Relationships between CPPS measures from the programs were determined. Results showed acceptable overall accuracy rates (75% accuracy, ADSV; 82% accuracy, Praat) and area under the ROC curves (area under the curve [AUC] = 0.81, ADSV; AUC = 0.91, Praat) for predicting voice disorder status, with slight differences in sensitivity and specificity. CPPS measures derived from Praat were uniquely predictive of disorder status above and beyond CPPS measures from ADSV (χ 2 (1) = 40.71, P < 0.001). CPPS measures from both programs were significantly and highly correlated (r = 0.88, P < 0.001). A single acoustic measure of CPPS was highly predictive of voice disorder status using either program. Clinicians may consider using CPPS to complement clinical voice evaluation and screening protocols. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

[Comparison between Glascow-Blatchford, Rockall and AIMS65 scores in patients with upper gastrointestinal bleeding in a hospital in Lima, Peru].

PubMed

Espinoza-Ríos, Jorge; Aguilar Sánchez, Victor; Bravo Paredes, Eduar Alban; Pinto Valdivia, José; Huerta-Mercado Tenorio, Jorge

2016-01-01

Identify the best score that predicts each variable outcome (mortality, rebleeding and need for transfusion for more than 2 red blood cells pack) in patients with upper gastrointestinal bleeding until 30 days of the event. Material y methods: Patients included were those over 18 years, who had upper gastrointestinal bleeding between January 2014 to June 2015 in a general hospital of third level. The data was analyzed by the area under the curve ROC (Receiver Operating Characteristic). In total, there were 231 cases of upper gastrointestinal bleeding, 154 (66.7%) cases were male, the average age was 57.8 ± 20.02 years, the most common cause of bleeding was peptic ulcer: 111 (48.1%) cases, the mortality rate and rebleeding was 7.8% and 3.9% respectively. 5 patients were excluded from the analysis because they do not count with endoscopy study, the analysis was performed in 226 rest. In the evaluation of mortality, it was found an area under the curve ROC for Glasgow-Blatchford: 0.73, Rockall score: 0.86 and AIMS65 score: 0.90 (p<0.05) to predict rebleeding the Glasgow-Blatchford score: 0.73 Rockall score: 0.66 and AIMS65 score: 0.64 (p=0.41) and transfusion requirements of more than 2 globular packages the Glasgow-Blatchford score: 0.72, Rockall score: 0.67 and AIMS65 score: 0.77 (p=0.09). AIMS65 score is a good predictor of mortality and is useful in predicting the need for more than 2 transfusions of red blood cells pack compared to score Glasgow-Blatchford and Rockall score.

A metabolomics approach to the identification of biomarkers of sugar-sweetened beverage intake.

PubMed

Gibbons, Helena; McNulty, Breige A; Nugent, Anne P; Walton, Janette; Flynn, Albert; Gibney, Michael J; Brennan, Lorraine

2015-03-01

The association between sugar-sweetened beverages (SSBs) and health risks remains controversial. To clarify proposed links, reliable and accurate dietary assessment methods of food intakes are essential. The aim of this present work was to use a metabolomics approach to identify a panel of urinary biomarkers indicative of SSB consumption from a national food consumption survey and subsequently validate this panel in an acute intervention study. Heat map analysis was performed to identify correlations between ¹H nuclear magnetic resonance (NMR) spectral regions and SSB intakes in participants of the National Adult Nutrition Survey (n = 565). Metabolites were identified and receiver operating characteristic (ROC) analysis was performed to assess sensitivity and specificity of biomarkers. The panel of biomarkers was validated in an acute study (n = 10). A fasting first-void urine sample and postprandial samples (2, 4, 6 h) were collected after SSB consumption. After NMR spectroscopic profiling of the urine samples, multivariate data analysis was applied. A panel of 4 biomarkers-formate, citrulline, taurine, and isocitrate-were identified as markers of SSB intake. This panel of biomarkers had an area under the curve of 0.8 for ROC analysis and a sensitivity and specificity of 0.7 and 0.8, respectively. All 4 biomarkers were identified in the SSB sample. After acute consumption of an SSB drink, all 4 metabolites increased in the urine. The present metabolomics-based strategy proved to be successful in the identification of SSB biomarkers. Future work will ascertain how to translate this panel of markers for use in nutrition epidemiology. © 2015 American Society for Nutrition.

Clinical utility of the Calgary Depression Scale for Schizophrenia in individuals at ultra-high risk of psychosis.

PubMed

Rekhi, Gurpreet; Ng, Wai Yee; Lee, Jimmy

2018-03-01

There is a pressing need for reliable and valid rating scales to assess and measure depression in individuals at ultra-high risk (UHR) of psychosis. The aim of this study was to examine the clinical utility of the Calgary Depression Scale for Schizophrenia (CDSS) in individuals at UHR of psychosis. 167 individuals at UHR of psychosis were included as participants in this study. The Structured Clinical Interview for DSM-IV Axis I Disorders, CDSS, Beck Anxiety Inventory and Global Assessment of Functioning were administered. A receiver operating characteristic (ROC) curve analysis and factor analyses were performed. Cronbach's alpha was computed. Correlations between CDSS factor scores and other clinical variables were examined. The median CDSS total score was 5.0 (IQR 1.0-9.0). The area under ROC curve was 0.886 and Cronbach's alpha was 0.855. A score of 7 on the CDSS yielded the highest sensitivity and specificity in detecting depression in UHR individuals. Exploratory factor analysis of the CDSS yielded two factors: depression-hopelessness and self depreciation-guilt, which was confirmed by confirmatory factor analysis. Further analysis showed that the depression-hopelessness factor predicted functioning; whereas the self depreciation-guilt factor was related to the severity of the attenuated psychotic symptoms. In conclusion, the CDSS demonstrates good psychometric properties when used to evaluate depression in individuals at UHR of psychosis. Our study results also support a two-factor structure of the CDSS in UHR individuals. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Detecting mammographically occult cancer in women with dense breasts using Radon Cumulative Distribution Transform: a preliminary analysis

NASA Astrophysics Data System (ADS)

Lee, Juhun; Nishikawa, Robert M.; Rohde, Gustavo K.

2018-02-01

We propose using novel imaging biomarkers for detecting mammographically-occult (MO) cancer in women with dense breast tissue. MO cancer indicates visually occluded, or very subtle, cancer that radiologists fail to recognize as a sign of cancer. We used the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to project the difference between left and right mammograms into a space, increasing the detectability of occult cancer. We used a dataset of 617 screening full-field digital mammograms (FFDMs) of 238 women with dense breast tissue. Among 238 women, 173 were normal with 2 - 4 consecutive screening mammograms, 552 normal mammograms in total, and the remaining 65 women had an MO cancer with a negative screening mammogram. We used Principal Component Analysis (PCA) to find representative patterns in normal mammograms in the RCDT space. We projected all mammograms to the space constructed by the first 30 eigenvectors of the RCDT of normal cases. Under 10-fold crossvalidation, we conducted quantitative feature analysis to classify normal mammograms and mammograms with MO cancer. We used receiver operating characteristic (ROC) analysis to evaluate the classifier's output using the area under the ROC curve (AUC) as the figure of merit. Four eigenvectors were selected via a feature selection method. The mean and standard deviation of the AUC of the trained classifier on the test set were 0.74 and 0.08, respectively. In conclusion, we utilized imaging biomarkers to highlight differences between left and right mammograms to detect MO cancer using novel imaging transformation.

Vertical or horizontal orientation of foot radiographs does not affect image interpretation

PubMed Central

Ferran, Nicholas Antonio; Ball, Luke; Maffulli, Nicola

2012-01-01

Summary This study determined whether the orientation of dorsoplantar and oblique foot radiographs has an effect on radiograph interpretation. A test set of 50 consecutive foot radiographs were selected (25 with fractures, and 25 normal), and duplicated in the horizontal orientation. The images were randomly arranged, numbered 1 through 100, and analysed by six image interpreters. Vertical and horizontal area under the ROC curve, accuracy, sensitivity and specificity were calculated for each image interpreter. There was no significant difference in the area under the ROC curve, accuracy, sensitivity or specificity of image interpretation between images viewed in the vertical or horizontal orientation. While conventions for display of radiographs may help to improve the development of an efficient visual search strategy in trainees, and allow for standardisation of publication of radiographic images, variation from the convention in clinical practice does not appear to affect the sensitivity or specificity of image interpretation. PMID:23738310

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

Vascular density of superficial esophageal squamous cell carcinoma determined by direct observation of resected specimen using narrow band imaging with magnifying endoscopy.

PubMed

Kikuchi, D; Iizuka, T; Hoteya, S; Nomura, K; Kuribayashi, Y; Toba, T; Tanaka, M; Yamashita, S; Furuhata, T; Matsui, A; Mitani, T; Inoshita, N; Kaise, M

2017-11-01

Observation of the microvasculature using narrow band imaging (NBI) with magnifying endoscopy is useful for diagnosing superficial squamous cell carcinoma. Increased vascular density is indicative of cancer, but not many studies have reported differences between cancerous and noncancerous areas based on an objective comparison. We observed specimens of endoscopic submucosal dissection (ESD) using NBI magnification, and determined the vascular density of cancerous and noncancerous areas. A total of 25 lesions of esophageal squamous cell carcinoma that were dissected en bloc by ESD between July 2013 and December 2013 were subjected to NBI magnification. We constructed a device that holds an endoscope and precisely controls the movement along the vertical axis in order to observe submerged specimens by NBI magnification. NBI image files of both cancerous (pathologically determined invasion depth, m1/2) and surrounding noncancerous areas were created and subjected to vascular density assessment by two endoscopists who were blinded to clinical information. The invasion depth was m1/2 in 20, m3/sm1 in four and sm2 in one esophageal cancer lesion. Mean vascular density was significantly increased in cancerous areas (37.6 ± 16.3 vessels/mm2) compared with noncancerous areas (17.6 ± 10.0 vessels/mm2) (P < 0.05). The correlation coefficients between vascular density determined by two endoscopists were 0.86 and 0.81 in cancerous and noncancerous areas, respectively. Receiver operating curve (ROC) analysis revealed that the area under the curve (AUC) of vascular density was 0.895 (95% CI, 0.804-0.986). For this ROC curve, sensitivity was 78.3% and specificity was 87.0% when the cutoff value of vascular density was 26 vessels/mm2. NBI magnification confirmed significant increases in vascular density in cancerous areas compared with noncancerous areas in esophageal squamous cell carcinoma. The rates of agreement between vascular density values determined by two independent operators were high. © The Authors 2017. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Circulating basal anti-Müllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization.

PubMed

Nardo, Luciano G; Gelbaya, Tarek A; Wilkinson, Hannah; Roberts, Stephen A; Yates, Allen; Pemberton, Phil; Laing, Ian

2009-11-01

To evaluate the clinical value of basal anti-Müllerian hormone (AMH) measurements compared with other available determinants, apart from chronologic age, in the prediction of ovarian response to gonadotrophin stimulation. Prospective cohort study. Tertiary referral center for reproductive medicine and an IVF unit. Women undergoing their first cycle of controlled ovarian hyperstimulation (COH) for in vitro fertilization (IVF). Basal levels of FSH and AMH as well as antral follicle count (AFC) were measured in 165 subjects. All patients were followed prospectively and their cycle outcomes recorded. Predictive value of FSH, AMH, and AFC for extremes of ovarian response to stimulation. Out of the 165 women, 134 were defined as normal responders, 15 as poor responders, and 16 as high responders. Subjects in the poor response group were significantly older then those in the other two groups. Anti-Müllerian hormone levels and AFC were markedly raised in the high responders and decreased in the poor responders. Compared with FSH and AFC, AMH performed better in the prediction of excessive response to ovarian stimulation-AMH area under receiver operating characteristic curve (ROC(AUC)) 0.81, FSH ROC(AUC) 0.66, AFC ROC(AUC) 0.69. For poor response, AMH (ROC(AUC) 0.88) was a significantly better predictor than FSH (ROC(AUC) 0.63) but not AFC (ROC(AUC) 0.81). AMH prediction of ovarian response was independent of age and PCOS. Anti-Müllerian hormone cutoffs of >3.75 ng/mL and <1.0 ng/mL would have modest sensitivity and specificity in predicting the extremes of response. Circulating AMH has the ability to predict excessive and poor response to stimulation with exogenous gonadotrophins. Overall, this biomarker is superior to basal FSH and AFC, and has the potential to be incorporated in to work-up protocols to predict patient's ovarian response to treatment and to individualize strategies aiming at reducing the cancellation rate and the iatrogenic complications of COH.

Hsa_circ_0001649: A circular RNA and potential novel biomarker for hepatocellular carcinoma.

PubMed

Qin, Meilin; Liu, Gang; Huo, Xisong; Tao, Xuemei; Sun, Xiaomeng; Ge, Zhouhong; Yang, Juan; Fan, Jia; Liu, Lei; Qin, Wenxin

2016-01-01

It has been shown that circular RNA (circRNA) is associated with human cancers, however, few studies have been reported in hepatocellular carcinoma (HCC). To estimate clinical values of a circular RNA, Hsa_circ_0001649, in HCC. Expression level of hsa_circ_0001649 was detected in HCC and paired adjacent liver tissues by real-time quantitative reverse transcription-polymerase chain reactions (qRT-PCRs). Differences in expression level of hsa_circ_0001649 were analyzed using the paired t-test. Tests were performed between clinical information and hsa_circ_0001649 expression level by analysis of variance (ANOVA) or welch t-test and a receiver operating characteristics (ROC) curve was established to estimate the value of hsa_circ_0001649 expression as a biomarker in HCC. hsa_circ_0001649 expression was significantly downregulated in HCC tissues (p = 0.0014) based on an analysis of 89 paired samples of HCC and adjacent liver tissues and the area under the ROC curve (AUC) was 0.63. Furthermore, hsa_circ_0001649 expression was correlated with tumor size (p = 0.045) and the occurrence of tumor embolus (p = 0.017) in HCC. We first found hsa_circ_0001649 was significantly downregulated in HCC. Our findings indicate hsa_circ_0001649 might serve as a novel potential biomarker for HCC and may function in tumorigenesis and metastasis of HCC.

[Establish Assessment Model of 18 Years of Age in Chinese Han Population by Mandibular Third Molar].

PubMed

Fan, Fei; Dai, Xin-hua; Wang, Liang; Li, Yuan; Zhang, Kui; Deng, Zhen-hua

2016-02-01

To explore the value of estimating chronologic age based on the grades of mandibular third molar development. To evaluate whether mandibular third molar could be used as an indicator for estimating the age under or over 18 years. The mineralization status of mandibular third molar of 1 845 individuals aged 10 - 30 was graded and marked based on Demirjian's classification of grades reformed by Orhan. Gender difference was examined by t-test. A cubic regression model was established to analyze the correlation between third molar and chronologic age. Each grade of age cumulative distribution diagram and ROC curve was respectively performed to evaluate the relationship between third molar and the age of 18. Using Bayes discriminant analysis, an equation was established for estimating the age of 18. The inner-rater reliability was 0.903. Statistical analysis showed a moderate correlation between age and grade. Significant differences of both genders were found only in grade D and H (P < 0.05). Males at the grades from 1 to D and females at the grades from 1 to C were under 18 years old, and both males and females at grade H were over 18 years old. The area under the ROC curve was 0.797 (P < 0.05). Third molar development shows a high correlation with age, and combined with other indicators, it can be used to estimate the age of 18.

Diagnostic Validity of High-Density Barium Sulfate in Gastric Cancer Screening: Follow-up of Screenees by Record Linkage with the Osaka Cancer Registry

PubMed Central

Yamamoto, Kenyu; Yamazaki, Hideo; Kuroda, Chikazumi; Kubo, Tsugio; Oshima, Akira; Katsuda, Toshizo; Kuwano, Tadao; Takeda, Yoshihiro

2010-01-01

Background The use of high-density barium sulfate was recommended by the Japan Society of Gastroenterological Cancer Screening (JSGCS) in 2004. We evaluated the diagnostic validity of gastric cancer screening that used high-density barium sulfate. Methods The study subjects were 171 833 residents of Osaka, Japan who underwent gastric cancer screening tests at the Osaka Cancer Prevention and Detection Center during the period from 1 January 2000 through 31 December 2001. Screening was conducted using either high-density barium sulfate (n = 48 336) or moderate-density barium sulfate (n = 123 497). The subjects were followed up and their medical records were linked to those of the Osaka Cancer Registry through 31 December 2002. The results of follow-up during 1 year were defined as the gold standard, and test performance values were calculated. Results The sensitivity and specificity of the screening test using moderate-density barium sulfate were 92.3% and 91.0%, respectively, while the sensitivity and specificity of the high-density barium test were 91.8% and 91.4%, respectively. The results of area under receiver-operating-characteristic (ROC) curve analysis revealed no significant difference between the 2 screening tests. Conclusions Screening tests using high- and moderate-density barium sulfate had similar validity, as determined by sensitivity, specificity, and ROC curve analysis. PMID:20551581

Predictors of Gestational Diabetes Mellitus in Chinese Women with Polycystic Ovary Syndrome: A Cross-Sectional Study.

PubMed

Zhang, Ya-Jie; Jin, Hua; Qin, Zhen-Li; Ma, Jin-Long; Zhao, Han; Zhang, Ling; Chen, Zi-Jiang

2016-01-01

This study aims to explore the independent predictors of gestational diabetes mellitus (GDM) in Chinese women with polycystic ovary syndrome (PCOS). This cross-sectional study analyzed primigravid women with PCOS and classified them as those with and without GDM. Independent risk factors and model performance were analyzed using multivariate logistic regression and the area under the curve (AUC) of receiver operating characteristic (ROC), respectively. Maternal body mass index, waist circumference, waist-to-hip ratio (WHR), fasting glucose, insulin, sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR) before pregnancy, gestation weight gain before 24 weeks and the incidence of family history of diabetes were different in the 2 groups. Logistic regression analysis showed that pre-pregnancy WHR, SHBG, HOMA-IR and gestation weight gain before 24 weeks were the independent predictors of GDM. ROC curve analysis confirmed that gestation weight gain before 24 weeks (AUC 0.767, 95% CI 0.688-0.841), pre-pregnant WHR (AUC 0.725, 95% CI 0.649-0.802), HOMA-IR (AUC 0.711, 95% CI 0.632-0.790) and SHBG levels (AUC 0.709, 95% CI 0.625-0.793) were the strong risk factors. In Chinese women with PCOS, factors of gestation weight gain before 24 weeks, pre-pregnant WHR, HOMA-IR and SHBG levels are strongly associated with subsequent development of GDM. © 2015 S. Karger AG, Basel.

Evaluation of erythrocyte dysmorphism by light microscopy with lowering of the condenser lens: A simple and efficient method.

PubMed

Barros Silva, Gyl Eanes; Costa, Roberto Silva; Ravinal, Roberto Cuan; Saraiva e Silva, Jucélia; Dantas, Marcio; Coimbra, Terezila Machado

2010-03-01

To demonstrate that the evaluation of erythrocyte dysmorphism by light microscopy with lowering of the condenser lens (LMLC) is useful to identify patients with a haematuria of glomerular or non-glomerular origin. A comparative double-blind study between phase contrast microscopy (PCM) and LMLC is reported to evaluate the efficacy of these techniques. Urine samples of 39 patients followed up for 9 months were analyzed, and classified as glomerular and non-glomerular haematuria. The different microscopic techniques were compared using receiver-operator curve (ROC) analysis and area under curve (AUC). Reproducibility was assessed by coefficient of variation (CV). Specific cut-offs were set for each method according to their best rate of specificity and sensitivity as follows: 30% for phase contrast microscopy and 40% for standard LMLC, reaching in the first method the rate of 95% and 100% of sensitivity and specificity, respectively, and in the second method the rate of 90% and 100% of sensitivity and specificity, respectively. In ROC analysis, AUC for PCM was 0.99 and AUC for LMLC was 0.96. The CV was very similar in glomerular haematuria group for PCM (35%) and LMLC (35.3%). LMLC proved to be effective in contributing to the direction of investigation of haematuria, toward the nephrological or urological side. This method can substitute PCM when this equipment is not available.

Can the pre-operative Western Ontario and McMaster score predict patient satisfaction following total hip arthroplasty?

PubMed

Rogers, B A; Alolabi, B; Carrothers, A D; Kreder, H J; Jenkinson, R J

2015-02-01

In this study we evaluated whether pre-operative Western Ontario and McMaster Universities (WOMAC) osteoarthritis scores can predict satisfaction following total hip arthroplasty (THA). Prospective data for a cohort of patients undergoing THA from two large academic centres were collected, and pre-operative and one-year post-operative WOMAC scores and a 25-point satisfaction questionnaire were obtained for 446 patients. Satisfaction scores were dichotomised into either improvement or deterioration. Scatter plots and Spearman's rank correlation coefficient were used to describe the association between pre-operative WOMAC and one-year post-operative WOMAC scores and patient satisfaction. Satisfaction was compared using receiver operating characteristic (ROC) analysis against pre-operative, post-operative and δ WOMAC scores. We found no relationship between pre-operative WOMAC scores and one-year post-operative WOMAC or satisfaction scores, with Spearman's rank correlation coefficients of 0.16 and -0.05, respectively. The ROC analysis showed areas under the curve (AUC) of 0.54 (pre-operative WOMAC), 0.67 (post-operative WOMAC) and 0.43 (δ WOMAC), respectively, for an improvement in satisfaction. We conclude that the pre-operative WOMAC score does not predict the post-operative WOMAC score or patient satisfaction after THA, and that WOMAC scores can therefore not be used to prioritise patient care. ©2015 The British Editorial Society of Bone & Joint Surgery.

Computer-aided diagnosis with textural features for breast lesions in sonograms.

PubMed

Chen, Dar-Ren; Huang, Yu-Len; Lin, Sheng-Hsiung

2011-04-01

Computer-aided diagnosis (CAD) systems provided second beneficial support reference and enhance the diagnostic accuracy. This paper was aimed to develop and evaluate a CAD with texture analysis in the classification of breast tumors for ultrasound images. The ultrasound (US) dataset evaluated in this study composed of 1020 sonograms of region of interest (ROI) subimages from 255 patients. Two-view sonogram (longitudinal and transverse views) and four different rectangular regions were utilized to analyze each tumor. Six practical textural features from the US images were performed to classify breast tumors as benign or malignant. However, the textural features always perform as a high dimensional vector; high dimensional vector is unfavorable to differentiate breast tumors in practice. The principal component analysis (PCA) was used to reduce the dimension of textural feature vector and then the image retrieval technique was performed to differentiate between benign and malignant tumors. In the experiments, all the cases were sampled with k-fold cross-validation (k=10) to evaluate the performance with receiver operating characteristic (ROC) curve. The area (A(Z)) under the ROC curve for the proposed CAD system with the specific textural features was 0.925±0.019. The classification ability for breast tumor with textural information is satisfactory. This system differentiates benign from malignant breast tumors with a good result and is therefore clinically useful to provide a second opinion. Copyright © 2010 Elsevier Ltd. All rights reserved.

FIESTA ROC: A new finite element analysis program for solar cell simulation

NASA Technical Reports Server (NTRS)

Clark, Ralph O.

1991-01-01

The Finite Element Semiconductor Three-dimensional Analyzer by Ralph O. Clark (FIESTA ROC) is a computational tool for investigating in detail the performance of arbitrary solar cell structures. As its name indicates, it uses the finite element technique to solve the fundamental semiconductor equations in the cell. It may be used for predicting the performance (thereby dictating the design parameters) of a proposed cell or for investigating the limiting factors in an established design.

Diagnostic accuracy of presepsin (sCD14-ST) as a biomarker of infection and sepsis in the emergency department.

PubMed

de Guadiana Romualdo, Luis García; Torrella, Patricia Esteban; Acebes, Sergio Rebollo; Otón, María Dolores Albaladejo; Sánchez, Roberto Jiménez; Holgado, Ana Hernando; Santos, Enrique Jiménez; Freire, Alejandro Ortín

2017-01-01

Presepsin is a promising biomarker for the diagnosis and prognosis of sepsis. However, results reported about its value to diagnose sepsis in an emergency department (ED) are controversial, probably due to differences in the design of the studies. We have evaluated the diagnostic accuracy of presepsin for infection and sepsis, compared with procalcitonin (PCT) and C-reactive protein (CRP), in patients presenting to the emergency department (ED) with suspected infection. 223 patients with suspected infection were enrolled for the study. Blood samples were collected on admission for measurement of biomarkers. Definitive diagnosis was obtained afterwards by analysis of digital medical records. Receiver operating characteristic (ROC) curve analysis was conducted to determine the diagnostic accuracy. Infection was confirmed in 200 patients, including 130 with non-complicated infection and 70 with sepsis. Median CRP, PCT and presepsin levels were significantly higher in patients with infection and sepsis. PCT was the biomarker with the highest performance for infection (ROC AUC: 0.910); for sepsis, PCT (ROC AUC: 0.815) and presepsin (ROC AUC: 0.775) shown a similar performance. Although presepsin is a valuable biomarker for diagnosis of infection and sepsis, its diagnostic accuracy in our study does not improve that of PCT. Its introduction in clinical practice is not justified. Copyright © 2016 Elsevier B.V. All rights reserved.

Diagnostic depressive symptoms of the mixed bipolar episode.

PubMed

Cassidy, F; Ahearn, E; Murry, E; Forest, K; Carroll, B J

2000-03-01

There is not yet consensus on the best diagnostic definition of mixed bipolar episodes. Many have suggested the DSM-III-R/-IV definition is too rigid. We propose alternative criteria using data from a large patient cohort. We evaluated 237 manic in-patients using DSM-III-R criteria and the Scale for Manic States (SMS). A bimodally distributed factor of dysphoric mood has been reported from the SMS data. We used both the factor and the DSM-III-R classifications to identify candidate depressive symptoms and then developed three candidate depressive symptom sets. Using ROC analysis we determined the optimal threshold number of symptoms in each set and compared the three ROC solutions. The optimal solution was tested against the DSM-III-R classification for crossvalidation. The optimal ROC solution was a set, derived from both the DSM-III-R and the SMS, and the optimal threshold for diagnosis was two or more symptoms. Applying this set iteratively to the DSM-III-R classification produced the identical ROC solution. The prevalence of mixed episodes in the cohort was 13.9% by DSM-III-R, 20.2% by the dysphoria factor and 27.4% by the new ROC solution. A diagnostic set of six dysphoric symptoms (depressed mood, anhedonia, guilt, suicide, fatigue and anxiety), with a threshold of two symptoms, is proposed for a mixed episode. This new definition has a foundation in clinical data, in the proved diagnostic performance of the qualifying symptoms, and in ROC validation against two previous definitions that each have face validity.

Sensitivity of A1C to diagnose diabetes is decreased in high-risk older Southeast Asians.

PubMed

Khoo, Joan; Tay, Tunn-Lin; Foo, Joo-Pin; Tan, Eberta; Soh, Shui-Boon; Chen, Richard; Au, Vanessa; Jen-Min Ng, Ben; Cho, Li-Wei

2012-01-01

To determine the effect of ageing on the performance of glycosylated haemoglobin A1C (A1C) for the diagnosis of diabetes mellitus (DM) in Southeast Asians. A1C was measured in 511 subjects (mean age of 52.4 years; range 14-93) undergoing the 75-g oral glucose tolerance test (OGTT). Using receiver operating curve (ROC) analysis, the performance of A1C for the diagnosis of diabetes (using different standard criteria) was compared between 4 groups: <45 (n=156), 45-54 (n=132), 55-64 (n=122), ≥65 years (n=101). Subjects aged ≥65 years had the highest false-negative rates with fasting plasma glucose (60.8%) and A1C (35.1%), the smallest area under ROC curve (0.723, 95% CI 0.627-0.820), the lowest sensitivity (58.7%, 95% CI 50.4-65.7) and specificity (71.1%, 95% CI 57.3-82.6) of A1C 6.5%, compared to the younger age groups. OGTT is preferable for diagnosis of DM in older Southeast Asian adults. Copyright © 2012 Elsevier Inc. All rights reserved.

Diagnostic value of Tg and TgAb for metastasis following ablation in patients with differentiated thyroid carcinoma coexistent with Hashimoto thyroiditis.

PubMed

Chai, Hong; Zhu, Zhao-Jin; Chen, Ze-Quan; Yu, Yong-Li

2016-08-01

This study was designed to investigate the clinical value of serum thyroglobulin (Tg) and antithyroglobulin antibody (TgAb) measurements and the cutoff value after ablation in differentiated thyroid carcinoma (DTC) complicated by Hashimoto thyroiditis (HT) with metastasis. We measured serum Tg and TgAb levels and evaluated the disease status in 164 cases of DTC coexistent with HT in pathologically confirmed patients after surgery and post-remnant ablation during a 3-year follow-up. All Tg and TgAb levels were assessed by chemiluminescent immunoassay (IMA). Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic value of Tg and TgAb for disease metastasis. The relationship between Tg and TgAb was analyzed using the scatter diagram distribution method. We found that the cutoff values of Tg and TgAb were 1.48 µg/L and 45 kIU/L, respectively. The area under the ROC curve (AUC) of Tg and TgAb was 0.907 and 0.650, respectively. In DTC coexistent with HT patients, the optimal cutoff value correlated with metastasis in Tg and TgAb was 1.48 µg/L and 45 kIU/L, respectively.

Analysis of glottal source parameters in Parkinsonian speech.

PubMed

Hanratty, Jane; Deegan, Catherine; Walsh, Mary; Kirkpatrick, Barry

2016-08-01

Diagnosis and monitoring of Parkinson's disease has a number of challenges as there is no definitive biomarker despite the broad range of symptoms. Research is ongoing to produce objective measures that can either diagnose Parkinson's or act as an objective decision support tool. Recent research on speech based measures have demonstrated promising results. This study aims to investigate the characteristics of the glottal source signal in Parkinsonian speech. An experiment is conducted in which a selection of glottal parameters are tested for their ability to discriminate between healthy and Parkinsonian speech. Results for each glottal parameter are presented for a database of 50 healthy speakers and a database of 16 speakers with Parkinsonian speech symptoms. Receiver operating characteristic (ROC) curves were employed to analyse the results and the area under the ROC curve (AUC) values were used to quantify the performance of each glottal parameter. The results indicate that glottal parameters can be used to discriminate between healthy and Parkinsonian speech, although results varied for each parameter tested. For the task of separating healthy and Parkinsonian speech, 2 out of the 7 glottal parameters tested produced AUC values of over 0.9.

Remote CT reading using an ultramobile PC and web-based remote viewing over a wireless network.

PubMed

Choi, Hyuk Joong; Lee, Jeong Hun; Kang, Bo Seung

2012-01-01

We developed a new type of mobile teleradiology system using an ultramobile PC (UMPC) for web-based remote viewing over a wireless network. We assessed the diagnostic performance of this system for abdominal CT interpretation. Performance was compared with an emergency department clinical monitor using a DICOM viewer. A total of 100 abdominal CT examinations were presented to four observers. There were 56 examinations showing appendicitis and 44 which were normal. The observers viewed the images using a UMPC display and an LCD monitor and rated each examination on a five-point scale. Receiver operating characteristics (ROC) analysis was used to test for differences. The sensitivity and specificities of all observers were similarly high. The average area under the ROC curve for readings performed on the UMPC and the LCD monitor was 0.959 and 0.976, respectively. There were no significant differences between the two display systems for interpreting abdominal CTs. The web-based mobile teleradiology system appears to be feasible for reading abdominal CTs for diagnosing appendicitis and may be valuable in emergency teleconsultation. Copyright © 2012 by the Royal Society of Medicine Press Ltd

Multiparametric MRI Apparent Diffusion Coefficient (ADC) Accuracy in Diagnosing Clinically Significant Prostate Cancer.

PubMed

Pepe, Pietro; D'Urso, Davide; Garufi, Antonio; Priolo, Giandomenico; Pennisi, Michele; Russo, Giorgio; Sabini, Maria Gabriella; Valastro, Lucia Maria; Galia, Antonio; Fraggetta, Filippo

2017-01-01

To evaluate the accuracy of multiparametric magnetic resonance imaging apparent diffusion coefficient (mpMRI ADC) in the diagnosis of clinically significant prostate cancer (PCa). From January 2016 to December 2016, 44 patients who underwent radical prostatectomy for PCa and mpMRI lesions suggestive of cancer were retrospectively evaluated at definitive specimen. The accuracy of suspicious mpMRI prostate imaging reporting and data system (PI-RADS ≥3) vs. ADC values in the diagnosis of Gleason score ≥7 was evaluated. Receiver operating characteristics (ROC) curve analysis gave back an ADC threshold of 0.747×10 -3 mm 2 /s to separate between Gleason Score 6 and ≥7. The diagnostic accuracy of ADC value (cut-off 0.747×10 -3 mm 2 /s) vs. PI-RADS score ≥3 in diagnosing PCa with Gleason score ≥7 was equal to 84% vs. 63.6% with an area under the curve (AUC) ROC of 0.81 vs. 0.71, respectively. ADC evaluation could support clinicians in decision making of patients with PI-RADS score <3 at risk for PCa. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

PubMed Central

Wray, Naomi R.; Yang, Jian; Goddard, Michael E.; Visscher, Peter M.

2010-01-01

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator. PMID:20195508

Diagnosis of insomnia sleep disorder using short time frequency analysis of PSD approach applied on EEG signal using channel ROC-LOC.

PubMed

Siddiqui, Mohd Maroof; Srivastava, Geetika; Saeed, Syed Hasan

2016-01-01

Insomnia is a sleep disorder in which the subject encounters problems in sleeping. The aim of this study is to identify insomnia events from normal or effected person using time frequency analysis of PSD approach applied on EEG signals using channel ROC-LOC. In this research article, attributes and waveform of EEG signals of Human being are examined. The aim of this study is to draw the result in the form of signal spectral analysis of the changes in the domain of different stages of sleep. The analysis and calculation is performed in all stages of sleep of PSD of each EEG segment. Results indicate the possibility of recognizing insomnia events based on delta, theta, alpha and beta segments of EEG signals.

Receiver operating characteristic analysis of age-related changes in lineup performance.

PubMed

Humphries, Joyce E; Flowe, Heather D

2015-04-01

In the basic face memory literature, support has been found for the late maturation hypothesis, which holds that face recognition ability is not fully developed until at least adolescence. Support for the late maturation hypothesis in the criminal lineup identification literature, however, has been equivocal because of the analytic approach that has been used to examine age-related changes in identification performance. Recently, receiver operator characteristic (ROC) analysis was applied for the first time in the adult eyewitness memory literature to examine whether memory sensitivity differs across different types of lineup tests. ROC analysis allows for the separation of memory sensitivity from response bias in the analysis of recognition data. Here, we have made the first ROC-based comparison of adults' and children's (5- and 6-year-olds and 9- and 10-year-olds) memory performance on lineups by reanalyzing data from Humphries, Holliday, and Flowe (2012). In line with the late maturation hypothesis, memory sensitivity was significantly greater for adults compared with young children. Memory sensitivity for older children was similar to that for adults. The results indicate that the late maturation hypothesis can be generalized to account for age-related performance differences on an eyewitness memory task. The implications for developmental eyewitness memory research are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers.

PubMed

Liu, Song; Zhang, Yujuan; Chen, Ling; Guan, Wenxian; Guan, Yue; Ge, Yun; He, Jian; Zhou, Zhengyang

2017-10-02

Whole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers. Eighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers. There were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADC min and ADC max ) and N (except ADC max ) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC 5% , ADC 10% , ADC min ) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADC max performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC 10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677-0.911). All the parameters except ADC max showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800. Whole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

Multiresolution Local Binary Pattern texture analysis for false positive reduction in computerized detection of breast masses on mammograms

NASA Astrophysics Data System (ADS)

Choi, Jae Young; Kim, Dae Hoe; Choi, Seon Hyeong; Ro, Yong Man

2012-03-01

We investigated the feasibility of using multiresolution Local Binary Pattern (LBP) texture analysis to reduce falsepositive (FP) detection in a computerized mass detection framework. A new and novel approach for extracting LBP features is devised to differentiate masses and normal breast tissue on mammograms. In particular, to characterize the LBP texture patterns of the boundaries of masses, as well as to preserve the spatial structure pattern of the masses, two individual LBP texture patterns are then extracted from the core region and the ribbon region of pixels of the respective ROI regions, respectively. These two texture patterns are combined to produce the so-called multiresolution LBP feature of a given ROI. The proposed LBP texture analysis of the information in mass core region and its margin has clearly proven to be significant and is not sensitive to the precise location of the boundaries of masses. In this study, 89 mammograms were collected from the public MAIS database (DB). To perform a more realistic assessment of FP reduction process, the LBP texture analysis was applied directly to a total of 1,693 regions of interest (ROIs) automatically segmented by computer algorithm. Support Vector Machine (SVM) was applied for the classification of mass ROIs from ROIs containing normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classification accuracy and its improvement using multiresolution LBP features. With multiresolution LBP features, the classifier achieved an average area under the ROC curve, , z A of 0.956 during testing. In addition, the proposed LBP features outperform other state-of-the-arts features designed for false positive reduction.

Wavelets analysis for differentiating solid, non-macroscopic fat containing, enhancing renal masses: a pilot study

NASA Astrophysics Data System (ADS)

Varghese, Bino; Hwang, Darryl; Mohamed, Passant; Cen, Steven; Deng, Christopher; Chang, Michael; Duddalwar, Vinay

2017-11-01

Purpose: To evaluate potential use of wavelets analysis in discriminating benign and malignant renal masses (RM) Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 144 patients (98 malignant RM: renal cell carcinoma (RCC) and 46 benign RM: oncocytoma, lipid-poor angiomyolipoma). Here, the Haar wavelet was used to analyze the grayscale images of the largest segmented tumor in the axial direction. Six metrics (energy, entropy, homogeneity, contrast, standard deviation (SD) and variance) derived from 3-levels of image decomposition in 3 directions (horizontal, vertical and diagonal) respectively, were used to quantify tumor texture. Independent t-test or Wilcoxon rank sum test depending on data normality were used as exploratory univariate analysis. Stepwise logistic regression and receiver operator characteristics (ROC) curve analysis were used to select predictors and assess prediction accuracy, respectively. Results: Consistently, 5 out of 6 wavelet-based texture measures (except homogeneity) were higher for malignant tumors compared to benign, when accounting for individual texture direction. Homogeneity was consistently lower in malignant than benign tumors irrespective of direction. SD and variance measured in the diagonal direction on the corticomedullary phase showed significant (p<0.05) difference between benign versus malignant tumors. The multivariate model with variance (3 directions) and SD (vertical direction) extracted from the excretory and pre-contrast phase, respectively showed an area under the ROC curve (AUC) of 0.78 (p < 0.05) in discriminating malignant from benign. Conclusion: Wavelet analysis is a valuable texture evaluation tool to add to a radiomics platforms geared at reliably characterizing and stratifying renal masses.

Limited diagnostic value of Dual-Time-Point (18)F-FDG PET/CT imaging for classifying solitary pulmonary nodules in granuloma-endemic regions both at visual and quantitative analyses.

PubMed

Chen, Song; Li, Xuena; Chen, Meijie; Yin, Yafu; Li, Na; Li, Yaming

2016-10-01

This study is aimed to compare the diagnostic power of using quantitative analysis or visual analysis with single time point imaging (STPI) PET/CT and dual time point imaging (DTPI) PET/CT for the classification of solitary pulmonary nodules (SPN) lesions in granuloma-endemic regions. SPN patients who received early and delayed (18)F-FDG PET/CT at 60min and 180min post-injection were retrospectively reviewed. Diagnoses are confirmed by pathological results or follow-ups. Three quantitative metrics, early SUVmax, delayed SUVmax and retention index(the percentage changes between the early SUVmax and delayed SUVmax), were measured for each lesion. Three 5-point scale score was given by blinded interpretations performed by physicians based on STPI PET/CT images, DTPI PET/CT images and CT images, respectively. ROC analysis was performed on three quantitative metrics and three visual interpretation scores. One-hundred-forty-nine patients were retrospectively included. The areas under curve (AUC) of the ROC curves of early SUVmax, delayed SUVmax, RI, STPI PET/CT score, DTPI PET/CT score and CT score are 0.73, 0.74, 0.61, 0.77 0.75 and 0.76, respectively. There were no significant differences between the AUCs in visual interpretation of STPI PET/CT images and DTPI PET/CT images, nor in early SUVmax and delayed SUVmax. The differences of sensitivity, specificity and accuracy between STPI PET/CT and DTPI PET/CT were not significantly different in either quantitative analysis or visual interpretation. In granuloma-endemic regions, DTPI PET/CT did not offer significant improvement over STPI PET/CT in differentiating malignant SPNs in both quantitative analysis and visual interpretation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The value of "liver windows" settings in the detection of small renal cell carcinomas on unenhanced computed tomography.

PubMed

Sahi, Kamal; Jackson, Stuart; Wiebe, Edward; Armstrong, Gavin; Winters, Sean; Moore, Ronald; Low, Gavin

2014-02-01

To assess if "liver window" settings improve the conspicuity of small renal cell carcinomas (RCC). Patients were analysed from our institution's pathology-confirmed RCC database that included the following: (1) stage T1a RCCs, (2) an unenhanced computed tomography (CT) abdomen performed ≤ 6 months before histologic diagnosis, and (3) age ≥ 17 years. Patients with multiple tumours, prior nephrectomy, von Hippel-Lindau disease, and polycystic kidney disease were excluded. The unenhanced CT was analysed, and the tumour locations were confirmed by using corresponding contrast-enhanced CT or magnetic resonance imaging studies. Representative single-slice axial, coronal, and sagittal unenhanced CT images were acquired in "soft tissue windows" (width, 400 Hounsfield unit (HU); level, 40 HU) and liver windows (width, 150 HU; level, 88 HU). In addition, single-slice axial, coronal, and sagittal unenhanced CT images of nontumourous renal tissue (obtained from the same cases) were acquired in soft tissue windows and liver windows. These data sets were randomized, unpaired, and were presented independently to 3 blinded radiologists for analysis. The presence or absence of suspicious findings for tumour was scored on a 5-point confidence scale. Eighty-three of 415 patients met the study criteria. Receiver operating characteristics (ROC) analysis, t test analysis, and kappa analysis were used. ROC analysis showed statistically superior diagnostic performance for liver windows compared with soft tissue windows (area under the curve of 0.923 vs 0.879; P = .0002). Kappa statistics showed "good" vs "moderate" agreement between readers for liver windows compared with soft tissue windows. Use of liver windows settings improves the detection of small RCCs on the unenhanced CT. Copyright © 2014 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

Predicting Ovarian Activity in Women Affected by Early Breast Cancer: A Meta-Analysis-Based Nomogram.

PubMed

Barnabei, Agnese; Strigari, Lidia; Marchetti, Paolo; Sini, Valentina; De Vecchis, Liana; Corsello, Salvatore Maria; Torino, Francesco

2015-10-01

The assessment of ovarian reserve in premenopausal women requiring anticancer gonadotoxic therapy can help clinicians address some challenging issues, including the probability of future pregnancies after the end of treatment. Anti-Müllerian hormone (AMH) and age can reliably estimate ovarian reserve. A limited number of studies have evaluated AMH and age as predictors of residual ovarian reserve following cytotoxic chemotherapy in breast cancer patients. To conduct a meta-analysis of published data on this topic, we searched the medical literature using the key MeSH terms "amenorrhea/chemically induced," "ovarian reserve," "anti-Mullerian hormone/blood," and "breast neoplasms/drug therapy." Preferred Reporting Items for Systematic Reviews and Meta-Analyses statements guided the search strategy. U.K. National Health Service guidelines were used in abstracting data and assessing data quality and validity. Area under the receiver operating characteristic curve (ROC/AUC) analysis was used to evaluate the predictive utility of baseline AMH and age model. The meta-analysis of data pooled from the selected studies showed that both age and serum AMH are reliable predictors of post-treatment ovarian activity in breast cancer patients. Importantly, ROC/AUC analysis indicated AMH was a more reliable predictor of post-treatment ovarian activity in patients aged younger than 40 years (0.753; 95% confidence interval [CI]: 0.602-0.904) compared with those older than 40 years (0.678; 95% CI: 0.491-0.866). We generated a nomogram describing the correlations among age, pretreatment AMH serum levels, and ovarian activity at 1 year from the end of chemotherapy. After the ongoing validation process, the proposed nomogram may help clinicians discern premenopausal women requiring cytotoxic chemotherapy who should be considered high priority for fertility preservation counseling and procedures. ©AlphaMed Press.

[An indirect ELISA using Legionella pneumophila recombinant MOMP protein and its application in serological diagnosis].

PubMed

Wang, Tao; Zhang, Caixia; Cao, Xiuqin; Yang, Zhiwei

2013-12-01

To express and purify the recombinant major outer membrane protein (MOMP) of Legionella pneumophila (Lp) as diagnostic antigen, and explore its practical value in the serological diagnosis of Lp infection. The recombinant plasmid pET-momp was transformed into the E.coli BL21 competent cells. The recombinant MOMP was induced to express, and then analyzed by SDS-PAGE electrophoresis, purified by affinity chromatography. We screened and obtained 58 positive blood serum and 32 negative blood serum using the DRG (Germany, IgG/IgM/IgA) Lp kit. The blood serum samples were detected for IgG, IgM, IgA antibody levels by indirect ELISA that we had established with the purified MOMP as the coating antigen, as well as by R&D (USA, IgG/IgM/IgA) Lp kit. Then using the receiver operating characteristic (ROC) curve, we compared these two methods in the sensitivity, specificity and consistency of the test results, for evaluating the application value of the indirect ELISA of recombinant MOMP. The approximately 45 000 recombinant MOMP was successfully expressed and purified. Compared with the indirect ELISA we established with the R&D Lp kit for detecting Lp antibody IgG, IgM and IgA in blood serum, the sensitivity of the indirect ELISA of recombinant MOMP to IgG was 90.9%, the specificity was 91.7%, the Kappa value was 0.784 (P < 0.05), and the area under the ROC curve was 0.913; the sensitivity to IgM was 91.4% and the specificity was 90.6%, the Kappa value was 0.809 (P < 0.05), and the area under the ROC curve was 0.910; the sensitivity to IgA was 92.1% and the specificity was 88.9%, the Kappa value was 0.793(P < 0.05), and the area under the ROC curve was 0.905. The recombinant MOMP was successfully induced to express and purified. The indirect ELISA we established with the recombinant MOMP protein as a diagnostic antigen showed good specificity, sensitivity and consistency, which laid a foundation for the development of serological diagnosis kit of Legionnaires' disease.

[Application of decision curve on evaluation of MRI predictive model for early assessing pathological complete response to neoadjuvant therapy in breast cancer].

PubMed

He, Y J; Li, X T; Fan, Z Q; Li, Y L; Cao, K; Sun, Y S; Ouyang, T

2018-01-23

Objective: To construct a dynamic enhanced MR based predictive model for early assessing pathological complete response (pCR) to neoadjuvant therapy in breast cancer, and to evaluate the clinical benefit of the model by using decision curve. Methods: From December 2005 to December 2007, 170 patients with breast cancer treated with neoadjuvant therapy were identified and their MR images before neoadjuvant therapy and at the end of the first cycle of neoadjuvant therapy were collected. Logistic regression model was used to detect independent factors for predicting pCR and construct the predictive model accordingly, then receiver operating characteristic (ROC) curve and decision curve were used to evaluate the predictive model. Results: ΔArea(max) and Δslope(max) were independent predictive factors for pCR, OR =0.942 (95% CI : 0.918-0.967) and 0.961 (95% CI : 0.940-0.987), respectively. The area under ROC curve (AUC) for the constructed model was 0.886 (95% CI : 0.820-0.951). Decision curve showed that in the range of the threshold probability above 0.4, the predictive model presented increased net benefit as the threshold probability increased. Conclusions: The constructed predictive model for pCR is of potential clinical value, with an AUC>0.85. Meanwhile, decision curve analysis indicates the constructed predictive model has net benefit from 3 to 8 percent in the likely range of probability threshold from 80% to 90%.

Diagnostic value of KLK6 as an ovarian cancer biomarker: A meta-analysis.

PubMed

Yang, Fan; Hu, Zhi-DE; Chen, Yingjian; Hu, Cheng-Jin

2016-06-01

Kallikrein-related peptidase 6 (KLK6) is a new potential serum biomarker of ovarian cancer. The aim of the present study was to assess the diagnostic value of KLK6 systematically for ovarian cancer. All the selected studies regarding the changes of KLK6 in ovarian cancer were published prior to April 2015. Five studies involving 485 patients with ovarian cancer, 420 benign cysts and 245 healthy controls met the inclusion criteria. The value of sensitivity, specificity, positive-likelihood ratio (LR+), negative-likelihood ratio (LR-) and area under the receiver operating characteristic curve (ROC) were obtained. All these indices were used to evaluate the diagnostic value of KLK6 for ovarian cancer. The values of sensitivity, specificity, LR+ and LR- (95% confidence interval) of KLK6 were 0.50 (0.47-0.54), 0.91 (0.89-0.93), 7.20 (3.34-15.52) and 0.51 (0.43-0.62), respectively. The area under the summary ROC of KLK6 was 0.86. The index of Q* was 0.79. In conclusion, KLK6 showed high specificity for the diagnosis of ovarian cancer. It can improve the diagnostic accuracy of cancer antigen 125 (CA125). A combined panel of CA125 and KL K6 shows a high diagnostic efficiency for advanced ovarian cancer. Owing to the small number of studies and lack of samples, additional studies meeting the inclusion criteria are required to further analyze the diagnostic value of KLK6 for ovarian cancer.

Cold-provocation testing for the vascular component of hand-arm vibration syndrome in health surveillance.

PubMed

Poole, Kerry; Elms, Joanne; Mason, Howard

2006-10-01

The aim was to investigate whether the use of infra-red thermography (I-R) and measurement of temperature gradients along the finger could improve the diagnostic accuracy of cold-provocation testing (15 degrees C for 5 min) in vascular hand-arm vibration syndrome (HAVS). Twenty-one controls and 33 individuals with stages 2/3V HAVS were studied. The standard measurement of time to rewarm by 4 degrees C (T4 degrees C) and temperature gradients between the finger tip, base and middle (measured using I-R) were calculated. Receiver Operating Characteristics (ROC) analysis to distinguish between the two groups revealed that for T4 degrees C the area under the ROC curve was not statistically significantly different from 0.5 (0.64 95% confidence interval 0.49-0.76). The difference between the tip and middle portion of the finger during the sixth minute of recovery was the most promising gradient with an area of 0.76 (95% confidence interval 0.62-0.87), and sensitivity and specificity of 57.6% and 85.7% respectively. However, this was not significantly different from that for the time to rewarm by 4 degrees C. In conclusion, the cold-provocation test used in this study does not appear to discriminate between individuals with stage 2/3V HAVS and controls and this is not improved by the measurement of temperature gradients along the fingers using I-R.

Fibrosis in nonalcoholic fatty liver disease: Noninvasive assessment using computed tomography volumetry.

PubMed

Fujita, Nobuhiro; Nishie, Akihiro; Asayama, Yoshiki; Ishigami, Kousei; Ushijima, Yasuhiro; Takayama, Yukihisa; Okamoto, Daisuke; Shirabe, Ken; Yoshizumi, Tomoharu; Kotoh, Kazuhiro; Furusyo, Norihiro; Hida, Tomoyuki; Oda, Yoshinao; Fujioka, Taisuke; Honda, Hiroshi

2016-10-28

To evaluate the diagnostic performance of computed tomography (CT) volumetry for discriminating the fibrosis stage in patients with nonalcoholic fatty liver disease (NAFLD). A total of 38 NAFLD patients were enrolled. On the basis of CT imaging, the volumes of total, left lateral segment (LLS), left medial segment, caudate lobe, and right lobe (RL) of the liver were calculated with a dedicated liver application. The relationship between the volume percentage of each area and fibrosis stage was analyzed using Spearman's rank correlation coefficient. A receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of CT volumetry for discriminating fibrosis stage. The volume percentages of the caudate lobe and the LLS significantly increased with the fibrosis stage ( r = 0.815, P < 0.001; and r = 0.465, P = 0.003, respectively). Contrarily, the volume percentage of the RL significantly decreased with fibrosis stage ( r = -0.563, P < 0.001). The volume percentage of the caudate lobe had the best diagnostic accuracy for staging fibrosis, and the area under the ROC curve values for discriminating fibrosis stage were as follows: ≥ F1, 0.896; ≥ F2, 0.929; ≥ F3, 0.955; and ≥ F4, 0.923. The best cut-off for advanced fibrosis (F3-F4) was 4.789%, 85.7% sensitivity and 94.1% specificity. The volume percentage of the caudate lobe calculated by CT volumetry is a useful diagnostic parameter for staging fibrosis in NAFLD patients.

Mortality in severe trauma patients attended by emergency services in Navarre, Spain: validation of a new prediction model and comparison with the Revised Injury Severity Classification Score II.

PubMed

Ali Ali, Bismil; Lefering, Rolf; Fortún Moral, Mariano; Belzunegui Otano, Tomás

2018-01-01

To validate the Mortality Prediction Model of Navarre (MPMN) to predict death after severe trauma and compare it to the Revised Injury Severity Classification Score II (RISCII). Retrospective analysis of a cohort of severe trauma patients (New Injury Severity Score >15) who were attended by emergency services in the Spanish autonomous community of Navarre between 2013 and 2015. The outcome variable was 30-day all-cause mortality. Risk was calculated with the MPMN and the RISCII. The performance of each model was assessed with the area under the receiver operating characteristic (ROC) curve and precision with respect to observed mortality. Calibration was assessed with the Hosmer-Lemeshow test. We included 516 patients. The mean (SD) age was 56 (23) years, and 363 (70%) were males. Ninety patients (17.4%) died within 30 days. The 30-day mortality rates predicted by the MPMN and RISCII were 16.4% and 15.4%, respectively. The areas under the ROC curves were 0.925 (95% CI, 0.902-0.952) for the MPMN and 0.941 (95% CI, 0.921-0.962) for the RISCII (P=0.269, DeLong test). Calibration statistics were 13.6 (P=.09) for the MPMN and 8.9 (P=.35) for the RISCII. Both the MPMN and the RISCII show good ability to discriminate risk and predict 30-day all-cause mortality in severe trauma patients.

The influence of averaging and noisy decision strategies on the recognition memory ROC.

PubMed

Malmberg, Kenneth J; Xu, Jing

2006-02-01

Many single- and dual-process models of recognition memory predict that the ratings and remember-know receiver operating characteristics (ROCs) are the same, but Rotello, Macmillan, and Reeder (2004) reported that the slopes of the remember-know and ratings z-transformed ROCs (zROCs) are different The authors show that averaging introduces nonlinearities to the form of the zROC and that ratings and remember-know zROCs are indistinguishable when constructed in a conventional manner. The authors show, further, that some nonoptimal decision strategies have a distinctive, nonlinear effect on the form of the single-process continuous-state zROC. The conclusion is that many factors having nothing to do with the nature of recognition memory can affect the shape of zROCs, and that therefore, the shape of the zROC does not, alone, characterize different memory models.

Gene expression profile of blood cells for the prediction of delayed cerebral ischemia after intracranial aneurysm rupture: a pilot study in humans.

PubMed

Baumann, Antoine; Devaux, Yvan; Audibert, Gérard; Zhang, Lu; Bracard, Serge; Colnat-Coulbois, Sophie; Klein, Olivier; Zannad, Faiez; Charpentier, Claire; Longrois, Dan; Mertes, Paul-Michel

2013-01-01

Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCI patients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in the microarray experiments and the small size of the DCI population sample. Standard two-tailed paired t test and C-statistic revealed significant associations between gene expression and the occurrence of DCI: in particular, the expression of neuroregulin 1 was 1.6-fold upregulated in patients with DCI (p = 0.01) and predicted DCI with an area under the ROC curve of 0.96. Logistic regression analyses revealed a significant association between neuroregulin 1 and DCI (odds ratio 1.46, 95% confidence interval 1.02-2.09, p = 0.02). This pilot study suggests that blood cells may be a reservoir of prognostic biomarkers of DCI in patients with intracranial aneurysm rupture. Despite an evident lack of power, this study elicited neuroregulin 1, a vasoreactivity-, inflammation- and angiogenesis-related gene, as a possible candidate predictor of DCI. Larger cohort studies are needed but genome-wide microarray-based studies are promising research tools for the understanding of DCI after intracranial aneurysm rupture. © 2013 S. Karger AG, Basel.

A hybrid segmentation approach for geographic atrophy in fundus auto-fluorescence images for diagnosis of age-related macular degeneration.

PubMed

Lee, Noah; Laine, Andrew F; Smith, R Theodore

2007-01-01

Fundus auto-fluorescence (FAF) images with hypo-fluorescence indicate geographic atrophy (GA) of the retinal pigment epithelium (RPE) in age-related macular degeneration (AMD). Manual quantification of GA is time consuming and prone to inter- and intra-observer variability. Automatic quantification is important for determining disease progression and facilitating clinical diagnosis of AMD. In this paper we describe a hybrid segmentation method for GA quantification by identifying hypo-fluorescent GA regions from other interfering retinal vessel structures. First, we employ background illumination correction exploiting a non-linear adaptive smoothing operator. Then, we use the level set framework to perform segmentation of hypo-fluorescent areas. Finally, we present an energy function combining morphological scale-space analysis with a geometric model-based approach to perform segmentation refinement of false positive hypo- fluorescent areas due to interfering retinal structures. The clinically apparent areas of hypo-fluorescence were drawn by an expert grader and compared on a pixel by pixel basis to our segmentation results. The mean sensitivity and specificity of the ROC analysis were 0.89 and 0.98%.

Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery

PubMed Central

Juhász, Csaba; Shah, Aashit; Sood, Sandeep; Chugani, Harry T.

2009-01-01

Since prediction of long-term seizure outcome using preoperative diagnostic modalities remains suboptimal in epilepsy surgery, we evaluated whether interictal spike frequency measures obtained from extraoperative subdural electrocorticography (ECoG) recording could predict long-term seizure outcome. This study included 61 young patients (age 0.4–23.0 years), who underwent extraoperative ECoG recording prior to cortical resection for alleviation of uncontrolled focal seizures. Patient age, frequency of preoperative seizures, neuroimaging findings, ictal and interictal ECoG measures were preoperatively obtained. The seizure outcome was prospectively measured [follow-up period: 2.5–6.4 years (mean 4.6 years)]. Univariate and multivariate logistic regression analyses determined how well preoperative demographic and diagnostic measures predicted long-term seizure outcome. Following the initial cortical resection, Engel Class I, II, III and IV outcomes were noted in 35, 6, 12 and 7 patients, respectively. One child died due to disseminated intravascular coagulation associated with pseudomonas sepsis 2 days after surgery. Univariate regression analyses revealed that incomplete removal of seizure onset zone, higher interictal spike-frequency in the preserved cortex and incomplete removal of cortical abnormalities on neuroimaging were associated with a greater risk of failing to obtain Class I outcome. Multivariate logistic regression analysis revealed that incomplete removal of seizure onset zone was the only independent predictor of failure to obtain Class I outcome. The goodness of regression model fit and the predictive ability of regression model were greatest in the full regression model incorporating both ictal and interictal measures [R2 0.44; Area under the receiver operating characteristic (ROC) curve: 0.81], slightly smaller in the reduced model incorporating ictal but not interictal measures (R2 0.40; Area under the ROC curve: 0.79) and slightly smaller again in the reduced model incorporating interictal but not ictal measures (R2 0.27; Area under the ROC curve: 0.77). Seizure onset zone and interictal spike frequency measures on subdural ECoG recording may both be useful in predicting the long-term seizure outcome of epilepsy surgery. Yet, the additive clinical impact of interictal spike frequency measures to predict long-term surgical outcome may be modest in the presence of ictal ECoG and neuroimaging data. PMID:19286694

Using Recombinant Proteins from Lutzomyia longipalpis Saliva to Estimate Human Vector Exposure in Visceral Leishmaniasis Endemic Areas

PubMed Central

Souza, Ana Paula; Andrade, Bruno Bezerril; Aquino, Dorlene; Entringer, Petter; Miranda, José Carlos; Alcantara, Ruan; Ruiz, Daniel; Soto, Manuel; Teixeira, Clarissa R.; Valenzuela, Jesus G.; de Oliveira, Camila Indiani; Brodskyn, Cláudia Ida; Barral-Netto, Manoel; Barral, Aldina

2010-01-01

Background Leishmania is transmitted by female sand flies and deposited together with saliva, which contains a vast repertoire of pharmacologically active molecules that contribute to the establishment of the infection. The exposure to vector saliva induces an immune response against its components that can be used as a marker of exposure to the vector. Performing large-scale serological studies to detect vector exposure has been limited by the difficulty in obtaining sand fly saliva. Here, we validate the use of two sand fly salivary recombinant proteins as markers for vector exposure. Methodology/principal findings ELISA was used to screen human sera, collected in an area endemic for visceral leishmaniasis, against the salivary gland sonicate (SGS) or two recombinant proteins (rLJM11 and rLJM17) from Lutzomyia longipalpis saliva. Antibody levels before and after SGS seroconversion (n = 26) were compared using the Wilcoxon signed rank paired test. Human sera from an area endemic for VL which recognize Lu. longipalpis saliva in ELISA also recognize a combination of rLJM17 and rLJM11. We then extended the analysis to include 40 sera from individuals who were seropositive and 40 seronegative to Lu. longipalpis SGS. Each recombinant protein was able to detect anti-saliva seroconversion, whereas the two proteins combined increased the detection significantly. Additionally, we evaluated the specificity of the anti-Lu. longipalpis response by testing 40 sera positive to Lutzomyia intermedia SGS, and very limited (2/40) cross-reactivity was observed. Receiver-operator characteristics (ROC) curve analysis was used to identify the effectiveness of these proteins for the prediction of anti-SGS positivity. These ROC curves evidenced the superior performance of rLJM17+rLJM11. Predicted threshold levels were confirmed for rLJM17+rLJM11 using a large panel of 1,077 serum samples. Conclusion Our results show the possibility of substituting Lu. longipalpis SGS for two recombinant proteins, LJM17 and LJM11, in order to probe for vector exposure in individuals residing in endemic areas. PMID:20351785

Quantitative Lesion-to-Fat Elasticity Ratio Measured by Shear-Wave Elastography for Breast Mass: Which Area Should Be Selected as the Fat Reference?

PubMed

Youk, Ji Hyun; Son, Eun Ju; Gweon, Hye Mi; Han, Kyung Hwa; Kim, Jeong-Ah

2015-01-01

To investigate whether the diagnostic performance of lesion-to-fat elasticity ratio (Eratio) was affected by the location of the reference fat. For 257 breast masses in 250 women who underwent shear-wave elastography before biopsy or surgery, multiple Eratios were measured with a fixed region-of-interest (ROI) in the mass along with multiple ROIs over the surrounding fat in different locations. Logistic regression analysis was used to determine that Eratio was independently associated with malignancy adjusted for the location of fat ROI (depth, laterality, and distance from lesion or skin). Mean (Emean) and maximum (Emax) elasticity values of fat were divided into four groups according to their interquartile ranges. Diagnostic performance of each group was evaluated using the area under the ROC curve (AUC). False diagnoses of Eratio were reviewed for ROIs on areas showing artifactual high or low stiffness and analyzed by logistic regression analysis to determine variables (associated palpable abnormality, lesion size, the vertical distance from fat ROI to skin, and elasticity values of lesion or fat) independently associated with false results. Eratio was independently associated with malignancy adjusted for the location of fat ROI (P<0.0001). Among four groups of fat elasticity values, the AUC showed no significant difference (<25th percentile, 25th percentile~median, median~75th percentile, and ≥75th percentile; 0.973, 0.982, 0.967, and 0.954 for Emean; 0.977, 0.967, 0.966, and 0.957 for Emax). Fat elasticity values were independently associated with false results of Eratio with the cut-off of 3.18 from ROC curve (P<0.0001). ROIs were set on fat showing artifactual high stiffness in 90% of 10 false negatives and on lesion showing vertical striped artifact or fat showing artifactual low stiffness in 77.5% of 71 false positives. Eratio shows good diagnostic performance regardless of the location of reference fat, except when it is placed in areas of artifacts.

Quantitative Lesion-to-Fat Elasticity Ratio Measured by Shear-Wave Elastography for Breast Mass: Which Area Should Be Selected as the Fat Reference?

PubMed Central

Youk, Ji Hyun; Son, Eun Ju; Gweon, Hye Mi; Han, Kyung Hwa; Kim, Jeong-Ah

2015-01-01

Objectives To investigate whether the diagnostic performance of lesion-to-fat elasticity ratio (Eratio) was affected by the location of the reference fat. Methods For 257 breast masses in 250 women who underwent shear-wave elastography before biopsy or surgery, multiple Eratios were measured with a fixed region-of-interest (ROI) in the mass along with multiple ROIs over the surrounding fat in different locations. Logistic regression analysis was used to determine that Eratio was independently associated with malignancy adjusted for the location of fat ROI (depth, laterality, and distance from lesion or skin). Mean (Emean) and maximum (Emax) elasticity values of fat were divided into four groups according to their interquartile ranges. Diagnostic performance of each group was evaluated using the area under the ROC curve (AUC). False diagnoses of Eratio were reviewed for ROIs on areas showing artifactual high or low stiffness and analyzed by logistic regression analysis to determine variables (associated palpable abnormality, lesion size, the vertical distance from fat ROI to skin, and elasticity values of lesion or fat) independently associated with false results. Results Eratio was independently associated with malignancy adjusted for the location of fat ROI (P<0.0001). Among four groups of fat elasticity values, the AUC showed no significant difference (<25th percentile, 25th percentile~median, median~75th percentile, and ≥75th percentile; 0.973, 0.982, 0.967, and 0.954 for Emean; 0.977, 0.967, 0.966, and 0.957 for Emax). Fat elasticity values were independently associated with false results of Eratio with the cut-off of 3.18 from ROC curve (P<0.0001). ROIs were set on fat showing artifactual high stiffness in 90% of 10 false negatives and on lesion showing vertical striped artifact or fat showing artifactual low stiffness in 77.5% of 71 false positives. Conclusion Eratio shows good diagnostic performance regardless of the location of reference fat, except when it is placed in areas of artifacts. PMID:26368920

Impulse oscillometry system as an alternative diagnostic method for chronic obstructive pulmonary disease.

PubMed

Wei, Xia; Shi, Zhihong; Cui, Yajuan; Mi, Jiuyun; Ma, Zhengquan; Ren, Jingting; Li, Jie; Xu, Shudi; Guo, Youmin

2017-11-01

We aimed to compare impulse oscillation system (IOS) and traditional pulmonary function tests (PFTs) for the assessment of the severity of chronic obstructive pulmonary disease (COPD), and to assess the use of IOS parameters to identify patients who were forced expiratory volume in 1 second (FEV1)%pred < 50%.Patients with COPD (n = 215) were enrolled at the Ninth Hospital of Xi'an Affiliated Hospital of Xi'an Jiaotong University between October 2014 and September 2016. All patients were assessed by traditional PFT and IOS. Diagnostic performance of IOS parameters to determine indication for patients of FEV1%pred < 50% was assessed on receiver-operating characteristics (ROC) curve analysis.Out of 215 patients, 18, 83, 78, and 36 patients were classified as grade 1, 2, 3, and 4, respectively, according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity grading. On Spearman correlation analysis, FEV1%pred, MMEF 75%-25%, and residual volume/total lung capacity (RV/TLC) correlated with total respiratory impedance (Z5)%pred, resistance at 5 Hz (R5)-resistance at 20 Hz (R20), R5-R20% R5, R5, R5%pred, frequency response (Fres), reactance area (Ax), and reactance at 5 Hz (X5). On ROC curve analysis, the area under the curve (AUC) of X5 absolute value, Fres, Ax, Z5%pred, R5-R20, and R5-R20% R5 were 0.748, 0.755, 0.760, 0.705, 0.715, and 0.735, respectively, for COPD patients who required inhalational glucocorticoid therapy.IOS parameters showed a good correlation with traditional pulmonary function parameters; reactance parameters showed a stronger correlation than that of the resistance parameters. IOS can be used as an alternative method for pulmonary function assessment in patients with COPD with FEV1%pred < 50% who need inhalational glucocorticoid therapy. ChiCTR-OCH-14004904.

Impulse oscillometry system as an alternative diagnostic method for chronic obstructive pulmonary disease

PubMed Central

Wei, Xia; Shi, Zhihong; Cui, Yajuan; Mi, Jiuyun; Ma, Zhengquan; Ren, Jingting; Li, Jie; Xu, Shudi; Guo, Youmin

2017-01-01

Abstract We aimed to compare impulse oscillation system (IOS) and traditional pulmonary function tests (PFTs) for the assessment of the severity of chronic obstructive pulmonary disease (COPD), and to assess the use of IOS parameters to identify patients who were forced expiratory volume in 1 second (FEV1)%pred < 50%. Patients with COPD (n = 215) were enrolled at the Ninth Hospital of Xi’an Affiliated Hospital of Xi’an Jiaotong University between October 2014 and September 2016. All patients were assessed by traditional PFT and IOS. Diagnostic performance of IOS parameters to determine indication for patients of FEV1%pred < 50% was assessed on receiver-operating characteristics (ROC) curve analysis. Out of 215 patients, 18, 83, 78, and 36 patients were classified as grade 1, 2, 3, and 4, respectively, according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity grading. On Spearman correlation analysis, FEV1%pred, MMEF 75%–25%, and residual volume/total lung capacity (RV/TLC) correlated with total respiratory impedance (Z5)%pred, resistance at 5 Hz (R5)-resistance at 20 Hz (R20), R5-R20% R5, R5, R5%pred, frequency response (Fres), reactance area (Ax), and reactance at 5 Hz (X5). On ROC curve analysis, the area under the curve (AUC) of X5 absolute value, Fres, Ax, Z5%pred, R5-R20, and R5-R20% R5 were 0.748, 0.755, 0.760, 0.705, 0.715, and 0.735, respectively, for COPD patients who required inhalational glucocorticoid therapy. IOS parameters showed a good correlation with traditional pulmonary function parameters; reactance parameters showed a stronger correlation than that of the resistance parameters. IOS can be used as an alternative method for pulmonary function assessment in patients with COPD with FEV1%pred < 50% who need inhalational glucocorticoid therapy. Clinical trial registration number: ChiCTR-OCH-14004904. PMID:29145259

Comparison of radiologist performance with photon-counting full-field digital mammography to conventional full-field digital mammography.

PubMed

Cole, Elodia B; Toledano, Alicia Y; Lundqvist, Mats; Pisano, Etta D

2012-08-01

The purpose of this study was to assess the performance of a MicroDose photon-counting full-field digital mammography (PCM) system in comparison to full-field digital mammography (FFDM) for area under the receiver-operating characteristic (ROC) curve (AUC), sensitivity, specificity, and feature analysis of standard-view mammography for women presenting for screening mammography, diagnostic mammography, or breast biopsy. A total of 133 women were enrolled in this study at two European medical centers, with 67 women who had a pre-existing 10-36 months FFDM enrolled prospectively into the study and 66 women who underwent breast biopsy and had screening PCM and diagnostic FFDM, including standard craniocaudal and mediolateral oblique views of the breast with the lesion, enrolled retrospectively. The case mix consisted of 49 cancers, 17 biopsy-benign cases, and 67 normal cases. Sixteen radiologists participated in the reader study and interpreted all 133 cases in both conditions, separated by washout period of ≥4 weeks. ROC curve and free-response ROC curve analyses were performed for noninferiority of PCM compared to FFDM using a noninferiority margin Δ value of 0.10. Feature analysis of the 66 cases with lesions was conducted with all 16 readers at the conclusion of the blinded reads. Mean glandular dose was recorded for all cases. The AUC for PCM was 0.947 (95% confidence interval [CI], 0.920-0.974) and for FFDM was 0.931 (95% CI, 0.898-0.964). Sensitivity per case for PCM was 0.936 (95% CI, 0.897-0.976) and for FFDM was 0.908 (95% CI, 0.856-0.960). Specificity per case for PCM was 0.764 (95% CI, 0.688-0.841) and for FFDM was 0.749 (95% CI, 0.668-0.830). Free-response ROC curve figures of merit were 0.920 (95% CI, 0.881-0.959) and 0.903 (95% CI, 0.858-0.948) for PCM and FFDM, respectively. Sensitivity per lesion was 0.903 (95% CI, 0.846-0.960) and 0.883 (95% CI, 0.823-0.944) for PCM and FFDM, respectively. The average false-positive marks per image of noncancer cases were 0.265 (95% CI, 0.171-0.359) and 0.281 (95% CI, 0.188-0.374) for PCM and FFDM, respectively. Noninferiority P values for AUC, sensitivity (per case and per lesion), specificity, and average false-positive marks per image were all statistically significant (P < .001). The noninferiority P value for free-response ROC was <.025, from the 95% CI for the difference. Feature analysis resulted in PCM being preferred to FFDM by the readers for ≥70% of the cases. The average mean glandular dose for PCM was 0.74 mGy (95% CI, 0.722-0.759 mGy) and for FFDM was 1.23 mGy (95% CI, 1.199-1.262 mGy). In this study, radiologist performance with PCM was not inferior to that with conventional FFDM at an average 40% lower mean glandular dose. Copyright © 2012 AUR. Published by Elsevier Inc. All rights reserved.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies.

PubMed

Rousson, Valentin; Zumbrunn, Thomas

2011-06-22

Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies

PubMed Central

2011-01-01

Background Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. Methods We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. Results We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. Conclusions We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application. PMID:21696604

Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.

PubMed

Du, Pang; Tang, Liansheng

2009-01-30

When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example. Copyright (c) 2008 John Wiley & Sons, Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Islam, Md. Shafiqul, E-mail: shafique@eng.ukm.my; Hannan, M.A., E-mail: hannan@eng.ukm.my; Basri, Hassan

Highlights: • Solid waste bin level detection using Dynamic Time Warping (DTW). • Gabor wavelet filter is used to extract the solid waste image features. • Multi-Layer Perceptron classifier network is used for bin image classification. • The classification performance evaluated by ROC curve analysis. - Abstract: The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensormore » intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.« less

Linking Retinal Microvasculature Features With Severity of Diabetic Retinopathy Using Optical Coherence Tomography Angiography.

PubMed

Bhanushali, Devanshi; Anegondi, Neha; Gadde, Santosh G K; Srinivasan, Priya; Chidambara, Lavanya; Yadav, Naresh Kumar; Sinha Roy, Abhijit

2016-07-01

To correlate retinal vascular features with severity and systemic indicators of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). A total of 209 eyes of 122 type 2 diabetes mellitus patients with DR and 60 eyes of 31 normal Indian subjects underwent OCTA imaging. The diabetic retinopathy patients were graded as having either nonproliferative diabetic retinopathy (NPDR: mild, moderate, and severe NPDR using Early Treatment Diabetic Retinopathy Study classification) or proliferative diabetic retinopathy (PDR). Local fractal analysis was applied to the superficial and deep retinal OCTA images. Foveal avascular zone area (FAZ in mm2); vessel density (%); spacing between large vessels (%); and spacing between small vessels (%) were analyzed. Sensitivity and specificity of vascular parameters were assessed with receiver operating characteristics (ROC) curve. Normal eyes had a significantly lower FAZ area, higher vessel density, and lower spacing between large and small vessels compared with DR grades (P < 0.001). In the superficial layer, PDR and severe NPDR had higher spacing between large vessels than mild and moderate NPDR (P = 0.04). However, mild NPDR had higher spacing between the small vessels (P < 0.001). Spacing between the large vessels in the superficial retinal layer correlated positively with HbA1c (r = 0.25, P = 0.03); fasting (r = 0.23, P = 0.02); and postprandial (r = 0.26, P = 0.03) blood sugar. The same spacing in the deep retinal vascular plexus had the highest area under the ROC curve (0.99 ± 0.01) and was uniformly elevated in all diabetic eyes (P > 0.05). Spacing between the large vessels in the superficial and deep retinal layers had superior diagnostic performance than overall vessel density.

Assessing Infant Feeding Attitudes of Expectant Women in a Provincial Population in Canada: Validation of the Iowa Infant Feeding Attitude Scale.

PubMed

Twells, Laurie K; Midodzi, William K; Ludlow, Valerie; Murphy-Goodridge, Janet; Burrage, Lorraine; Gill, Nicole; Halfyard, Beth; Schiff, Rebecca; Newhook, Leigh Anne

2016-08-01

Maternal attitudes to infant feeding are predictive of intent and initiation of breastfeeding. The Iowa Infant Feeding Attitude Scale (IIFAS) has not been validated in the Canadian population. This study was conducted in Newfoundland and Labrador, a Canadian province with low breastfeeding rates. Objectives were to assess the reliability and validity of the IIFAS in expectant mothers; to compare attitudes to infant feeding in urban and rural areas; and to examine whether attitudes are associated with intent to breastfeed. The IIFAS assessment tool was administered to 793 pregnant women. Differences in the total IIFAS scores were compared between urban and rural areas. Reliability and validity analysis was conducted on the IIFAS. The receiver operating characteristic (ROC) of the IIFAS was assessed against mother's intent to breastfeed. The mean ± SD of the total IIFAS score of the overall sample was 64.0 ± 10.4. There were no significant differences in attitudes between urban (63.9 ± 10.5) and rural (64.4 ± 9.9) populations. There were significant differences in total IIFAS scores between women who intend to breastfeed (67.3 ± 8.3) and those who do not (51.6 ± 7.7), regardless of population region. The high value of the area under the curve (AUC) of the ROC (AUC = 0.92) demonstrates excellent ability of the IIFAS to predict intent to breastfeed. The internal consistency of the IIFAS was strong, with a Cronbach's alpha greater than .80 in the overall sample. The IIFAS examined in this provincial population provides a valid and reliable assessment of maternal attitudes toward infant feeding. This tool could be used to identify mothers less likely to breastfeed and to inform health promotion programs. © The Author(s) 2014.

Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system.

PubMed

Iyatomi, Hitoshi; Oka, Hiroshi; Saito, Masataka; Miyake, Ayako; Kimoto, Masayuki; Yamagami, Jun; Kobayashi, Seiichiro; Tanikawa, Akiko; Hagiwara, Masafumi; Ogawa, Koichi; Argenziano, Giuseppe; Soyer, H Peter; Tanaka, Masaru

2006-04-01

The aims of this study were to provide a quantitative assessment of the tumour area extracted by dermatologists and to evaluate computer-based methods from dermoscopy images for refining a computer-based melanoma diagnostic system. Dermoscopic images of 188 Clark naevi, 56 Reed naevi and 75 melanomas were examined. Five dermatologists manually drew the border of each lesion with a tablet computer. The inter-observer variability was evaluated and the standard tumour area (STA) for each dermoscopy image was defined. Manual extractions by 10 non-medical individuals and by two computer-based methods were evaluated with STA-based assessment criteria: precision and recall. Our new computer-based method introduced the region-growing approach in order to yield results close to those obtained by dermatologists. The effectiveness of our extraction method with regard to diagnostic accuracy was evaluated. Two linear classifiers were built using the results of conventional and new computer-based tumour area extraction methods. The final diagnostic accuracy was evaluated by drawing the receiver operating curve (ROC) of each classifier, and the area under each ROC was evaluated. The standard deviations of the tumour area extracted by five dermatologists and 10 non-medical individuals were 8.9% and 10.7%, respectively. After assessment of the extraction results by dermatologists, the STA was defined as the area that was selected by more than two dermatologists. Dermatologists selected the melanoma area with statistically smaller divergence than that of Clark naevus or Reed naevus (P = 0.05). By contrast, non-medical individuals did not show this difference. Our new computer-based extraction algorithm showed superior performance (precision, 94.1%; recall, 95.3%) to the conventional thresholding method (precision, 99.5%; recall, 87.6%). These results indicate that our new algorithm extracted a tumour area close to that obtained by dermatologists and, in particular, the border part of the tumour was adequately extracted. With this refinement, the area under the ROC increased from 0.795 to 0.875 and the diagnostic accuracy showed an increase of approximately 20% in specificity when the sensitivity was 80%. It can be concluded that our computer-based tumour extraction algorithm extracted almost the same area as that obtained by dermatologists and provided improved computer-based diagnostic accuracy.

The Long Exercise Test in Periodic Paralysis: A Bayesian Analysis.

PubMed

Simmons, Daniel B; Lanning, Julie; Cleland, James C; Puwanant, Araya; Twydell, Paul T; Griggs, Robert C; Tawil, Rabi; Logigian, Eric L

2018-05-12

The long exercise test (LET) is used to assess the diagnosis of periodic paralysis (PP), but LET methodology and normal "cut-off" values vary. To determine optimal LET methodology and cut-offs, we reviewed LET data (abductor digiti minimi (ADM) motor response amplitude, area) from 55 PP patients (32 genetically definite) and 125 controls. Receiver operating characteristic (ROC) curves were constructed and area-under-the-curve (AUC) calculated to compare 1) peak-to-nadir versus baseline-to-nadir methodologies, and 2) amplitude versus area decrements. Using Bayesian principles, optimal "cut-off" decrements that achieved 95% post-test probability of PP were calculated for various pre-test probabilities (PreTPs). AUC was highest for peak-to-nadir methodology and equal for amplitude and area decrements. For PreTP ≤50%, optimal decrement cut-offs (peak-to-nadir) were >40% (amplitude) or >50% (area). For confirmation of PP, our data endorse the diagnostic utility of peak-to-nadir LET methodology using 40% amplitude or 50% area decrement cut-offs for PreTPs ≤50%. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Operating characteristics of depression and anxiety disorder phenotype dimensions and trait neuroticism: a theoretical examination of the fear and distress disorders from the Netherlands study of depression and anxiety.

PubMed

Tully, Phillip J; Wardenaar, Klaas J; Penninx, Brenda W J H

2015-03-15

The receiver operating characteristics (ROC) of anhedonic depression and anxious arousal to detect the distress- (major depression, dysthymia, generalized anxiety disorder) and fear-disorder clusters (i.e. panic disorder, agoraphobia, social phobia) have not been reported in a large sample. A sample of 2981 persons underwent structured psychiatric interview; n=652 were without lifetime depression and anxiety disorder history. Participants also completed a neuroticism scale (Revised NEO Five Factor Inventory [NEO-FFI]), and the 30-item short adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ-D30) measuring anhedonic depression, anxious arousal and general distress. Maximal sensitivity and specificity was determined by the Youden Index and the area-under-the-curve (AUC) in ROC analysis. A total of 2624 completed all measures (age M=42.4 years±13.1, 1760 females [67.1%]), including 1060 (40.4%) persons who met criteria for a distress-disorder, and 973 (37.1%) who met criteria for a fear-disorder. The general distress dimension provided the highest ROC values in the detection of the distress-disorders (AUC=.814, sensitivity=71.95%, specificity=76.34%, positive predictive value=67.33, negative predictive value=80.07). None of the measures provided suitable operating characteristics in the detection of the fear-disorders with specificity values <75%. Over sampling of depression and anxiety disorders may lead to inflated positive- and negative predictive values. The MASQ-D30 general distress dimension showed clinically suitable operating characteristics in the detection of distress-disorders. Neither neuroticism nor the MASQ-D30 dimensions provided suitable operating characteristics in the detection of the fear-disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

Strain ratio ultrasound elastography increases the accuracy of colour-Doppler ultrasound in the evaluation of Thy-3 nodules. A bi-centre university experience.

PubMed

Cantisani, Vito; Maceroni, Piero; D'Andrea, Vito; Patrizi, Gregorio; Di Segni, Mattia; De Vito, Corrado; Grazhdani, Hektor; Isidori, Andrea M; Giannetta, Elisa; Redler, Adriano; Frattaroli, Fabrizio; Giacomelli, Laura; Di Rocco, Giorgio; Catalano, Carlo; D'Ambrosio, Ferdinando

2016-05-01

To assess whether ultrasound elastography (USE) with strain ratio increases diagnostic accuracy of Doppler ultrasound in further characterisation of cytologically Thy3 thyroid nodules. In two different university diagnostic centres, 315 patients with indeterminate cytology (Thy3) in thyroid nodules aspirates were prospectively evaluated with Doppler ultrasound and strain ratio USE before surgery. Ultrasonographic features were analysed separately and together as ultrasound score, to assess sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Receiver operating characteristic (ROC) curves to identify optimal cut-off value of the strain ratio were also provided. Diagnosis on a surgical specimen was considered the standard of reference. Higher strain ratio values were found in malignant nodules, with an optimum strain ratio cut-off of 2.09 at ROC analysis. USE with strain ratio showed 90.6% sensitivity, 93% specificity, 82.8% PPV, 96.4% NPV, while US score yielded a sensitivity of 52.9%, specificity of 84.3%, PPV 55.6% and NPV 82.9%. The diagnostic gain with strain ratio was statistically significant as proved by ROC areas, which was 0.9182 for strain ratio and 0.6864 for US score. USE with strain ratio should be considered a useful additional tool to colour-Doppler US, since it improves characterisation of thyroid nodules with indeterminate cytology. • Strain ratio measurements improve differentiation of thyroid nodules with indeterminate cytology • Elastography with strain ratio is more reliable than ultrasound features and ultrasound score • Strain ratio may help to better select patients with Thy 3 nodules candidate for surgery.

Plasma long non-coding RNA BACE1 as a novel biomarker for diagnosis of Alzheimer disease.

PubMed

Feng, Liang; Liao, Yu-Ting; He, Jin-Cai; Xie, Cheng-Long; Chen, Si-Yan; Fan, Hui-Hui; Su, Zhi-Peng; Wang, Zhen

2018-01-09

Long non-coding RNA (LncRNA) have been reported to be involved in the pathogenesis of neurodegenerative diseases, but whether it can serve as a biomarker for Alzheimer disease (AD) is not yet known. The present study selected four specific LncRNA (17A, 51A, BACE1 and BC200) as possible AD biomarker. RT-qPCR was performed to validate the LncRNA. Receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) were applied to study the potential of LncRNA as a biomarker in a population of 88 AD patients and 72 control individuals. We found that the plasma LncRNA BACE1 level of AD patients was significantly higher than that of healthy controls (p = 0.006). Plasma level of LncRNA 17A, 51A and BC200 did not show a significant difference between two groups (p = 0.098, p = 0.204 and p = 0.232, respectively). ROC curve analysis showed that LncRNA BACE1 was the best candidate of these LncRNA (95% CI: 0.553-0.781, p = 0.003). In addition, no correlation was found for expression of these LncRNA in both control and AD groups with age or MMSE scale (p > 0.05). Our present study compared the plasma level of four LncRNA between AD and non-AD patients, and found that the level of the BACE1 is increased in the plasma of AD patients and have a high specificity (88%) for AD, indicating BACE1 may be a potential candidate biomarker to predict AD.

Diagnostic accuracy of the Eurotest for dementia: a naturalistic, multicenter phase II study

PubMed Central

Carnero-Pardo, Cristobal; Gurpegui, Manuel; Sanchez-Cantalejo, Emilio; Frank, Ana; Mola, Santiago; Barquero, M Sagrario; Montoro-Rios, M Teresa

2006-01-01

Background Available screening tests for dementia are of limited usefulness because they are influenced by the patient's culture and educational level. The Eurotest, an instrument based on the knowledge and handling of money, was designed to overcome these limitations. The objective of this study was to evaluate the diagnostic accuracy of the Eurotest in identifying dementia in customary clinical practice. Methods A cross-sectional, multi-center, naturalistic phase II study was conducted. The Eurotest was administered to consecutive patients, older than 60 years, in general neurology clinics. The patients' condition was classified as dementia or no dementia according to DSM-IV diagnostic criteria. We calculated sensitivity (Sn), specificity (Sp) and area under the ROC curves (aROC) with 95% confidence intervals. The influence of social and educational factors on scores was evaluated with multiple linear regression analysis, and the influence of these factors on diagnostic accuracy was evaluated with logistic regression. Results Sixteen neurologists recruited a total of 516 participants: 101 with dementia, 380 without dementia, and 35 who were excluded. Of the 481 participants who took the Eurotest, 38.7% were totally or functionally illiterate and 45.5% had received no formal education. Mean time needed to administer the test was 8.2+/-2.0 minutes. The best cut-off point was 20/21, with Sn = 0.91 (0.84–0.96), Sp = 0.82 (0.77–0.85), and aROC = 0.93 (0.91–0.95). Neither the scores on the Eurotest nor its diagnostic accuracy were influenced by social or educational factors. Conclusion This naturalistic and pragmatic study shows that the Eurotest is a rapid, simple and useful screening instrument, which is free from educational influences, and has appropriate internal and external validity. PMID:16606455

A completely automated CAD system for mass detection in a large mammographic database.

PubMed

Bellotti, R; De Carlo, F; Tangaro, S; Gargano, G; Maggipinto, G; Castellano, M; Massafra, R; Cascio, D; Fauci, F; Magro, R; Raso, G; Lauria, A; Forni, G; Bagnasco, S; Cerello, P; Zanon, E; Cheran, S C; Lopez Torres, E; Bottigli, U; Masala, G L; Oliva, P; Retico, A; Fantacci, M E; Cataldo, R; De Mitri, I; De Nunzio, G

2006-08-01

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

External validation and comparison of three pediatric clinical dehydration scales.

PubMed

Jauregui, Joshua; Nelson, Daniel; Choo, Esther; Stearns, Branden; Levine, Adam C; Liebmann, Otto; Shah, Sachita P

2014-01-01

To prospectively validate three popular clinical dehydration scales and overall physician gestalt in children with vomiting or diarrhea relative to the criterion standard of percent weight change with rehydration. We prospectively enrolled a non-consecutive cohort of children ≤ 18 years of age with an acute episode of diarrhea or vomiting. Patient weight, clinical scale variables and physician clinical impression, or gestalt, were recorded before and after fluid resuscitation in the emergency department and upon hospital discharge. The percent weight change from presentation to discharge was used to calculate the degree of dehydration, with a weight change of ≥ 5% considered significant dehydration. Receiver operating characteristics (ROC) curves were constructed for each of the three clinical scales and physician gestalt. Sensitivity and specificity were calculated based on the best cut-points of the ROC curve. We approached 209 patients, and of those, 148 were enrolled and 113 patients had complete data for analysis. Of these, 10.6% had significant dehydration based on our criterion standard. The Clinical Dehydration Scale (CDS) and Gorelick scales both had an area under the ROC curve (AUC) statistically different from the reference line with AUCs of 0.72 (95% CI 0.60, 0.84) and 0.71 (95% CI 0.57, 0.85) respectively. The World Health Organization (WHO) scale and physician gestalt had AUCs of 0.61 (95% CI 0.45, 0.77) and 0.61 (0.44, 0.78) respectively, which were not statistically significant. The Gorelick scale and Clinical Dehydration Scale were fair predictors of dehydration in children with diarrhea or vomiting. The World Health Organization scale and physician gestalt were not helpful predictors of dehydration in our cohort.

External Validation and Comparison of Three Pediatric Clinical Dehydration Scales

PubMed Central

Jauregui, Joshua; Nelson, Daniel; Choo, Esther; Stearns, Branden; Levine, Adam C.; Liebmann, Otto; Shah, Sachita P.

2014-01-01

Objective To prospectively validate three popular clinical dehydration scales and overall physician gestalt in children with vomiting or diarrhea relative to the criterion standard of percent weight change with rehydration. Methods We prospectively enrolled a non-consecutive cohort of children ≤ 18 years of age with an acute episode of diarrhea or vomiting. Patient weight, clinical scale variables and physician clinical impression, or gestalt, were recorded before and after fluid resuscitation in the emergency department and upon hospital discharge. The percent weight change from presentation to discharge was used to calculate the degree of dehydration, with a weight change of ≥ 5% considered significant dehydration. Receiver operating characteristics (ROC) curves were constructed for each of the three clinical scales and physician gestalt. Sensitivity and specificity were calculated based on the best cut-points of the ROC curve. Results We approached 209 patients, and of those, 148 were enrolled and 113 patients had complete data for analysis. Of these, 10.6% had significant dehydration based on our criterion standard. The Clinical Dehydration Scale (CDS) and Gorelick scales both had an area under the ROC curve (AUC) statistically different from the reference line with AUCs of 0.72 (95% CI 0.60, 0.84) and 0.71 (95% CI 0.57, 0.85) respectively. The World Health Organization (WHO) scale and physician gestalt had AUCs of 0.61 (95% CI 0.45, 0.77) and 0.61 (0.44, 0.78) respectively, which were not statistically significant. Conclusion The Gorelick scale and Clinical Dehydration Scale were fair predictors of dehydration in children with diarrhea or vomiting. The World Health Organization scale and physician gestalt were not helpful predictors of dehydration in our cohort. PMID:24788134

Predisposing factors for postoperative nausea and vomiting in gynecologic tumor patients.

PubMed

de Souza, Daiane Spitz; Costa, Amine Farias; Chaves, Gabriela Villaça

2016-11-01

To evaluate the predictors of postoperative nausea and vomiting (PONV) in women with gynecologic tumor. The analysis was based on prospectively collected data of 82 adult patients with gynecologic tumor, who were submitted to open surgical treatment and undergoing general anesthesia. The predictors included were age ≥50 years, non-smoker, use of postoperative opioids, mechanical bowel preparation, intraoperative intravenous hydration (IH) ≥10 mL/kg/h, and IH in the immediate postoperative, first and second postoperative days (PO1 and PO2) ≥30 mL/kg. A score with predictor variables was built. A multiple logistic regression was fitted. To estimate the discriminating power of the chosen model, a receiver operating characteristic (ROC) curve was plotted and the area under the ROC curve (AUC) was calculated. Statistical significance was set at p value <0.05 and the confidence interval at 95 %. The incidence (%) of nausea, vomiting and both, in the general population, was 36.6, 28.1, 22.0, respectively. The highest incidences of PONV were found in non-smokers and in patients who received >30 mL/kg of IH in the PO2. The results of the adjusted model showed an increased risk of PONV for each 1-point increase in the score punctuation. The relative risk was higher than 2.0 for vomiting in all period and in the PO1. The ROC curve showed great discrimination of postoperative nausea and vomiting from the proposed score (AUC >0.75). The study population was at high risk of PONV. Therefore, institutional guidelines abolishing modificable variables following temporal evaluation of the effectiveness should be undertaken.

Cumulative lactate and hospital mortality in ICU patients

PubMed Central

2013-01-01

Background Both hyperlactatemia and persistence of hyperlactatemia have been associated with bad outcome. We compared lactate and lactate-derived variables in outcome prediction. Methods Retrospective observational study. Case records from 2,251 consecutive intensive care unit (ICU) patients admitted between 2001 and 2007 were analyzed. Baseline characteristics, all lactate measurements, and in-hospital mortality were recorded. The time integral of arterial blood lactate levels above the upper normal threshold of 2.2 mmol/L (lactate-time-integral), maximum lactate (max-lactate), and time-to-first-normalization were calculated. Survivors and nonsurvivors were compared and receiver operating characteristic (ROC) analysis were applied. Results A total of 20,755 lactate measurements were analyzed. Data are srpehown as median [interquartile range]. In nonsurvivors (n = 405) lactate-time-integral (192 [0–1881] min·mmol/L) and time-to-first normalization (44.0 [0–427] min) were higher than in hospital survivors (n = 1846; 0 [0–134] min·mmol/L and 0 [0–75] min, respectively; all p < 0.001). Normalization of lactate <6 hours after ICU admission revealed better survival compared with normalization of lactate >6 hours (mortality 16.6% vs. 24.4%; p < 0.001). AUC of ROC curves to predict in-hospital mortality was the largest for max-lactate, whereas it was not different among all other lactate derived variables (all p > 0.05). The area under the ROC curves for admission lactate and lactate-time-integral was not different (p = 0.36). Conclusions Hyperlactatemia is associated with in-hospital mortality in a heterogeneous ICU population. In our patients, lactate peak values predicted in-hospital mortality equally well as lactate-time-integral of arterial blood lactate levels above the upper normal threshold. PMID:23446002

Holistic component of image perception in mammogram interpretation: gaze-tracking study.

PubMed

Kundel, Harold L; Nodine, Calvin F; Conant, Emily F; Weinstein, Susan P

2007-02-01

To test the hypothesis that rapid and accurate performance of the proficient observer in mammogram interpretation involves a shift in the mechanism of image perception from a relatively slow search-to-find mode to a relatively fast holistic mode. This HIPAA-compliant study had institutional review board approval, and participant informed consent was obtained; patient informed consent was not required. The eye positions of three full-time mammographers, one attending radiologist, two mammography fellows, and three radiology residents were recorded during the interpretation of 20 normal and 20 subtly abnormal mammograms. The search time required to first locate a cancer, as well as the initial eye scan path, was determined and compared with diagnostic performance as measured with receiver operating characteristic (ROC) analysis. The median time for all observers to fixate a cancer, regardless of the decision outcome, was 1.13 seconds, with a range of 0.68 second to 3.06 seconds. Even though most of the lesions were fixated, recognition of them as cancerous ranged from 85% (17 of 20) to 10% (two of 20), with corresponding areas under the ROC curve of 0.87-0.40. The ROC index of detectability, d(a), was linearly related to the time to first fixate a cancer with a correlation (r(2)) of 0.81. The rapid initial fixation of a true abnormality is evidence for a global perceptual process capable of analyzing the visual input of the entire retinal image and pinpointing the spatial location of an abnormality. It appears to be more highly developed in the most proficient observers, replacing the less efficient initial search-to-find strategies. (c) RSNA, 2007.

Validation of the German Diabetes Risk Score among the general adult population: findings from the German Health Interview and Examination Surveys

PubMed Central

Paprott, Rebecca; Mühlenbruch, Kristin; Mensink, Gert B M; Thiele, Silke; Schulze, Matthias B; Scheidt-Nave, Christa; Heidemann, Christin

2016-01-01

Objective To evaluate the German Diabetes Risk Score (GDRS) among the general adult German population for prediction of incident type 2 diabetes and detection of prevalent undiagnosed diabetes. Methods The longitudinal sample for prediction of incident diagnosed type 2 diabetes included 3625 persons who participated both in the examination survey in 1997–1999 and the examination survey in 2008–2011. Incident diagnosed type 2 diabetes was defined as first-time physician diagnosis or antidiabetic medication during 5 years of follow-up excluding potential incident type 1 and gestational diabetes. The cross-sectional sample for detection of prevalent undiagnosed diabetes included 6048 participants without diagnosed diabetes of the examination survey in 2008–2011. Prevalent undiagnosed diabetes was defined as glycated haemoglobin ≥6.5% (48 mmol/mol). We assessed discrimination as area under the receiver operating characteristic curve (ROC-AUC (95% CI)) and calibration through calibration plots. Results In longitudinal analyses, 82 subjects with incident diagnosed type 2 diabetes were identified after 5 years of follow-up. For prediction of incident diagnosed diabetes, the GDRS yielded an ROC-AUC of 0.87 (0.83 to 0.90). Calibration plots indicated excellent prediction for low diabetes risk and overestimation for intermediate and high diabetes risk. When considering the entire follow-up period of 11.9 years (ROC-AUC: 0.84 (0.82 to 0.86)) and including incident undiagnosed diabetes (ROC-AUC: 0.81 (0.78 to 0.84)), discrimination decreased somewhat. A previously simplified paper version of the GDRS yielded a similar predictive ability (ROC-AUC: 0.86 (0.82 to 0.89)). In cross-sectional analyses, 128 subjects with undiagnosed diabetes were identified. For detection of prevalent undiagnosed diabetes, the ROC-AUC was 0.84 (0.81 to 0.86). Again, the simplified version yielded a similar result (ROC-AUC: 0.83 (0.80 to 0.86)). Conclusions The GDRS might be applied for public health monitoring of diabetes risk in the German adult population. Future research needs to evaluate whether the GDRS is useful to improve diabetes risk awareness and prevention among the general population. PMID:27933187

[Value of non-invasive models of liver fibrosis in judgment of treatment timing in chronic hepatitis B patients with ALT < 2×upper limit of normal].

PubMed

Zhou, Q Q; Hu, Y B; Zhou, K; Zhang, W W; Li, M H; Dong, P; Di, J G; Hong, L; Du, Q W; Xie, Y; Sun, Q F

2016-09-20

Objective: To investigate the value of non-invasive liver fibrosis models, FIB-4, S index, aspartate aminotransferase to platelet ratio index(APRI), globulin-platelet(GP)model, aspartate aminotransferase/platelet/gamma-glutamyl transpeptidase/alpha-fetoprotein(APGA), and platelet/age/phosphatase/alpha-fetoprotein/aspartate aminotransferase(PAPAS), in the diagnosis of marked liver fibrosis in chronic hepatitis B(CHB)patients with ALT < 2×upper limit of normal(ULN), as well as treatment timing for this population. Methods: A total of 389 CHB patients with ALT < 2×ULN who were admitted to Beijing Ditan Hospital and whose treatment timing was difficult to judge were enrolled. Transdermal liver biopsy was performed to obtain pathological results, and routine serological tests were performed, including routine blood test, serum biochemical parameters, hepatitis B virus(HBV)markers, and HBV DNA. According to liver pathology, the patients were divided into non-marked liver fibrosis group(S < 2)with 324 patients and marked liver fibrosis group(S≥2)with 65 patients. The non-invasive models for predicting liver fibrosis was established with reference to original articles. SPSS 19.0 software was used for statistical analysis, and the receiver operating characteristic(ROC)curve was used to compare the value of different non-invasive models in predicting marked liver fibrosis in this population. Results: All the non-invasive models had a certain diagnostic value for liver fibrosis degree in these patients, and the areas under the ROC curve for APRI, FIB-4, APGA, S index, PAPAS, and GP model were 0.718, 0.691, 0.758, 0.729, 0.673, and 0.691, respectively. APGA had the largest area under the ROC curve(0.758, 95% CI 0.673-0.844), and gamma-glutamyl transpeptidase was significantly positively correlated with liver fibrosis degree. Conclusion: The non-invasive models of liver fibrosis can identify marked liver fibrosis in CHB patients with ALT < 2×ULN in whom it is difficult to judge treatment timing and help to determine treatment timing for them. APGA model has the highest value and can reduce the need for liver biopsy to the certain degree.

B-type Natriuretic Peptide and RISK-PCI Score in the Risk Assessment in Patients with STEMI Treated by Primary Percutaneous Coronary Intervention.

PubMed

Asanin, Milika; Mrdovic, Igor; Savic, Lidija; Matic, Dragan; Krljanac, Gordana; Vukcevic, Vladan; Orlic, Dejan; Stankovic, Goran; Marinkovic, Jelena; Stankovic, Sanja

2016-01-01

RISK-PCI score is a novel score for risk stratification of patients with ST elevation myocardial infarction (STEMI) treated by primary percutaneous coronary intervention (pPCI). The aim of this study was to evaluate the role of B-type natriuretic peptide (BNP) and the RISK-PCI score for early risk assessment in patients with STEMI treated by pPCI. In 120 patients with STEMI treated by pPCI, BNP was measured on admission before pPCI. The primary end point was 30-day mortality. The ROC curve analysis revealed that the most powerful predictive factors of 30-day mortality were the plasma level of BNP ≥ 206.6 pg/mL with the sensitivity of 75% and specificity of 87.5% and the RISK-PCI score ≥ 5.25 with the sensitivity of 75% and specificity of 85.7%. Thirty-day mortality was 6.7%. After multivariate adjustment, admission BNP (≥ 206.6 pg/mL) (OR 2.952, 95% CI 1.072 - 8.133, p = 0.036) and the RISK-PCI score (≥ 5.25) (OR 2.284, 95% CI 1.140-4.578, p = 0.020) were independent predictors of 30-day mortality. The area under the ROC curve using the RISK-PCI score and BNP to detect mortality was 0.828 (p = 0.002) and 0.903 (p < 0.001), respectively. Addition of BNP to RISK-PCI score increased the area under the ROC to 0.949 (p < 0.001), but this increase measured by the c-statistic was not significant (p = 0.107). Furthermore, the significant improvement in risk reclassification (p < 0.001) and the integrated discrimination index (p = 0.042) were observed with the addition of BNP to RISK-PCI score for 30-day mortality. BNP on admission and the RISK-PCI score were the independent predictors of 30-day mortality in patients with the STEMI treated by pPCI. BNP in combination with the RISK-PCI score showed the way to more accurate risk assessment in patients with STEMI treated by pPCI.

MYC and Human Telomerase Gene (TERC) Copy Number Gain in Early-stage Non–small Cell Lung Cancer

PubMed Central

Flacco, Antonella; Ludovini, Vienna; Bianconi, Fortunato; Ragusa, Mark; Bellezza, Guido; Tofanetti, Francesca R.; Pistola, Lorenza; Siggillino, Annamaria; Vannucci, Jacopo; Cagini, Lucio; Sidoni, Angelo; Puma, Francesco; Varella-Garcia, Marileila; Crinò, Lucio

2015-01-01

Objectives We investigated the frequency of MYC and TERC increased gene copy number (GCN) in early-stage non–small cell lung cancer (NSCLC) and evaluated the correlation of these genomic imbalances with clinicopathologic parameters and outcome. Materials and Methods Tumor tissues were obtained from 113 resected NSCLCs. MYC and TERC GCNs were tested by fluorescence in situ hybridization (FISH) according to the University of Colorado Cancer Center (UCCC) criteria and based on the receiver operating characteristic (ROC) classification. Results When UCCC criteria were applied, 41 (36%) cases for MYC and 41 (36%) cases for TERC were considered FISH-positive. MYC and TERC concurrent FISH-positive was observed in 12 cases (11%): 2 (17%) cases with gene amplification and 10 (83%) with high polysomy. By using the ROC analysis, high MYC (mean ≥2.83 copies/cell) and TERC (mean ≥2.65 copies/cell) GCNs were observed in 60 (53.1%) cases and 58 (51.3%) cases, respectively. High TERC GCN was associated with squamous cell carcinoma (SCC) histology (P = 0.001). In univariate analysis, increased MYC GCN was associated with shorter overall survival (P = 0.032 [UCCC criteria] or P = 0.02 [ROC classification]), whereas high TERC GCN showed no association. In multivariate analysis including stage and age, high MYC GCN remained significantly associated with worse overall survival using both the UCCC criteria (P = 0.02) and the ROC classification (P = 0.008). Conclusions Our results confirm MYC as frequently amplified in early-stage NSCLC and increased MYC GCN as a strong predictor of worse survival. Increased TERC GCN does not have prognostic impact but has strong association with squamous histology. PMID:25806711

Expected p-values in light of an ROC curve analysis applied to optimal multiple testing procedures.

PubMed

Vexler, Albert; Yu, Jihnhee; Zhao, Yang; Hutson, Alan D; Gurevich, Gregory

2017-01-01

Many statistical studies report p-values for inferential purposes. In several scenarios, the stochastic aspect of p-values is neglected, which may contribute to drawing wrong conclusions in real data experiments. The stochastic nature of p-values makes their use to examine the performance of given testing procedures or associations between investigated factors to be difficult. We turn our focus on the modern statistical literature to address the expected p-value (EPV) as a measure of the performance of decision-making rules. During the course of our study, we prove that the EPV can be considered in the context of receiver operating characteristic (ROC) curve analysis, a well-established biostatistical methodology. The ROC-based framework provides a new and efficient methodology for investigating and constructing statistical decision-making procedures, including: (1) evaluation and visualization of properties of the testing mechanisms, considering, e.g. partial EPVs; (2) developing optimal tests via the minimization of EPVs; (3) creation of novel methods for optimally combining multiple test statistics. We demonstrate that the proposed EPV-based approach allows us to maximize the integrated power of testing algorithms with respect to various significance levels. In an application, we use the proposed method to construct the optimal test and analyze a myocardial infarction disease dataset. We outline the usefulness of the "EPV/ROC" technique for evaluating different decision-making procedures, their constructions and properties with an eye towards practical applications.

Mixture models in diagnostic meta-analyses--clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation.

PubMed

Schlattmann, Peter; Verba, Maryna; Dewey, Marc; Walther, Mario

2015-01-01

Bivariate linear and generalized linear random effects are frequently used to perform a diagnostic meta-analysis. The objective of this article was to apply a finite mixture model of bivariate normal distributions that can be used for the construction of componentwise summary receiver operating characteristic (sROC) curves. Bivariate linear random effects and a bivariate finite mixture model are used. The latter model is developed as an extension of a univariate finite mixture model. Two examples, computed tomography (CT) angiography for ruling out coronary artery disease and procalcitonin as a diagnostic marker for sepsis, are used to estimate mean sensitivity and mean specificity and to construct sROC curves. The suggested approach of a bivariate finite mixture model identifies two latent classes of diagnostic accuracy for the CT angiography example. Both classes show high sensitivity but mainly two different levels of specificity. For the procalcitonin example, this approach identifies three latent classes of diagnostic accuracy. Here, sensitivities and specificities are quite different as such that sensitivity increases with decreasing specificity. Additionally, the model is used to construct componentwise sROC curves and to classify individual studies. The proposed method offers an alternative approach to model between-study heterogeneity in a diagnostic meta-analysis. Furthermore, it is possible to construct sROC curves even if a positive correlation between sensitivity and specificity is present. Copyright © 2015 Elsevier Inc. All rights reserved.

Clinical evaluation of JPEG2000 compression for digital mammography

NASA Astrophysics Data System (ADS)

Sung, Min-Mo; Kim, Hee-Joung; Kim, Eun-Kyung; Kwak, Jin-Young; Yoo, Jae-Kyung; Yoo, Hyung-Sik

2002-06-01

Medical images, such as computed radiography (CR), and digital mammographic images will require large storage facilities and long transmission times for picture archiving and communications system (PACS) implementation. American College of Radiology and National Equipment Manufacturers Association (ACR/NEMA) group is planning to adopt a JPEG2000 compression algorithm in digital imaging and communications in medicine (DICOM) standard to better utilize medical images. The purpose of the study was to evaluate the compression ratios of JPEG2000 for digital mammographic images using peak signal-to-noise ratio (PSNR), receiver operating characteristic (ROC) analysis, and the t-test. The traditional statistical quality measures such as PSNR, which is a commonly used measure for the evaluation of reconstructed images, measures how the reconstructed image differs from the original by making pixel-by-pixel comparisons. The ability to accurately discriminate diseased cases from normal cases is evaluated using ROC curve analysis. ROC curves can be used to compare the diagnostic performance of two or more reconstructed images. The t test can be also used to evaluate the subjective image quality of reconstructed images. The results of the t test suggested that the possible compression ratios using JPEG2000 for digital mammographic images may be as much as 15:1 without visual loss or with preserving significant medical information at a confidence level of 99%, although both PSNR and ROC analyses suggest as much as 80:1 compression ratio can be achieved without affecting clinical diagnostic performance.

Association between Platelet Counts before and during Pharmacological Therapy for Patent Ductus Arteriosus and Treatment Failure in Preterm Infants.

PubMed

Sallmon, Hannes; Weber, Sven C; Dirks, Juliane; Schiffer, Tamara; Klippstein, Tamara; Stein, Anja; Felderhoff-Müser, Ursula; Metze, Boris; Hansmann, Georg; Bührer, Christoph; Cremer, Malte; Koehne, Petra

2018-01-01

The role of platelets for mediating closure of the ductus arteriosus in human preterm infants is controversial. Especially, the effect of low platelet counts on pharmacological treatment failure is still unclear. In this retrospective study of 471 preterm infants [<1,500 g birth weight (BW)], who were treated for a patent ductus arteriosus (PDA) with indomethacin or ibuprofen, we investigated whether platelet counts before or during pharmacological treatment had an impact on the successful closure of a hemodynamically significant PDA. The effects of other factors, such as sepsis, preeclampsia, gestational age, BW, and gender, were also evaluated. Platelet counts before initiation of pharmacological PDA treatment did not differ between infants with later treatment success or failure. However, we found significant associations between low platelet counts during pharmacological PDA therapy and treatment failure ( p  < 0.05). Receiver operating characteristic (ROC) curve analysis showed that platelet counts after the first, and before and after the second cyclooxygenase inhibitor (COXI) cycle were significantly associated with treatment failure (area under the curve of >0.6). However, ROC curve analysis did not reveal a specific platelet cutoff-value that could predict PDA treatment failure. Multivariate logistic regression analysis showed that lower platelet counts, a lower BW, and preeclampsia were independently associated with COXI treatment failure. We provide further evidence for an association between low platelet counts during pharmacological therapy for symptomatic PDA and treatment failure, while platelet counts before initiation of therapy did not affect treatment outcome.

Intact protein analysis of ubiquitin in cerebrospinal fluid by multiple reaction monitoring reveals differences in Alzheimer's disease and frontotemporal lobar degeneration.

PubMed

Oeckl, Patrick; Steinacker, Petra; von Arnim, Christine A F; Straub, Sarah; Nagl, Magdalena; Feneberg, Emily; Weishaupt, Jochen H; Ludolph, Albert C; Otto, Markus

2014-11-07

The impairment of the ubiquitin-proteasome system (UPS) is thought to be an early event in neurodegeneration, and monitoring UPS alterations might serve as a disease biomarker. Our aim was to establish an alternate method to antibody-based assays for the selective measurement of free monoubiquitin in cerebrospinal fluid (CSF). Free monoubiquitin was measured with liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MS/MS) in CSF of patients with Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), behavioral variant of frontotemporal dementia (bvFTD), Creutzfeldt-Jakob disease (CJD), Parkinson's disease (PD), primary progressive aphasia (PPA), and progressive supranuclear palsy (PSP). The LC-MS/MS method showed excellent intra- and interassay precision (4.4-7.4% and 4.9-10.3%) and accuracy (100-107% and 100-106%). CSF ubiquitin concentration was increased compared with that of controls (33.0 ± 9.7 ng/mL) in AD (47.5 ± 13.1 ng/mL, p < 0.05) and CJD patients (171.5 ± 103.5 ng/mL, p < 0.001) but not in other neurodegenerative diseases. Receiver operating characteristic curve (ROC) analysis of AD vs control patients revealed an area under the curve (AUC) of 0.832, and the specificity and sensitivity were 75 and 75%, respectively. ROC analysis of AD and FTLD patients yielded an AUC of 0.776, and the specificity and sensitivity were 53 and 100%, respectively. In conclusion, our LC-MS/MS method may facilitate ubiquitin determination to a broader community and might help to discriminate AD, CJD, and FTLD patients.

Tumour xenograft detection through quantitative analysis of the metabolic profile of urine in mice

NASA Astrophysics Data System (ADS)

Moroz, Jennifer; Turner, Joan; Slupsky, Carolyn; Fallone, Gino; Syme, Alasdair

2011-02-01

The metabolic content of urine from NIH III nude mice (n = 22) was analysed before and after inoculation with human glioblastoma multiforme (GBM) cancer cells. An age- and gender-matched control population (n = 14) was also studied to identify non-tumour-related changes. Urine samples were collected daily for 6 weeks, beginning 1 week before cell injection. Metabolite concentrations were obtained via targeted profiling with Chenomx Suite 5.1, based on nuclear magnetic resonance (NMR) spectra acquired on an Oxford 800 MHz cold probe NMR spectrometer. The Wilcoxon rank sum test was used to evaluate the significance of the change in metabolite concentration between the two time points. Both the metabolite concentrations and the ratios of pairs of metabolites were studied. The complicated inter-relationships between metabolites were assessed through partial least-squares discriminant analysis (PLS-DA). Receiver operating characteristic (ROC) curves were generated for all variables and the area under the curve (AUC) calculated. The data indicate that the number of statistically significant changes in metabolite concentrations was more pronounced in the tumour-bearing population than in the control animals. This was also true of the ratios of pairs of metabolites. ROC analysis suggests that the ratios were better able to differentiate between the pre- and post-injection samples compared to the metabolite concentrations. PLS-DA models produced good separation between the populations and had the best AUC results (all models exceeded 0.937). These results demonstrate that metabolomics may be used as a screening tool for GBM cells grown in xenograft models in mice.

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

NASA Astrophysics Data System (ADS)

Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun

2018-01-01

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

An explorative childhood pneumonia analysis based on ultrasonic imaging texture features

NASA Astrophysics Data System (ADS)

Zenteno, Omar; Diaz, Kristians; Lavarello, Roberto; Zimic, Mirko; Correa, Malena; Mayta, Holger; Anticona, Cynthia; Pajuelo, Monica; Oberhelman, Richard; Checkley, William; Gilman, Robert H.; Figueroa, Dante; Castañeda, Benjamín.

2015-12-01

According to World Health Organization, pneumonia is the respiratory disease with the highest pediatric mortality rate accounting for 15% of all deaths of children under 5 years old worldwide. The diagnosis of pneumonia is commonly made by clinical criteria with support from ancillary studies and also laboratory findings. Chest imaging is commonly done with chest X-rays and occasionally with a chest CT scan. Lung ultrasound is a promising alternative for chest imaging; however, interpretation is subjective and requires adequate training. In the present work, a two-class classification algorithm based on four Gray-level co-occurrence matrix texture features (i.e., Contrast, Correlation, Energy and Homogeneity) extracted from lung ultrasound images from children aged between six months and five years is presented. Ultrasound data was collected using a L14-5/38 linear transducer. The data consisted of 22 positive- and 68 negative-diagnosed B-mode cine-loops selected by a medical expert and captured in the facilities of the Instituto Nacional de Salud del Niño (Lima, Peru), for a total number of 90 videos obtained from twelve children diagnosed with pneumonia. The classification capacity of each feature was explored independently and the optimal threshold was selected by a receiver operator characteristic (ROC) curve analysis. In addition, a principal component analysis was performed to evaluate the combined performance of all the features. Contrast and correlation resulted the two more significant features. The classification performance of these two features by principal components was evaluated. The results revealed 82% sensitivity, 76% specificity, 78% accuracy and 0.85 area under the ROC.

Clinical Usefulness of Measuring Red Blood Cell Distribution Width in Patients with Hepatitis B Virus-Related Acute-On-Chronic Liver Failure.

PubMed

Jin, Lei; Gao, Yufeng; Ye, Jun; Zou, Guizhou; Li, Xu

2017-09-01

The red blood cell distribution width (RDW) is increased in chronic liver disease, but its clinical significance in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is still unclear. The aim of the present study was to investigate the clinical significance of RDW in HBV-ACLF patients. The medical records of HBV-ACLF patients who were admitted to The Second Affiliated Hospital of Anhui Medical University between April 2012 and December 2015 were retrospectively reviewed. Correlations between RDW, neutrophil lymphocyte ratio (NLR), and the model for end-stage liver disease (MELD) scores were analyzed using the Spearman's approach. Multivariable stepwise logistic regression test was used to evaluate independent clinical parameters predicting 3-month mortality of HBV-ACLF patients. The association between RDW and hospitalization outcome was estimated by receiver operating curve (ROC) analysis. Patient survival was estimated by Kaplan-Meier analysis and subsequently compared by log-rank test. Sixty-two HBV-ACLF patients and sixty CHB patients were enrolled. RDW were increased in HBVACLF patients and positively correlated with the NLR as well as MELD scores. Multivariate analysis demonstrated that RDW value was an independent predictor for mortality. RDW had an area under the ROC of 0.799 in predicting 3-month mortality of HBV-ACLF patients. Patients with HBV-ACLF who had RDW > 17% showed significantly poorer survival than those who had RDW ≤ 17%. RDW values are significantly increased in patients with HBV-ACLF. Moreover, RDW values are an independent predicting factor for an in-hospital mortality in patients with HBV-ACLF.

A comparison of ROC inferred from FROC and conventional ROC

NASA Astrophysics Data System (ADS)

McEntee, Mark F.; Littlefair, Stephen; Pietrzyk, Mariusz W.

2014-03-01

This study aims to determine whether receiver operating characteristic (ROC) scores inferred from free-response receiver operating characteristic (FROC) were equivalent to conventional ROC scores for the same readers and cases. Forty-five examining radiologists of the American Board of Radiology independently reviewed 47 PA chest radiographs under at least two conditions. Thirty-seven cases had abnormal findings and 10 cases had normal findings. Half the readers were asked to first locate any visualized lung nodules, mark them and assign a level of confidence [the FROC mark-rating pair] and second give an overall to the entire image on the same scale [the ROC score]. The second half of readers gave the ROC rating first followed by the FROC mark-rating pairs. A normal image was represented with number 1 and malignant lesions with numbers 2-5. A jackknife free-response receiver operating characteristic (JAFROC), and inferred ROC (infROC) was calculated from the mark-rating pairs using JAFROC V4.1 software. ROC based on the overall rating of the image calculated using DBM MRMC software, which was also used to compare infROC and ROC AUCs treating the methods as modalities. Pearson's correlations coefficient and linear regression were used to examine their relationship using SPSS, version 21.0; (SPSS, Chicago, IL). The results of this study showed no significant difference between the ROC and Inferred ROC AUCs (p≤0.25). While Pearson's correlation coefficient was 0.7 (p≤0.01). Inter-reader correlation calculated from Obuchowski- Rockette covariance's ranged from 0.43-0.86 while intra-reader agreement was greater than previously reported ranging from 0.68-0.82.

Statistical evidences of seismo-ionospheric precursors applying receiver operating characteristic (ROC) curve on the GPS total electron content in China

NASA Astrophysics Data System (ADS)

Chen, Yuh-Ing; Huang, Chi-Shen; Liu, Jann-Yenq

2015-12-01

Evidence of the seismo-ionospheric precursor (SIP) is reported by statistically investigating the relationship between the total electron content (TEC) in global ionosphere map (GIM) and 56 M ⩾ 6.0 earthquakes during 1998-2013 in China. A median-based method together with the z test is employed to examine the TEC variations 30 days before and after the earthquake. It is found that the TEC significantly decreases 0600-1000 LT 1-6 days before the earthquake, and anomalously increases in 3 time periods of 1300-1700 LT 12-15 days; 0000-0500 LT 15-17 days; and 0500-0900 LT 22-28 days before the earthquake. The receiver operating characteristic (ROC) curve is then used to evaluate the efficiency of TEC for predicting M ⩾ 6.0 earthquakes in China during a specified time period. Statistical results suggest that the SIP is the significant TEC reduction in the morning period of 0600-1000 LT. The SIP is further confirmed since the area under the ROC curve is positively associated with the earthquake magnitude.

A bivariate contaminated binormal model for robust fitting of proper ROC curves to a pair of correlated, possibly degenerate, ROC datasets.

PubMed

Zhai, Xuetong; Chakraborty, Dev P

2017-06-01

The objective was to design and implement a bivariate extension to the contaminated binormal model (CBM) to fit paired receiver operating characteristic (ROC) datasets-possibly degenerate-with proper ROC curves. Paired datasets yield two correlated ratings per case. Degenerate datasets have no interior operating points and proper ROC curves do not inappropriately cross the chance diagonal. The existing method, developed more than three decades ago utilizes a bivariate extension to the binormal model, implemented in CORROC2 software, which yields improper ROC curves and cannot fit degenerate datasets. CBM can fit proper ROC curves to unpaired (i.e., yielding one rating per case) and degenerate datasets, and there is a clear scientific need to extend it to handle paired datasets. In CBM, nondiseased cases are modeled by a probability density function (pdf) consisting of a unit variance peak centered at zero. Diseased cases are modeled with a mixture distribution whose pdf consists of two unit variance peaks, one centered at positive μ with integrated probability α, the mixing fraction parameter, corresponding to the fraction of diseased cases where the disease was visible to the radiologist, and one centered at zero, with integrated probability (1-α), corresponding to disease that was not visible. It is shown that: (a) for nondiseased cases the bivariate extension is a unit variances bivariate normal distribution centered at (0,0) with a specified correlation ρ 1 ; (b) for diseased cases the bivariate extension is a mixture distribution with four peaks, corresponding to disease not visible in either condition, disease visible in only one condition, contributing two peaks, and disease visible in both conditions. An expression for the likelihood function is derived. A maximum likelihood estimation (MLE) algorithm, CORCBM, was implemented in the R programming language that yields parameter estimates and the covariance matrix of the parameters, and other statistics. A limited simulation validation of the method was performed. CORCBM and CORROC2 were applied to two datasets containing nine readers each contributing paired interpretations. CORCBM successfully fitted the data for all readers, whereas CORROC2 failed to fit a degenerate dataset. All fits were visually reasonable. All CORCBM fits were proper, whereas all CORROC2 fits were improper. CORCBM and CORROC2 were in agreement (a) in declaring only one of the nine readers as having significantly different performances in the two modalities; (b) in estimating higher correlations for diseased cases than for nondiseased ones; and (c) in finding that the intermodality correlation estimates for nondiseased cases were consistent between the two methods. All CORCBM fits yielded higher area under curve (AUC) than the CORROC2 fits, consistent with the fact that a proper ROC model like CORCBM is based on a likelihood-ratio-equivalent decision variable, and consequently yields higher performance than the binormal model-based CORROC2. The method gave satisfactory fits to four simulated datasets. CORCBM is a robust method for fitting paired ROC datasets, always yielding proper ROC curves, and able to fit degenerate datasets. © 2017 American Association of Physicists in Medicine.

[Establishment of the prediction model for ischemic cardiovascular disease of elderly male population under current health care program].

PubMed

Chen, Jin-hong; Wu, Hai-yun; He, Kun-lun; He, Yao; Qin, Yin-he

2010-10-01

To establish and verify the prediction model for ischemic cardiovascular disease (ICVD) among the elderly population who were under the current health care programs. Statistical analysis on data from physical examination, hospitalization of the past years, from questionnaire and telephone interview was carried out in May, 2003. Data was from a hospital which implementing a health care program. Baseline population with a proportion of 4:1 was randomly selected to generate both module group and verification group. Baseline data was induced to make the verification group into regression model of module group and to generate the predictive value. Distinguished ability with area under ROC curve and the predictive veracity were verified through comparing the predictive incidence rate and actual incidence rate of every deciles group by Hosmer-Lemeshow test. Predictive veracity of the prediction model at population level was verified through comparing the predictive 6-year incidence rates of ICVD with actual 6-year accumulative incidence rates of ICVD with error rate calculated. The samples included 2271 males over the age of 65 with 1817 people for modeling population and 454 for verified population. All of the samples were stratified into two layers to establish hierarchical Cox proportional hazard regression model, including one advanced age group (greater than or equal to 75 years old), and another elderly group (less than 75 years old). Data from the statically analysis showed that the risk factors in aged group were age, systolic blood pressure, serum creatinine level, fasting blood glucose level, while protective factor was high density lipoprotein;in advanced age group, the risk factors were body weight index, systolic blood pressure, serum total cholesterol level, serum creatinine level, fasting blood glucose level, while protective factor was HDL-C. The area under the ROC curve (AUC) and 95%CI were 0.723 and 0.687 - 0.759 respectively. Discriminating power was good. All individual predictive ICVD cumulative incidence and actual incidence were analyzed using Hosmer-Lemeshow test, χ(2) = 1.43, P = 0.786, showing that the predictive veracity was good. The stratified Cox Hazards Regression model was used to establish prediction model of the aged male population under a certain health care program. The common prediction factor of the two age groups were: systolic blood pressure, serum creatinine level, fasting blood glucose level and HDL-C. The area under the ROC curve of the verification group was 0.723, showing that the distinguished ability was good and the predict ability at the individual level and at the group level were also satisfactory. It was feasible to using Cox Proportional Hazards Regression Model for predicting the population groups.

Mobile health technology transforms injury severity scoring in South Africa.

PubMed

Spence, Richard Trafford; Zargaran, Eiman; Hameed, S Morad; Navsaria, Pradeep; Nicol, Andrew

2016-08-01

The burden of data collection associated with injury severity scoring has limited its application in areas of the world with the highest incidence of trauma. Since January 2014, electronic records (electronic Trauma Health Records [eTHRs]) replaced all handwritten records at the Groote Schuur Hospital Trauma Unit in South Africa. Data fields required for Glasgow Coma Scale, Revised Trauma Score, Kampala Trauma Score, Injury Severity Score (ISS), and Trauma Score-Injury Severity Score calculations are now prospectively collected. Fifteen months after implementation of eTHR, the injury severity scores were compared as predictors of mortality on three accounts: (1) ability to discriminate (area under receiver operating curve, ROC); (2) ability to calibrate (observed versus expected ratio, O/E); and (3) feasibility of data collection (rate of missing data). A total of 7460 admissions were recorded by eTHR from April 1, 2014 to July 7, 2015, including 770 severely injured patients (ISS > 15) and 950 operations. The mean age was 33.3 y (range 13-94), 77.6% were male, and the mechanism of injury was penetrating in 39.3% of cases. The cohort experienced a mortality rate of 2.5%. Patient reserve predictors required by the scores were 98.7% complete, physiological injury predictors were 95.1% complete, and anatomic injury predictors were 86.9% complete. The discrimination and calibration of Trauma Score-Injury Severity Score was superior for all admissions (ROC 0.9591 and O/E 1.01) and operatively managed patients (ROC 0.8427 and O/E 0.79). In the severely injured cohort, the discriminatory ability of Revised Trauma Score was superior (ROC 0.8315), but no score provided adequate calibration. Emerging mobile health technology enables reliable and sustainable injury severity scoring in a high-volume trauma center in South Africa. Copyright © 2016 Elsevier Inc. All rights reserved.

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

PubMed

Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

2012-10-01

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

Feasibility of histogram analysis of susceptibility-weighted MRI for staging of liver fibrosis

PubMed Central

Yang, Zhao-Xia; Liang, He-Yue; Hu, Xin-Xing; Huang, Ya-Qin; Ding, Ying; Yang, Shan; Zeng, Meng-Su; Rao, Sheng-Xiang

2016-01-01

PURPOSE We aimed to evaluate whether histogram analysis of susceptibility-weighted imaging (SWI) could quantify liver fibrosis grade in patients with chronic liver disease (CLD). METHODS Fifty-three patients with CLD who underwent multi-echo SWI (TEs of 2.5, 5, and 10 ms) were included. Histogram analysis of SWI images were performed and mean, variance, skewness, kurtosis, and the 1st, 10th, 50th, 90th, and 99th percentiles were derived. Quantitative histogram parameters were compared. For significant parameters, further receiver operating characteristic (ROC) analyses were performed to evaluate the potential diagnostic performance for differentiating liver fibrosis stages. RESULTS The number of patients in each pathologic fibrosis grade was 7, 3, 5, 5, and 33 for F0, F1, F2, F3, and F4, respectively. The results of variance (TE: 10 ms), 90th percentile (TE: 10 ms), and 99th percentile (TE: 10 and 5 ms) in F0–F3 group were significantly lower than in F4 group, with areas under the ROC curves (AUCs) of 0.84 for variance and 0.70–0.73 for the 90th and 99th percentiles, respectively. The results of variance (TE: 10 and 5 ms), 99th percentile (TE: 10 ms), and skewness (TE: 2.5 and 5 ms) in F0–F2 group were smaller than those of F3/F4 group, with AUCs of 0.88 and 0.69 for variance (TE: 10 and 5 ms, respectively), 0.68 for 99th percentile (TE: 10 ms), and 0.73 and 0.68 for skewness (TE: 2.5 and 5 ms, respectively). CONCLUSION Magnetic resonance histogram analysis of SWI, particularly the variance, is promising for predicting advanced liver fibrosis and cirrhosis. PMID:27113421

Identifying clinically important difference on the Epworth Sleepiness Scale: results from a narcolepsy clinical trial of JZP-110.

PubMed

Scrima, Lawrence; Emsellem, Helene A; Becker, Philip M; Ruoff, Chad; Lankford, Alan; Bream, Gary; Khayrallah, Moise; Lu, Yuan; Black, Jed

2017-10-01

While scores ≤10 on the Epworth Sleepiness Scale (ESS) are within the normal range, the reduction in elevated ESS score that is clinically meaningful in patients with narcolepsy has not been established. This post hoc analysis of a clinical trial of patients with narcolepsy evaluated correlations between Patient Global Impression of Change (PGI-C) and ESS. Data of adult patients with narcolepsy from a double-blind, 12-week placebo-controlled study of JZP-110, a wake-promoting agent, were used in this analysis. Descriptive statistics and receiver operating characteristic (ROC) analysis compared PGI-C (anchor measure) to percent change from baseline in ESS to establish the responder criterion from patients taking either placebo or JZP-110 (treatments). At week 12, patients (n = 10) who reported being "very much improved" on the PGI-C had a mean 76.7% reduction in ESS score, and patients (n = 33) who reported being "much improved" on the PGI-C had a mean 49.1% reduction in ESS score. ROC analysis showed that patients who improved were almost exclusively from JZP-110 treatment group, with an area-under-the-curve of 0.9, and revealed that a 25% reduction in ESS (sensitivity, 81.4%; specificity, 80.9%) may be an appropriate threshold for defining a meaningful patient response to JZP-110 and placebo. A ≥25% reduction in patients' subjective ESS score may be useful as a threshold to identify patients with narcolepsy who respond to JZP-110 treatment. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Diagnostic utility of a one-item question to screen for depressive disorders: results from the KORA F3 study.

PubMed

Blozik, Eva; Scherer, Martin; Lacruz, Maria E; Ladwig, Karl-Heinz

2013-12-23

Screening for depressive disorders in the general adult population is recommended, however, it is unclear which instruments combine user friendliness and diagnostic utility. We evaluated the test performance of a yes/no single item screener for depressive disorders ("Have you felt depressed or sad much of the time in the past year?") in comparison to the depressive disorder module of the Patient Health Questionnaire (PHQ-9). Data from 3184 participants of the population-based KORA F3 survey in Augsburg/ Germany were used to analyse sensitivity, specificity, ROC area, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), and negative predictive value (NPV) of the single item screener in comparison with "depressive mood" and "major depressive disorder" defined according to PHQ-9 (both interviewer-administered versions). In comparison to PHQ-9 "depressive mood", sensitivity was low (46%) with an excellent specificity (94%), (PPV 76%; NPV 82%; LR + 8.04; LR- .572, ROC area .702). When using the more conservative definition for "major depressive disorder", sensitivity increased to 83% with a specificity of 88%. The PPV under the conservative definition was low (32%), but NPV was 99% (LR + 6.65; LR- .196; ROC area .852). Results varied across age groups and between males and females. The single item screener is able to moderately decrease post-test probability of major depressive disorders and to identify populations that should undergo additional, more detailed evaluation for depression. It may have limited utility in combination with additional screening tests or for selection of at-risk populations, but cannot be recommended for routine use as a screening tool in clinical practice.

Validation of an osteoporosis self-assessment tool to identify primary osteoporosis and new osteoporotic vertebral fractures in postmenopausal Chinese women in Beijing

PubMed Central

2013-01-01

Background This study aimed to validate the effectiveness of the Osteoporosis Self-assessment Tool for Asians (OSTA) in identifying postmenopausal women at increased risk of primary osteoporosis and painful new osteoporotic vertebral fractures in a large selected Han Chinese population in Beijing. Methods We assessed the performance of the OSTA in 1201 women. Subjects with an OSTA index > -1 were classified as the low risk group, and those with an index ≤ -1 were classified as the increased risk group. Osteoporosis is defined by a T-score ≤ 2.5 standard deviations according to the WHO criteria. All painful, new vertebral fractures were identified by X-ray and MRI scans with correlating clinical signs and symptoms. We determined the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve for correctly selecting women with osteoporosis and painful new vertebral fractures. Results Of the study subjects, 29.3% had osteoporosis, and the prevalence of osteoporosis increased progressively with age. The areas under the ROC curves of the OSTA index (cutoff = -1) to identify osteoporosis in the femoral neck, total hip, and lumbar spine were 0.824, 0.824, and 0.776, respectively. The sensitivity and specificity of the OSTA index (cutoff = -1) to identify osteoporosis in healthy women were 66% and 76%, respectively. With regard to painful new vertebral fractures, the area under the ROC curve relating the OSTA index (cutoff = -1) to new vertebral fractures was 0.812. Conclusions The OSTA may be a simple and effective tool for identifying the risk of osteoporosis and new painful osteoporotic vertebral fractures in Han Chinese women. PMID:24053509

Diagnostic utility of a one-item question to screen for depressive disorders: results from the KORA F3 study

PubMed Central

2013-01-01

Background Screening for depressive disorders in the general adult population is recommended, however, it is unclear which instruments combine user friendliness and diagnostic utility. We evaluated the test performance of a yes/no single item screener for depressive disorders (“Have you felt depressed or sad much of the time in the past year?”) in comparison to the depressive disorder module of the Patient Health Questionnaire (PHQ-9). Methods Data from 3184 participants of the population-based KORA F3 survey in Augsburg/ Germany were used to analyse sensitivity, specificity, ROC area, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), and negative predictive value (NPV) of the single item screener in comparison with “depressive mood” and “major depressive disorder” defined according to PHQ-9 (both interviewer-administered versions). Results In comparison to PHQ-9 “depressive mood”, sensitivity was low (46%) with an excellent specificity (94%), (PPV 76%; NPV 82%; LR + 8.04; LR- .572, ROC area .702). When using the more conservative definition for “major depressive disorder”, sensitivity increased to 83% with a specificity of 88%. The PPV under the conservative definition was low (32%), but NPV was 99% (LR + 6.65; LR- .196; ROC area .852). Results varied across age groups and between males and females. Conclusions The single item screener is able to moderately decrease post-test probability of major depressive disorders and to identify populations that should undergo additional, more detailed evaluation for depression. It may have limited utility in combination with additional screening tests or for selection of at-risk populations, but cannot be recommended for routine use as a screening tool in clinical practice. PMID:24359193

Summary Report of a Study to Assist in the Development of a Regional Occupational Center System in Tulare and Kings Counties

ERIC Educational Resources Information Center

Tadlock, Max; And Others

A study by Management and Economic Research, Inc. (MERI) of the occupational education in a 2-county area analyzed employer and student needs and existing facilities. To reduce wasteful competition, it recommended a change from local to area planning and the organization of a Regional Occupational Center (ROC) System with subsystems in contiguous…

Potential Role of Circulating MiR-21 in the Diagnosis and Prognosis of Digestive System Cancer: A Systematic Review and Meta-Analysis.

PubMed

Yin, Chengqiang; Zhou, Xiaoying; Dang, Yini; Yan, Jin; Zhang, Guoxin

2015-12-01

Recent evidences indicate that circulating microRNAs (miRNAs) exhibit aberrant expression in the plasma of patients suffering from cancer compared to normal individuals, suggesting that it may be a useful noninvasion diagnostic method. MiR-21 plays crucial roles in carcinogenesis and can be served as a biomarker for the detection of various cancers. Therefore, the aim of this meta-analysis is to assess the potential role of miR-21 for digestive system cancer. By searching the PubMed, Embase, and Web of Science for publications concerning the diagnostic value of miR-21 for digestive system cancer, total of 23 publications were included in this meta-analysis. Receiver operating characteristic curves (ROC) were used to check the overall test performance. For prognostic meta-analysis, pooled hazard ratios (HRs) of circulating miR-21 for survival were calculated. Totally 23 eligible publications were included in this meta-analysis (15 articles for diagnosis and 8 articles for prognosis). For diagnostic meta-analysis, the summary estimates revealed that the pooled sensitivity and specificity were 0.76 (95% CI = 0.70-0.82) and 0.84 (95% CI = 0.78-0.89). Besides, the area under the summary ROC curve (AUC) is 0.87. For prognostic meta-analysis, the pooled HR of higher miR-21 expression in circulation was 1.94 (95% CI = 0.99-3.82, P = 0.055), which indicated higher miR-21 expression could be likely to predict poorer survival in digestive system cancer. The subgroup analysis implied the higher expression of miR-21 was correlated with worse overall survival in the Asian population in digestive system cancer (HR = 2.41, 95% CI = 1.21-4.77, P = 0.012). The current evidence suggests circulating miR-21 may be suitable to be a diagnostic and prognostic biomarker for digestive system cancer in the Asians.

Usefulness of Cellular Analysis of Bronchoalveolar Lavage Fluid for Predicting the Etiology of Pneumonia in Critically Ill Patients

PubMed Central

Hong, Hyo-Lim; Kim, Sung-Han; Huh, Jin Won; Sung, Heungsup; Lee, Sang-Oh; Kim, Mi-Na; Jeong, Jin-Yong; Lim, Chae-Man; Kim, Yang Soo; Woo, Jun Hee; Koh, Younsuck

2014-01-01

Background The usefulness of bronchoalveolar lavage (BAL) fluid cellular analysis in pneumonia has not been adequately evaluated. This study investigated the ability of cellular analysis of BAL fluid to differentially diagnose bacterial pneumonia from viral pneumonia in adult patients who are admitted to intensive care unit. Methods BAL fluid cellular analysis was evaluated in 47 adult patients who underwent bronchoscopic BAL following less than 24 hours of antimicrobial agent exposure. The abilities of BAL fluid total white blood cell (WBC) counts and differential cell counts to differentiate between bacterial and viral pneumonia were evaluated using receiver operating characteristic (ROC) curve analysis. Results Bacterial pneumonia (n = 24) and viral pneumonia (n = 23) were frequently associated with neutrophilic pleocytosis in BAL fluid. BAL fluid median total WBC count (2,815/µL vs. 300/µL, P<0.001) and percentage of neutrophils (80.5% vs. 54.0%, P = 0.02) were significantly higher in the bacterial pneumonia group than in the viral pneumonia group. In ROC curve analysis, BAL fluid total WBC count showed the best discrimination, with an area under the curve of 0.855 (95% CI, 0.750–0.960). BAL fluid total WBC count ≥510/µL had a sensitivity of 83.3%, specificity of 78.3%, positive likelihood ratio (PLR) of 3.83, and negative likelihood ratio (NLR) of 0.21. When analyzed in combination with serum procalcitonin or C-reactive protein, sensitivity was 95.8%, specificity was 95.7%, PLR was 8.63, and NLR was 0.07. BAL fluid total WBC count ≥510/µL was an independent predictor of bacterial pneumonia with an adjusted odds ratio of 13.5 in multiple logistic regression analysis. Conclusions Cellular analysis of BAL fluid can aid early differential diagnosis of bacterial pneumonia from viral pneumonia in critically ill patients. PMID:24824328

AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

PubMed

Sun, Lei; Wang, Jun; Wei, Jinmao

2017-03-14

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.

PubMed

Huynh, Benjamin Q; Li, Hui; Giger, Maryellen L

2016-07-01

Convolutional neural networks (CNNs) show potential for computer-aided diagnosis (CADx) by learning features directly from the image data instead of using analytically extracted features. However, CNNs are difficult to train from scratch for medical images due to small sample sizes and variations in tumor presentations. Instead, transfer learning can be used to extract tumor information from medical images via CNNs originally pretrained for nonmedical tasks, alleviating the need for large datasets. Our database includes 219 breast lesions (607 full-field digital mammographic images). We compared support vector machine classifiers based on the CNN-extracted image features and our prior computer-extracted tumor features in the task of distinguishing between benign and malignant breast lesions. Five-fold cross validation (by lesion) was conducted with the area under the receiver operating characteristic (ROC) curve as the performance metric. Results show that classifiers based on CNN-extracted features (with transfer learning) perform comparably to those using analytically extracted features [area under the ROC curve [Formula: see text

Optimal joint detection and estimation that maximizes ROC-type curves

PubMed Central

Wunderlich, Adam; Goossens, Bart; Abbey, Craig K.

2017-01-01

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation. PMID:27093544

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.

PubMed

Wunderlich, Adam; Goossens, Bart; Abbey, Craig K

2016-09-01

Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.

[The application of cortical and subcortical stimulation threshold in identifying the motor pathway and guiding the resection of gliomas in the functional areas].

PubMed

Ren, X H; Yang, X C; Huang, W; Yang, K Y; Liu, L; Qiao, H; Guo, L J; Cui, Y; Lin, S

2018-03-06

Objective: This study aimed to analyze the application of cortical and subcortical stimulation threshold in identifying the motor pathway and guiding the resection of gliomas in the functional area, and to illustrate the minimal safe threshold by ROC method. Methods: Fifty-seven patients with gliomas in the functional areas were enrolled in the study at Beijing Tiantan Hospital from 2015 to 2017. Anesthesia was maintained intravenously with propofol 10% and remifentanil. Throughout the resection process, cortical or subcortical stimulation threshold was determined along tumor border using monopolar or bipolar electrodes. The motor pathway was identified and protected from resection according to the stimulation threshold and transcranial MEPs. Minimal threshold in each case was recorded. Results: Total resection was achieved in 32 cases(56.1%), sub-total resection in 22 cases(38.6%), and partial resection in 3 cases(5.3%). Pre-operative motor disability was found in 9 cases. Compared with pre-operative motor scores, 19 exhibited impaired motor functions on day 1 after surgery, 5 had quick recovery by day 7 after surgery, and 7 had late recovery by 3 months after surgery. At 3 months, 7 still had impaired motor function. The frequency of intraoperative seizure was 1.8%(1/57). No other side effect was found during electronic monitoring in the operation. The ROC curve revealed that the minimal safe monopolar subcortical threshold was 5.70 mA for strength deterioration on day 1 and day 7 after surgery. Univariate analysis revealed that decreased transcranial MEPs and minimal subcortical threshold ≤5.7 mA were correlated with postoperative strength deterioration. Conclusions: Cortical and subcortical stimulation threshold has its merit in identifying the motor pathway and guiding the resection for tumors within the functional areas. 5.7 mA can be used as the minimal safe threshold to protect the motor pathway from injury.

Optimal Hemoglobin A1c Levels for Screening of Diabetes and Prediabetes in the Japanese Population.

PubMed

Shimodaira, Masanori; Okaniwa, Shinji; Hanyu, Norinao; Nakayama, Tomohiro

2015-01-01

The aim of this study was to evaluate the utility of hemoglobin A1c (HbA1c) to identify individuals with diabetes and prediabetes in the Japanese population. A total of 1372 individuals without known diabetes were selected for this study. A 75 g oral glucose tolerance test (OGTT) was used to diagnose diabetes and prediabetes. The ability of HbA1c to detect diabetes and prediabetes was investigated using receiver operating characteristic (ROC) analysis. The kappa (κ) coefficient was used to test the agreement between HbA1c categorization and OGTT-based diagnosis. ROC analysis demonstrated that HbA1c was a good test to identify diabetes and prediabetes, with areas under the curve of 0.918 and 0.714, respectively. Optimal HbA1c cutoffs for diagnosing diabetes and prediabetes were 6.0% (sensitivity 83.7%, specificity 87.6%) and 5.7% (sensitivity 60.6%, specificity 72.1%), respectively, although the cutoff for prediabetes showed low accuracy (67.6%) and a high false-negative rate (39.4%). Agreement between HbA1c categorization and OGTT-based diagnosis was low in diabetes (κ = 0.399) and prediabetes (κ = 0.324). In Japanese subjects, the HbA1c cutoff of 6.0% had appropriate sensitivity and specificity for diabetes screening, whereas the cutoff of 5.7% had modest sensitivity and specificity in identifying prediabetes. Thus, HbA1c may be inadequate as a screening tool for prediabetes.

Association between promoter methylation of MLH1 and MSH2 and reactive oxygen species in oligozoospermic men-A pilot study.

PubMed

Gunes, S; Agarwal, A; Henkel, R; Mahmutoglu, A M; Sharma, R; Esteves, S C; Aljowair, A; Emirzeoglu, D; Alkhani, A; Pelegrini, L; Joumah, A; Sabanegh, E

2018-04-01

MLH1 and MSH2 are important genes for DNA mismatch repair and crossing over during meiosis and are implicated in male infertility. Therefore, the methylation patterns of the DNA mismatch repair genes MLH1 and MSH2 in oligozoospermic males were investigated. Ten oligozoospermic patients and 29 normozoospermic donors were analysed. Methylation profiles of the MLH1 and MSH2 promotors were analysed. In addition, sperm motility and seminal reactive oxygen species (ROS) were recorded. Receiver operating characteristic (ROC) analysis was conducted to determine the accuracy of the DNA methylation status of MLH1 and MSH2 to distinguish between oligozoospermic and normozoospermic men. In oligozoospermic men, MLH1 was significantly (p = .0013) more methylated compared to normozoospermic men. Additionally, there was a significant positive association (r = .384; p = .0159) between seminal ROS levels and MLH1 methylation. Contrary, no association between MSH2 methylation and oligozoospermia was found. ROC curve analysis for methylation status of MLH1 was significant (p = .0275) with an area under the curve of 61.1%, a sensitivity of 22.2% and a specificity of 100.0%. This pilot study indicates oligozoospermic patients have more methylation of MLH1 than normozoospermic patients. Whether hypermethylation of the MLH1 promoter plays a role in repairing relevant mismatches of sperm DNA strands in idiopathic oligozoospermia warrants further investigation. © 2017 Blackwell Verlag GmbH.

Predicting malignant and tuberculous pleural effusions through demographics and pleural fluid analysis of patients.

PubMed

Valdés, Luis; San-José, Esther; Ferreiro, Lucía; Golpe, Antonio; González-Barcala, Francisco-Javier; Toubes, María E; Rodríguez-Álvarez, María X; Álvarez-Dobaño, José M; Rodríguez-Núñez, Nuria; Rábade, Carlos; Gude, Francisco

2015-04-01

The differential diagnosis of malignant and tuberculous pleural effusion is frequently difficult. The aim of our study is to determine the discrimination value of demographic parameters and different biological markers in pleural fluid. In pleural fluid obtained from 106 patients with tuberculous, 250 with malignant and 218 with miscellaneous pleural effusion, clinical and analytical parameters were analysed, applying polytomous regression analysis and the receiver operating characteristic (ROC) curves. The three groups could be differentiated using the measured markers. Age, tumour necrosing factor-alpha, lactate dehydrogenase (LDH), adenosine deaminase (ADA), C-reactive protein (CRP) and carcinoembryonic antigen (CEA) were significant predictors for discriminating tuberculous from malignant pleural effusions; nucleated cells, lymphocytes, cholesterol, LDH, ADA, CRP, CEA and CA15.3 distinguish between malignant and miscellaneous pleural effusions. The ROC areas (95% confidence interval) were, 0.973 (0.953, 0.992) for tuberculous, 0.922 (0.900, 0.943) for miscellaneous, and 0.927 (0.907, 0.948) for malignant pleural effusion. The polytomous model correctly classified a significantly high proportion of patients with tuberculosis (85.8%) and cancer (81.6%). The incorrect classification rate was 17.8%, which increased to 19.5% in the correction using bootstrap. The results obtained to estimate the probability of tuberculous and malignant pleural effusion confirm that this model achieves a high diagnostic accuracy. This model should be applied to determine which patients with a pleural effusion of unknown origin would not benefit from further invasive procedures. © 2014 John Wiley & Sons Ltd.

Estimating mortality risk in preoperative patients using immunologic, nutritional, and acute-phase response variables.

PubMed Central

Christou, N V; Tellado-Rodriguez, J; Chartrand, L; Giannas, B; Kapadia, B; Meakins, J; Rode, H; Gordon, J

1989-01-01

We measured the delayed type hypersensitivity (DTH) skin test response, along with additional variables of host immunocompetence in 245 preoperative patients to determine which variables are associated with septic-related deaths following operation. Of the 14 deaths (5.7%), 12 were related to sepsis and in 2 sepsis was contributory. The DTH response (p less than 0.00001), age (p less than 0.0002), serum albumin (p less than 0.003), hemoglobin (p less than 0.02), and total hemolytic complement (p less than 0.03), were significantly different between those who died and those who lived. By logistic regression analysis, only the DTH skin test response (log likelihood = 41.7, improvement X2 = 6.24, p less than 0.012) and the serum albumin (log likelihood = 44.8, improvement X2 = 17.7, p less than 0.001) were significantly and independently associated with the deaths. The resultant probability of mortality calculation equation was tested in a separate validation group of 519 patients (mortality = 5%) and yielded a good predictive capability as assessed by (1) X2 = 0.08 between observed and expected deaths, NS; (2) Goodman-Kruskall G statistic = 0.673) Receiver-Operating-Characteristic (ROC) curve analysis with an area under the ROC curve, Az = 0.79 +/- 0.05. We conclude that a reduced immune response (DTH skin test anergy) plus a nutritional deficit and/or acute-phase response change are both associated with increased septic-related deaths in elective surgical patients. PMID:2472781

Contrast-enhanced spectral mammography improves diagnostic accuracy in the symptomatic setting.

PubMed

Tennant, S L; James, J J; Cornford, E J; Chen, Y; Burrell, H C; Hamilton, L J; Girio-Fragkoulakis, C

2016-11-01

To assess the diagnostic accuracy of contrast-enhanced spectral mammography (CESM), and gauge its "added value" in the symptomatic setting. A retrospective multi-reader review of 100 consecutive CESM examinations was performed. Anonymised low-energy (LE) images were reviewed and given a score for malignancy. At least 3 weeks later, the entire examination (LE and recombined images) was reviewed. Histopathology data were obtained for all cases. Differences in performance were assessed using receiver operator characteristic (ROC) analysis. Sensitivity, specificity, and lesion size (versus MRI or histopathology) differences were calculated. Seventy-three percent of cases were malignant at final histology, 27% were benign following standard triple assessment. ROC analysis showed improved overall performance of CESM over LE alone, with area under the curve of 0.93 versus 0.83 (p<0.025). CESM showed increased sensitivity (95% versus 84%, p<0.025) and specificity (81% versus 63%, p<0.025) compared to LE alone, with all five readers showing improved accuracy. Tumour size estimation at CESM was significantly more accurate than LE alone, the latter tending to undersize lesions. In 75% of cases, CESM was deemed a useful or significant aid to diagnosis. CESM provides immediately available, clinically useful information in the symptomatic clinic in patients with suspicious palpable abnormalities. Radiologist sensitivity, specificity, and size accuracy for breast cancer detection and staging are all improved using CESM as the primary mammographic investigation. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

The Total Urine Protein-to-Creatinine Ratio Can Predict the Presence of Microalbuminuria

PubMed Central

Yamamoto, Kyoko; Yamamoto, Hiroyuki; Yoshida, Katsumi; Niwa, Koichiro; Nishi, Yutaro; Mizuno, Atsushi; Kuwabara, Masanari; Asano, Taku; Sakoda, Kunihiro; Niinuma, Hiroyuki; Nakahara, Fumiko; Takeda, Kyoko; Shindoh, Chiyohiko; Komatsu, Yasuhiro

2014-01-01

Background The Kidney Disease: Improving Global Outcomes chronic kidney disease (CKD) guidelines recommend that CKD be classified based on the etiology, glomerular filtration rate (GFR) and degree of albuminuria. The present study aimed to establish a method that predicts the presence of microalbuminuria by measuring the total urine protein-to-creatinine ratio (TPCR) in patients with cardiovascular disease (CVD) risk factors. Methods and Results We obtained urine samples from 1,033 patients who visited the cardiovascular clinic at St. Luke's International Hospital from February 2012 to August 2012. We measured the TPCR and the urine albumin-to-creatinine ratio (ACR) from random spot urine samples. We performed correlation, receiver operating characteristic (ROC) curve, sensitivity, and subgroup analyses. There was a strong positive correlation between the TPCR and ACR (R2 = 0.861, p<0.001). A ROC curve analysis for the TPCR revealed a sensitivity of 94.4%, a specificity of 86.1%, and an area under the curve of 0.903 for detecting microalbuminuria for a TPCR cut-off value of 84 mg/g of creatinine. The subgroup analysis indicated that the cut-off value could be used for patients with CVD risk factors. Conclusions These results suggest that the TPCR with an appropriate cut-off value could be used to screen for the presence of microalbuminuria in patients with CVD risk factors. This simple, inexpensive measurement has broader applications, leading to earlier intervention and public benefit. PMID:24614247

Pancreatic thickness as a predictive factor for postoperative pancreatic fistula after distal pancreatectomy using an endopath stapler.

PubMed

Okano, Keiichi; Oshima, Minoru; Kakinoki, Keitaro; Yamamoto, Naoki; Akamoto, Shintaro; Yachida, Shinichi; Hagiike, Masanobu; Kamada, Hideki; Masaki, Tsutomu; Suzuki, Yasuyuki

2013-02-01

No consistent risk factor has yet been established for the development of pancreatic fistula (PF) after distal pancreatectomy (DP) with a stapler. A total of 31 consecutive patients underwent DP with an endopath stapler between June 2006 and December 2010 using a slow parenchymal flattening technique. The risk factors for PF after DP with an endopath stapler were identified based on univariate and multivariate analyses. Clinical PF developed in 7 of 31 (22 %) patients who underwent DP with a stapler. The pancreata were significantly thicker at the transection line in patients with PF (19.4 ± 1.47 mm) in comparison to patients without PF (12.6 ± 0.79 mm; p = 0.0003). A 16-mm cut-off for pancreatic thickness was established based on the receiver operating characteristic (ROC) curve; the area under the ROC curve was 0.875 (p = 0.0215). Pancreatic thickness (p = 0.0006) and blood transfusion (p = 0.028) were associated with postoperative PF in a univariate analysis. Pancreatic thickness was the only significant independent factor (odds ratio 9.99; p = 0.036) according to a multivariate analysis with a specificity of 72 %, and a sensitivity of 85 %. Pancreatic thickness is a significant independent risk factor for PF development after DP with an endopath stapler. The stapler technique is thus considered to be an appropriate modality in patients with a pancreatic thicknesses of <16 mm.

Post Hoc Analysis of Passive Cavitation Imaging for Classification of Histotripsy-Induced Liquefaction in Vitro.

PubMed

Bader, Kenneth B; Haworth, Kevin J; Maxwell, Adam D; Holland, Christy K

2018-01-01

Histotripsy utilizes focused ultrasound to generate bubble clouds for transcutaneous tissue liquefaction. Bubble activity maps are under development to provide image guidance and monitor treatment progress. The aim of this paper was to investigate the feasibility of using plane wave B-mode and passive cavitation images to be used as binary classifiers of histotripsy-induced liquefaction. Prostate tissue phantoms were exposed to histotripsy pulses over a range of pulse durations (5- ) and peak negative pressures (12-23 MPa). Acoustic emissions were recorded during the insonation and beamformed to form passive cavitation images. Plane wave B-mode images were acquired following the insonation to detect the hyperechoic bubble cloud. Phantom samples were sectioned and stained to delineate the liquefaction zone. Correlation between passive cavitation and plane wave B-mode images and the liquefaction zone was assessed using receiver operating characteristic (ROC) curve analysis. Liquefaction of the phantom was observed for all the insonation conditions. The area under the ROC (0.94 versus 0.82), accuracy (0.90 versus 0.83), and sensitivity (0.81 versus 0.49) was greater for passive cavitation images relative to B-mode images ( ) along the azimuth of the liquefaction zone. The specificity was greater than 0.9 for both imaging modalities. These results demonstrate a stronger correlation between histotripsy-induced liquefaction and passive cavitation imaging compared with the plane wave B-mode imaging, albeit with limited passive cavitation image range resolution.

Screening instruments for a population of older adults: The 10-item Kessler Psychological Distress Scale (K10) and the 7-item Generalized Anxiety Disorder Scale (GAD-7).

PubMed

Vasiliadis, Helen-Maria; Chudzinski, Veronica; Gontijo-Guerra, Samantha; Préville, Michel

2015-07-30

Screening tools that appropriately detect older adults' mental disorders are of great public health importance. The present study aimed to establish cutoff scores for the 10-item Kessler Psychological Distress (K10) and the 7-item Generalized Anxiety Disorder (GAD-7) scales when screening for depression and anxiety. We used data from participants (n = 1811) in the Enquête sur la Santé des Aînés-Service study. Depression and anxiety were measured using DSM-V and DSM-IV criteria. Receiver operating characteristic (ROC) curve analysis provided an area under the curve (AUC) of 0.767 and 0.833 for minor and for major depression when using K10. A cutoff of 19 was found to balance sensitivity (0.794) and specificity (0.664) for minor depression, whereas a cutoff of 23 was found to balance sensitivity (0.692) and specificity (0.811) for major depression. When screening for an anxiety with GAD-7, ROC analysis yielded an AUC of 0.695; a cutoff of 5 was found to balance sensitivity (0.709) and specificity (0.568). No significant differences were found between subgroups of age and gender. Both K10 and GAD-7 were able to discriminate between cases and non-cases when screening for depression and anxiety in an older adult population of primary care service users. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

New scoring system combining the FIB-4 index and cytokeratin-18 fragments for predicting steatohepatitis and liver fibrosis in patients with nonalcoholic fatty liver disease.

PubMed

Tada, Toshifumi; Kumada, Takashi; Toyoda, Hidenori; Saibara, Toshiji; Ono, Masafumi; Kage, Masayoshi

To establish a new scoring system as a noninvasive tool for predicting steatohepatitis and liver fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). A total of 170 patients histologically diagnosed with nonalcoholic steatohepatitis (NASH) (n = 130) or nonalcoholic fatty liver (NAFL) (n = 40) were enrolled. We analyzed receiver operating characteristic (ROC) curves and performed multivariate analysis to predict steatohepatitis and liver fibrosis. Multivariate analysis showed that cytokeratin-18 fragment (CK18-F) levels (≥278 U/L) (odds ratio [OR], 4.46; 95% confidence interval [CI], 1.42-14.00; p = 0.010) and the FIB-4 index (≥1.46) (OR, 4.54; 95% CI, 1.93-29.50; p = 0.004) were independently associated with prediction of NASH. We then established a new scoring system (named the FIC-22 score) for predicting NASH using CK18-F levels and FIB-4 index. The areas under the ROC curve (AUROCs) of the FIC-22 score and NAFIC score were 0.82 (95% CI, 0.75-0.89) and 0.71 (95% CI, 0.62-0.78) (p = 0.044). Additionally, the AUROC of the FIC-22 score for predicting the presence of fibrosis (F ≥ 1) was 0.78 (95% CI, 0.70-0.85). In patients with NAFLD, the FIC-22 score had high predictive accuracy not only for steatohepatitis but also for the presence of liver fibrosis.

Development and Validation of a Disease Severity Scoring Model for Pediatric Sepsis.

PubMed

Hu, Li; Zhu, Yimin; Chen, Mengshi; Li, Xun; Lu, Xiulan; Liang, Ying; Tan, Hongzhuan

2016-07-01

Multiple severity scoring systems have been devised and evaluated in adult sepsis, but a simplified scoring model for pediatric sepsis has not yet been developed. This study aimed to develop and validate a new scoring model to stratify the severity of pediatric sepsis, thus assisting the treatment of sepsis in children. Data from 634 consecutive patients who presented with sepsis at Children's hospital of Hunan province in China in 2011-2013 were analyzed, with 476 patients placed in training group and 158 patients in validation group. Stepwise discriminant analysis was used to develop the accurate discriminate model. A simplified scoring model was generated using weightings defined by the discriminate coefficients. The discriminant ability of the model was tested by receiver operating characteristic curves (ROC). The discriminant analysis showed that prothrombin time, D-dimer, total bilirubin, serum total protein, uric acid, PaO2/FiO2 ratio, myoglobin were associated with severity of sepsis. These seven variables were assigned with values of 4, 3, 3, 4, 3, 3, 3 respectively based on the standardized discriminant coefficients. Patients with higher scores had higher risk of severe sepsis. The areas under ROC (AROC) were 0.836 for accurate discriminate model, and 0.825 for simplified scoring model in validation group. The proposed disease severity scoring model for pediatric sepsis showed adequate discriminatory capacity and sufficient accuracy, which has important clinical significance in evaluating the severity of pediatric sepsis and predicting its progress.

Is being female a risk factor for shallow anterior chamber? The associations between anterior chamber depth and age, sex, and body height

PubMed Central

Hsu, Wei-Cherng; Shen, Elizabeth P; Hsieh, Yi-Ting

2014-01-01

Aim of Study: To analyze the association between anterior chamber depth (ACD) and age, sex, and body height (BH). Materials and Methods: One thousand four hundred eighty eyes of 1480 adults 40 years of age and older receiving preoperative evaluation for cataract surgery were recruited consecutively from June 1, 2006, to December 31, 2010. ACD was measured with the Zeiss IOLMaster. Univariate and multivariate linear regression models were used to analyze the correlations, and receiving operator characteristic (ROC) curves and the area under the curve (AUC) were used for evaluating the predictability of an ACD less than 2.70 mm. Results: ACD was negatively correlated with age and positively correlated with BH in both univariate and multivariate regression analysis (P < 0.001). Sex was associated with ACD in univariate analysis, but not after adjustment with age and BH. In predicting an ACD less than 2.70 mm, the AUCs of ROC curves for ‘age and sex’, ‘age and BH’, and ‘age, sex, and BH’ were 0.687, 0.689, and 0.689, respectively. Conclusion: Age and BH were independent associating factors of ACD; however, sex was not. Older people and shorter ones likely had shallower ACD, and therefore were predisposed to Primary angle closure glaucoma (PACG). The predictability of ACD by age and BH solely was low, and adding sex did not increase it. PMID:24145564

Utilizing Social Media for Community Consultation and Public Disclosure in Exception from Informed Consent Trials

PubMed Central

Stephens, Shannon W.; Williams, Carolyn; Gray, Randal; Kerby, Jeffrey D.; Wang, Henry E.; Bosarge, Patrick L.

2016-01-01

Background The U.S. Food and Drug Administration and Department of Health and Human Services outline regulations allowing an Exception From Informed Consent (EFIC) for research conducted in an emergency settings. Acute care clinical trials utilizing EFIC must include community consultation and public disclosure (CC/PD) activities. We describe our experience using social media to facilitate the CC/PD process in two trauma resuscitation clinical trials. Methods We conducted local CC/PD activities for two multicenter trauma clinical trials, Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) and Prehospital Tranexamic Acid Use for Traumatic Brain Injury (ROC TXA). As part of the CC/PD process, we developed research study advertisements using the social media website Facebook. The Facebook advertisements directed users to a regional study website that contained trial information. We targeted the advertisements to specific demographic users, in specific geographic areas. We analyzed the data using descriptive statistics. Results During the study periods, the PROPPR Facebook advertisement was displayed 5,001,520 times, (12 displays per target population) with 374 individuals selected the advertisement. The ROC-TXA Facebook advertisement was displayed 3,806,448 times (8 per target population) with 790 individuals selecting the advertisement. Respondents to both Facebook advertisements were mostly male (52.6%), with the highest proportion between the ages 15-24 (28.2%). Collectively, 26.9% of individuals that clicked on the Facebook advertisement, spent > 3 minutes on the study website [3min – 49 min]. Commonly accessed webpages were “Contact Us” (PROPPR 5.5%, TXA 7.7%), “Study-specific FAQs” (PROPPR 2.4%), ROC-TXA 6.7%) and “Opt-Out of Research” (PROPPR 2.5%, ROC-TXA 3.8%). Of 51 total individuals viewing the opt-out of research information (PROPPR 19, ROC-TXA 32), Time spent on that specific page was modest (PROPPR 62 seconds, ROC-TXA 55 seconds), with no individuals requesting to opt-out of either study participation. Conclusion In clinical trauma trials, using EFIC, social media may provide a viable option for facilitating the CC/PD process. Level Of Evidence Descriptive Study, Level IV. PMID:26998781

Investigation of Three-Group Classifiers to Fully Automate Detection and Classification of Breast Lesions in an Intelligent CAD Mammography Workstation

DTIC Science & Technology

2007-05-01

evaluation of approximations,” tech. rep., Dep. Sistemes Informàtics i Computació, Univ. Politècnica de València (Spain), 2003. [7] D. C. Edwards, C. E...Maryellen L. Giger, scientific collaborator • Lorenzo Pesce, computer programmer 16 C The Hypervolume under the ROC Hypersurface of “Near-Guessing...the simple model we have just described corresponds in the two-class classification task to ROC analysis performed ‘‘per ARTICLE IN PRESS

A simple diagnostic model for ruling out pneumoconiosis among construction workers.

PubMed

Suarthana, Eva; Moons, Karel G M; Heederik, Dick; Meijer, Evert

2007-09-01

Construction workers exposed to silica-containing dust are at risk of developing silicosis even at low exposure levels. Health surveillance among these workers is commonly advised but the exact diagnostic work-up is not specified and therefore may result in unnecessary chest x ray investigations. To develop a simple diagnostic model to estimate the probability of an individual worker having pneumoconiosis from questionnaire and spirometry results, in order to accurately rule out workers without pneumoconiosis. The study was performed using cross-sectional data of 1291 Dutch natural stone and construction workers with potentially high quartz dust exposure. A multivariable logistic regression model was developed using chest x ray with ILO profusion category > or =1/1 as the reference standard. The model's calibration was evaluated with the Hosmer-Lemeshow test; the discriminative ability was determined by calculating the area under the receiver operating characteristic curve (ROC area). Internal validity of the final model was assessed by a bootstrapping procedure. For clinical application, the diagnostic model was transformed into an easy-to-use score chart. Age 40 years or older, current smoker, high-exposure job, working 15 years or longer in the construction industry, "feeling unhealthy" and FEV1 were independent predictors in the diagnostic model. The model showed good calibration (a non-significant Hosmer-Lemeshow test) and discriminative ability (ROC area 0.81, 95% CI 0.74 to 0.85). Internal validity was reasonable; the optimism corrected ROC area was 0.76. By using a cut-off point with a high negative predictive value the occupational physician can efficiently detect a large proportion of workers with a low probability of having pneumoconiosis and exclude them from unnecessary x ray investigations. This diagnostic model is an efficient and effective instrument to rule out pneumoconiosis among construction workers. Its use in health surveillance among these workers can reduce the number of redundant x ray investigations.

Prediction of postnatal outcomes in fetuses with isolated congenital diaphragmatic hernias using different lung-to-head ratio measurements.

PubMed

Kehl, Sven; Siemer, Jörn; Brunnemer, Suna; Weiss, Christel; Eckert, Sven; Schaible, Thomas; Sütterlin, Marc

2014-05-01

The purpose of this study was to compare different methods for measuring the fetal lung area-to-head circumference ratio and to investigate their prediction of postpartum survival and the need for neonatal extracorporeal membrane oxygenation (ECMO) therapy in fetuses with isolated congenital diaphragmatic hernias. This prospective study included 118 fetuses of at least 20 weeks' gestation with isolated left-sided congenital diaphragmatic hernias. The lung-to-head ratio was measured with 3 different methods (longest diameter, anteroposterior diameter, and tracing). To eliminate the influence of gestational age, the observed-to-expected lung-to-head ratio was calculated. Receiver operating characteristic (ROC) curves were calculated for the statistical prediction of survival and need for ECMO therapy by the observed-to-expected lung-to-head ratio measured with the different methods. For survival and ECMO necessity 118 and 102 cases (16 neonates were not eligible for ECMO) were assessed, respectively. For prediction of postpartum survival and ECMO necessity, the areas under the ROC curves and 95% confidence intervals showed very similar results for the 3 methods for prediction of survival (tracing, 0.8445 [0.7553-0.9336]; longest diameter, 0.8248 [0.7360-0.9136]; and anteroposterior diameter, 0.8002 [0.7075-0.8928]) and for ECMO necessity (tracing, 0.7344 [0.6297-0.8391]; longest diameter, 0.7128 [0.6027-0.8228]; and anteroposterior diameter, 0.7212 [0.6142-0.8281]). Comparisons between the areas under the ROC curves showed that the tracing method was superior to the anteroposterior diameter method in predicting postpartum survival (P = .0300). Lung-to-head ratio and observed-to-expected lung-to-head ratio measurements were shown to accurately predict postnatal survival and the need for ECMO therapy in fetuses with left-sided congenital diaphragmatic hernias. Tracing the limits of the lungs seems to be the favorable method for calculating the fetal lung area.

Detection of simulated microcalcifications in fixed mammary tissue: An ROC study of the effect of local versus global histogram equalization.

PubMed

Sund, T; Olsen, J B

2006-09-01

To investigate whether sliding window adaptive histogram equalization (SWAHE) of digital mammograms improves the detection of simulated calcifications, as compared to images normalized by global histogram equalization (GHE). Direct digital mammograms were obtained from mammary tissue phantoms superimposed with different frames. Each frame was divided into forty squares by a wire mesh, and contained granular calcifications randomly positioned in about 50% of the squares. Three radiologists read the mammograms on a display monitor. They classified their confidence in the presence of microcalcifications in each square on a scale of 1 to 5. Images processed with GHE were first read and used as a reference. In a later session, the same images processed with SWAHE were read. The results were compared using ROC methodology. When the total areas AZ were compared, the results were completely equivocal. When comparing the high-specificity partial ROC area AZ,0.2 below false-positive fraction (FPF) 0.20, two of the three observers performed best with the images processed with SWAHE. The difference was not statistically significant. When the reader's confidence threshold in malignancy is set at a high level, increasing the contrast of mammograms with SWAHE may enhance the visibility of microcalcifications without adversely affecting the false-positive rate. When the reader's confidence threshold is set at a low level, the effect of SWAHE is an increase of false positives. Further investigation is needed to confirm the validity of the conclusions.

FibroScan, aspartate aminotransferase and alanine aminotransferase ratio (AAR), aspartate aminotransferase to platelet ratio index (APRI), fibrosis index based on the 4 factor (FIB-4), and their combinations in the assessment of liver fibrosis in patients with hepatitis B.

PubMed

Ding, Deping; Li, Hongbing; Liu, Ping; Chen, Lingli; Kang, Jian; Zhang, Yinhua; Ma, Deqiang; Chen, Yue; Luo, Jie; Meng, Zhongji

2015-01-01

The aim of this study was to assess the effects of FibroScan, aspartate aminotransferase and alanine aminotransferase ratio (AAR), aspartate aminotransferase to platelet ratio index (APRI), fibrosis index based on the 4 factor (FIB-4) and their combinations on liver fibrosis in patients with hepatitis B. 406 hospitalized patients with chronic hepatitis B (CHB) and cirrhosis in our hospital were analyzed retrospectively and collected patients clinical indicators, including liver stiffness (LS), AAR, APRI and FIB-4, and then compared the differences of these indicators between CHB group and hepatitis B with cirrhosis group. Receiver operating curve (ROC) was used to evaluate the differentiating capacity of these indicators on CHB and liver cirrhosis. Four indicators related to liver cirrhosis had a statistical significance between two groups (P < 0.01); the under ROC curve areas of LS, AAR, APRI and FIB-4 for differential diagnosis of CHB and liver cirrhosis were 0.866, 0.772, 0.632 and 0.885, respectively. The under ROC curve areas of LS, AAR, APRI and FIB-4 for differential diagnosis of liver cirrhosis at compensatory stage and de-compensatory stage were 0.627, 0.666, 0.795 and 0.820, respectively. LS, AAR, APRI and FIB-4 were good indicators as clinical diagnosis and differential diagnosis on hepatitis B related cirrhosis.

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farjam, Reza; Tsien, Christina I.; Lawrence, Theodore S.

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. Amore » DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.« less

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farjam, Reza; Tsien, Christina I.; Lawrence, Theodore S.

2014-01-15

Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. Amore » DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.« less

Diagnostic value of (99m)Tc-3PRGD2 scintimammography for differentiation of malignant from benign breast lesions: Comparison of visual and semi-quantitative analysis.

PubMed

Chen, Qianqian; Xie, Qian; Zhao, Min; Chen, Bin; Gao, Shi; Zhang, Haishan; Xing, Hua; Ma, Qingjie

2015-01-01

To compare the diagnostic value of visual and semi-quantitative analysis of technetium-99m-poly-ethylene glycol, 4-arginine-glycine-aspartic acid ((99m)Tc-3PRGD2) scintimammography (SMG) for better differentiation of benign from malignant breast masses, and also investigate the incremental role of semi-quantitative index of SMG. A total of 72 patients with breast lesions were included in the study. Technetium-99m-3PRGD2 SMG was performed with single photon emission computed tomography (SPET) at 60 min after intravenous injection of 749 ± 86MBq of the radiotracer. Images were evaluated by visual interpretation and semi-quantitative indices of tumor to non-tumor (T/N) ratios, which were compared with pathology results. Receiver operating characteristics (ROC) curve analyses were performed to determine the optimal visual grade, to calculate cut-off values of semi-quantitative indices, and to compare visual and semi-quantitative diagnostic values. Among the 72 patients, 89 lesions were confirmed by histopathology after fine needle aspiration biopsy or surgery, 48 malignant and 41 benign lesions. The mean T/N ratio of (99m)Tc-3PRGD2 SMG in malignant lesions was significantly higher than that in benign lesions (P<0.05). When grade 2 of the disease was used as cut-off value for the detection of primary breast cancer, the sensitivity, specificity and accuracy were 81.3%, 70.7%, and 76.4%, respectively. When a T/N ratio of 2.01 was used as cut-off value, the sensitivity, specificity and accuracy were 79.2%, 75.6%, and 77.5%, respectively. According to ROC analysis, the area under the curve for semi-quantitative analysis was higher than that for visual analysis, but the statistical difference was not significant (P=0.372). Compared with visual analysis or semi-quantitative analysis alone, the sensitivity, specificity and accuracy of visual analysis combined with semi-quantitative analysis in diagnosing primary breast cancer were higher, being: 87.5%, 82.9%, and 85.4%, respectively. The area under the curve was 0.891. Results of the present study suggest that the semi-quantitative and visual analysis statistically showed similar results. The semi-quantitative analysis provided incremental value additive to visual analysis of (99m)Tc-3PRGD2 SMG for the detection of breast cancer. It seems from our results that, when the tumor was located in the medial part of the breast, the semi-quantitative analysis gave better diagnostic results.

Quantitative fibrosis parameters highly predict esophageal-gastro varices in primary biliary cirrhosis.

PubMed

Wu, Q-M; Zhao, X-Y; You, H

2016-01-01

Esophageal-gastro Varices (EGV) may develop in any histological stages of primary biliary cirrhosis (PBC). We aim to establish and validate quantitative fibrosis (qFibrosis) parameters in portal, septal and fibrillar areas as ideal predictors of EGV in PBC patients. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. PBC patients with liver biopsy, esophagogastroscopy and Second Harmonic Generation (SHG)/Two-photon Excited Fluorescence (TPEF) microscopy images were retrospectively enrolled in this study. qFibrosis parameters in portal, septal and fibrillar areas were acquired by computer-assisted SHG/TPEF imaging system. Independent predictor was identified using multivariate logistic regression analysis. Among the forty-nine PBC patients with qFibrosis images, twenty-nine PBC patients with both esophagogastroscopy data and qFibrosis data were selected out for EGV prognosis analysis and 44.8% (13/29) of them had EGV. The qFibrosis parameters of collagen percentage and number of crosslink in fibrillar area, short/long/thin strings number and length/width of the strings in septa area were associated with EGV (p < 0.05). Multivariate logistic analysis showed that the collagen percentage in fibrillar area ≥ 3.6% was an independent factor to predict EGV (odds ratio 6.9; 95% confidence interval 1.6-27.4). The area under receiver operating characteristic (ROC), diagnostic sensitivity and specificity was 0.9, 100% and 75% respectively. Collagen percentage in Collagen percentage in the fibrillar area as an independent predictor can highly predict EGV in PBC patients.

On the convexity of ROC curves estimated from radiological test results.

PubMed

Pesce, Lorenzo L; Metz, Charles E; Berbaum, Kevin S

2010-08-01

Although an ideal observer's receiver operating characteristic (ROC) curve must be convex-ie, its slope must decrease monotonically-published fits to empirical data often display "hooks." Such fits sometimes are accepted on the basis of an argument that experiments are done with real, rather than ideal, observers. However, the fact that ideal observers must produce convex curves does not imply that convex curves describe only ideal observers. This article aims to identify the practical implications of nonconvex ROC curves and the conditions that can lead to empirical or fitted ROC curves that are not convex. This article views nonconvex ROC curves from historical, theoretical, and statistical perspectives, which we describe briefly. We then consider population ROC curves with various shapes and analyze the types of medical decisions that they imply. Finally, we describe how sampling variability and curve-fitting algorithms can produce ROC curve estimates that include hooks. We show that hooks in population ROC curves imply the use of an irrational decision strategy, even when the curve does not cross the chance line, and therefore usually are untenable in medical settings. Moreover, we sketch a simple approach to improve any nonconvex ROC curve by adding statistical variation to the decision process. Finally, we sketch how to test whether hooks present in ROC data are likely to have been caused by chance alone and how some hooked ROCs found in the literature can be easily explained as fitting artifacts or modeling issues. In general, ROC curve fits that show hooks should be looked on with suspicion unless other arguments justify their presence. 2010 AUR. Published by Elsevier Inc. All rights reserved.

On the convexity of ROC curves estimated from radiological test results

PubMed Central

Pesce, Lorenzo L.; Metz, Charles E.; Berbaum, Kevin S.

2010-01-01

Rationale and Objectives Although an ideal observer’s receiver operating characteristic (ROC) curve must be convex — i.e., its slope must decrease monotonically — published fits to empirical data often display “hooks.” Such fits sometimes are accepted on the basis of an argument that experiments are done with real, rather than ideal, observers. However, the fact that ideal observers must produce convex curves does not imply that convex curves describe only ideal observers. This paper aims to identify the practical implications of non-convex ROC curves and the conditions that can lead to empirical and/or fitted ROC curves that are not convex. Materials and Methods This paper views non-convex ROC curves from historical, theoretical and statistical perspectives, which we describe briefly. We then consider population ROC curves with various shapes and analyze the types of medical decisions that they imply. Finally, we describe how sampling variability and curve-fitting algorithms can produce ROC curve estimates that include hooks. Results We show that hooks in population ROC curves imply the use of an irrational decision strategy, even when the curve doesn’t cross the chance line, and therefore usually are untenable in medical settings. Moreover, we sketch a simple approach to improve any non-convex ROC curve by adding statistical variation to the decision process. Finally, we sketch how to test whether hooks present in ROC data are likely to have been caused by chance alone and how some hooked ROCs found in the literature can be easily explained as fitting artifacts or modeling issues. Conclusion In general, ROC curve fits that show hooks should be looked upon with suspicion unless other arguments justify their presence. PMID:20599155

Change in end-tidal carbon dioxide outperforms other surrogates for change in cardiac output during fluid challenge.

PubMed

Lakhal, K; Nay, M A; Kamel, T; Lortat-Jacob, B; Ehrmann, S; Rozec, B; Boulain, T

2017-03-01

During fluid challenge, volume expansion (VE)-induced increase in cardiac output (Δ VE CO) is seldom measured. In patients with shock undergoing strictly controlled mechanical ventilation and receiving VE, we assessed minimally invasive surrogates for Δ VE CO (by transthoracic echocardiography): fluid-induced increases in end-tidal carbon dioxide (Δ VE E'CO2 ); pulse (Δ VE PP), systolic (Δ VE SBP), and mean systemic blood pressure (Δ VE MBP); and femoral artery Doppler flow (Δ VE FemFlow). In the absence of arrhythmia, fluid-induced decrease in heart rate (Δ VE HR) and in pulse pressure respiratory variation (Δ VE PPV) were also evaluated. Areas under the receiver operating characteristic curves (AUC ROC s) reflect the ability to identify a response to VE (Δ VE CO ≥15%). In 86 patients, Δ VE E'CO2 had an AUC ROC =0.82 [interquartile range 0.73-0.90], significantly higher than the AUC ROC for Δ VE PP, Δ VE SBP, Δ VE MBP, and Δ VE FemFlow (AUC ROC =0.61-0.65, all P  <0.05). A value of Δ VE E'CO2  >1 mm Hg (>0.13 kPa) had good positive (5.0 [2.6-9.8]) and fair negative (0.29 [0.2-0.5]) likelihood ratios. The 16 patients with arrhythmia had similar relationships between Δ VE E'CO2 and Δ VE CO to patients with regular rhythm ( r 2 =0.23 in both subgroups). In 60 patients with no arrhythmia, Δ VE E'CO2 (AUC ROC =0.84 [0.72-0.92]) outperformed Δ VE HR (AUC ROC =0.52 [0.39-0.66], P <0.05) and tended to outperform Δ VE PPV (AUC ROC =0.73 [0.60-0.84], P =0.21). In the 45 patients with no arrhythmia and receiving ventilation with tidal volume <8 ml kg -1 , Δ VE E'CO2 performed better than Δ VE PPV, with AUC ROC =0.86 [0.72-0.95] vs 0.66 [0.49-0.80], P =0.02. Δ VE E'CO2 outperformed Δ VE PP, Δ VE SBP, Δ VE MBP, Δ VE FemFlow, and Δ VE HR and, during protective ventilation, arrhythmia, or both, it also outperformed Δ VE PPV. A value of Δ VE E'CO2 >1 mm Hg (>0.13 kPa) indicated a likely response to VE. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

PubMed

Lau, Lawrence; Kankanige, Yamuna; Rubinstein, Benjamin; Jones, Robert; Christophi, Christopher; Muralidharan, Vijayaragavan; Bailey, James

2017-04-01

The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritized. An index that is derived to predict graft failure using donor and recipient factors, based on local data sets, will be more beneficial in the Australian context. Liver transplant data from the Austin Hospital, Melbourne, Australia, from 2010 to 2013 has been included in the study. The top 15 donor, recipient, and transplant factors influencing the outcome of graft failure within 30 days were selected using a machine learning methodology. An algorithm predicting the outcome of interest was developed using those factors. Donor Risk Index predicts the outcome with an area under the receiver operating characteristic curve (AUC-ROC) value of 0.680 (95% confidence interval [CI], 0.669-0.690). The combination of the factors used in Donor Risk Index with the model for end-stage liver disease score yields an AUC-ROC of 0.764 (95% CI, 0.756-0.771), whereas survival outcomes after liver transplantation score obtains an AUC-ROC of 0.638 (95% CI, 0.632-0.645). The top 15 donor and recipient characteristics within random forests results in an AUC-ROC of 0.818 (95% CI, 0.812-0.824). Using donor, transplant, and recipient characteristics known at the decision time of a transplant, high accuracy in matching donors and recipients can be achieved, potentially providing assistance with clinical decision making.

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

PubMed Central

Tsui, Po-Hsiang; Chen, Chin-Kuo; Kuo, Wen-Hung; Chang, King-Jen; Fang, Jui; Ma, Hsiang-Yang; Chou, Dean

2017-01-01

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging. PMID:28106118

Anthropometric indicators of obesity in the prediction of high body fat in adolescents

PubMed Central

Pelegrini, Andreia; Silva, Diego Augusto Santos; Silva, João Marcos Ferreira de Lima; Grigollo, Leoberto; Petroski, Edio Luiz

2015-01-01

OBJECTIVE: To determine the anthropometric indicators of obesity in the prediction of high body fat in adolescents from a Brazilian State. METHODS: The study included 1,197 adolescents (15-17 years old). The following anthropometric measurements were collected: body mass (weight and height), waist circumference and skinfolds (triceps and medial calf). The anthropometric indicators analyzed were: body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR) and conicity index (C-Index). Body fat percentage, estimated by the Slaughter et al equation, was used as the reference method. Descriptive statistics, U Mann-Whitney test, and ROC curve were used for data analysis. RESULTS: Of the four anthropometric indicators studied, BMI, WHtR and WC had the largest areas under the ROC curve in relation to relative high body fat in both genders. The cutoffs for boys and girls, respectively, associated with high body fat were BMI 22.7 and 20.1kg/m², WHtR 0.43 and 0.41, WC 75.7 and 67.7cm and C-Index 1.12 and 1.06. CONCLUSIONS: Anthropometric indicators can be used in screening for identification of body fat in adolescents, because they are simple, have low cost and are non-invasive. PMID:25649384

Acoustic cue weighting in the singleton vs geminate contrast in Lebanese Arabic: The case of fricative consonants.

PubMed

Al-Tamimi, Jalal; Khattab, Ghada

2015-07-01

This paper is the first reported investigation of the role of non-temporal acoustic cues in the singleton-geminate contrast in Lebanese Arabic, alongside the more frequently reported temporal cues. The aim is to explore the extent to which singleton and geminate consonants show qualitative differences in a language where phonological length is prominent and where moraic structure governs segment timing and syllable weight. Twenty speakers (ten male, ten female) were recorded producing trochaic disyllables with medial singleton and geminate fricatives preceded by phonologically short and long vowels. The following acoustic measures were applied on the medial fricative and surrounding vowels: absolute duration; intensity; fundamental frequency; spectral peak and shape, dynamic amplitude, and voicing patterns of medial fricatives; and vowel quality and voice quality correlates of surrounding vowels. Discriminant analysis and receiver operating characteristics (ROC) curves were used to assess each acoustic cue's contribution to the singleton-geminate contrast. Classification rates of 89% and ROC curves with an area under the curve rate of 96% confirmed the major role played by temporal cues, with non-temporal cues contributing to the contrast but to a much lesser extent. These results confirm that the underlying contrast for gemination in Arabic is temporal, but highlight [+tense] (fortis) as a secondary feature.

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Tsui, Po-Hsiang; Chen, Chin-Kuo; Kuo, Wen-Hung; Chang, King-Jen; Fang, Jui; Ma, Hsiang-Yang; Chou, Dean

2017-01-01

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

Assessment of Diagnostic and Prognostic Role of Copeptin in the Clinical Setting of Sepsis.

PubMed

Battista, Stefania; Audisio, Umberto; Galluzzo, Claudia; Maggiorotto, Matteo; Masoero, Monica; Forno, Daniela; Pizzolato, Elisa; Ulla, Marco; Lucchiari, Manuela; Vitale, Annarita; Moiraghi, Corrado; Lupia, Enrico; Settanni, Fabio; Mengozzi, Giulio

2016-01-01

The diagnostic and prognostic usefulness of copeptin were evaluated in septic patients, as compared to procalcitonin assessment. In this single centre and observational study 105 patients were enrolled: 24 with sepsis, 25 with severe sepsis, 15 with septic shock, and 41 controls, divided in two subgroups (15 patients with gastrointestinal bleeding and 26 with suspected SIRS secondary to trauma, acute coronary syndrome, and pulmonary embolism). Biomarkers were determined at the first medical evaluation and thereafter 24, 48, and 72 hours after admission. Definitive diagnosis and in-hospital survival rates at 30 days were obtained through analysis of medical records. At entry, copeptin proved to be able to distinguish cases from controls and also sepsis group from septic shock group, while procalcitonin could distinguish also severe sepsis from septic shock group. Areas under the ROC curve for copeptin and procalcitonin were 0.845 and 0.861, respectively. Noteworthy, patients with copeptin concentrations higher than the threshold value (23.2 pmol/L), calculated from the ROC curve, at admission presented higher 30-day mortality. No significant differences were found in copeptin temporal profile among different subgroups. Copeptin showed promising diagnostic and prognostic role in the management of sepsis, together with its possible role in monitoring the response to treatment.

Assessment of Diagnostic and Prognostic Role of Copeptin in the Clinical Setting of Sepsis

PubMed Central

Battista, Stefania; Audisio, Umberto; Galluzzo, Claudia; Maggiorotto, Matteo; Masoero, Monica; Forno, Daniela; Pizzolato, Elisa; Ulla, Marco; Lucchiari, Manuela; Vitale, Annarita; Moiraghi, Corrado; Lupia, Enrico; Settanni, Fabio; Mengozzi, Giulio

2016-01-01

The diagnostic and prognostic usefulness of copeptin were evaluated in septic patients, as compared to procalcitonin assessment. In this single centre and observational study 105 patients were enrolled: 24 with sepsis, 25 with severe sepsis, 15 with septic shock, and 41 controls, divided in two subgroups (15 patients with gastrointestinal bleeding and 26 with suspected SIRS secondary to trauma, acute coronary syndrome, and pulmonary embolism). Biomarkers were determined at the first medical evaluation and thereafter 24, 48, and 72 hours after admission. Definitive diagnosis and in-hospital survival rates at 30 days were obtained through analysis of medical records. At entry, copeptin proved to be able to distinguish cases from controls and also sepsis group from septic shock group, while procalcitonin could distinguish also severe sepsis from septic shock group. Areas under the ROC curve for copeptin and procalcitonin were 0.845 and 0.861, respectively. Noteworthy, patients with copeptin concentrations higher than the threshold value (23.2 pmol/L), calculated from the ROC curve, at admission presented higher 30-day mortality. No significant differences were found in copeptin temporal profile among different subgroups. Copeptin showed promising diagnostic and prognostic role in the management of sepsis, together with its possible role in monitoring the response to treatment. PMID:27366743

GPU-based prompt gamma ray imaging from boron neutron capture therapy.

PubMed

Yoon, Do-Kun; Jung, Joo-Young; Jo Hong, Key; Sil Lee, Keum; Suk Suh, Tae

2015-01-01

The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU). Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray image reconstruction using the GPU computation for BNCT simulations.

Problematic smartphone use, nature connectedness, and anxiety.

PubMed

Richardson, Miles; Hussain, Zaheer; Griffiths, Mark D

2018-03-01

Background Smartphone use has increased greatly at a time when concerns about society's disconnection from nature have also markedly increased. Recent research has also indicated that smartphone use can be problematic for a small minority of individuals. Methods In this study, associations between problematic smartphone use (PSU), nature connectedness, and anxiety were investigated using a cross-sectional design (n = 244). Results Associations between PSU and both nature connectedness and anxiety were confirmed. Receiver operating characteristic (ROC) curves were used to identify threshold values on the Problematic Smartphone Use Scale (PSUS) at which strong associations with anxiety and nature connectedness occur. The area under the curve was calculated and positive likelihood ratios used as a diagnostic parameter to identify optimal cut-off for PSU. These provided good diagnostic ability for nature connectedness, but poor and non-significant results for anxiety. ROC analysis showed the optimal PSUS threshold for high nature connectedness to be 15.5 (sensitivity: 58.3%; specificity: 78.6%) in response to an LR+ of 2.88. Conclusions The results demonstrate the potential utility for the PSUS as a diagnostic tool, with a level of smartphone use that users may perceive as non-problematic being a significant cut-off in terms of achieving beneficial levels of nature connectedness. Implications of these findings are discussed.

Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

PubMed

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

2018-06-05

Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

Optimizing the use of the AUDIT for alcohol screening in college students.

PubMed

Demartini, Kelly S; Carey, Kate B

2012-12-01

The screening and brief intervention modality of treatment for at-risk college drinking is becoming increasingly popular. A key to effective implementation is use of validated screening tools. Although the Alcohol Use Disorders Identification Test (AUDIT) has been validated in adult samples and is often used with college students, research has not yet established optimal cutoff scores to screen for at-risk drinking. Four hundred and one current drinkers completed computerized assessments of demographics, family history of alcohol use disorders, alcohol use history, alcohol-related problems, and general health. Of the 401 drinkers, 207 met criteria for at-risk drinking. Receiver operating characteristic (ROC) curve analysis revealed that the area under the ROC (AUROC) of the AUDIT was .86 (95% CI [.83, .90]). The first 3 consumption items of the AUDIT (AUDIT-C; AUROC = .89, 95% CI [.86, .92]) performed significantly better than the AUDIT in the detection of at-risk drinking in the whole sample, and specifically for females. Gender differences emerged in the optimal cutoff scores for the AUDIT-C. A total score of 7 should be used for males, and a score of 5 should be used for females. These empirical guidelines may enhance identification of at-risk drinkers in college settings.

Metabolomic profile of systemic sclerosis patients.

PubMed

Murgia, Federica; Svegliati, Silvia; Poddighe, Simone; Lussu, Milena; Manzin, Aldo; Spadoni, Tatiana; Fischetti, Colomba; Gabrielli, Armando; Atzori, Luigi

2018-05-16

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by 1 H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726-0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7-0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Assessment of single-item literacy questions, age, and education level in the prediction of low health numeracy.

PubMed

Johnson, Tim V; Abbasi, Ammara; Kleris, Renee S; Ehrlich, Samantha S; Barthwaite, Echo; DeLong, Jennifer; Master, Viraj A

2013-08-01

Determining a patient's health literacy is important to optimum patient care. Single-item questions exist for screening written health literacy. We sought to assess the predictive potential of three common screening questions, along with patient age and education level, in the prediction of low health numerical literacy (numeracy). After demographic and educational information was obtained, 441 patients were administered three health literacy screening questions. The three-item Schwartz-Woloshin Numeracy Scale was then administered to assess for low health numeracy (score of 0 out of 3). This score served as the reference standard for Receiver Operating Characteristics (ROC) curve analysis. ROC curves were constructed and used to determine the area under the curve (AUC); a higher AUC suggests increased statistical significance. None of the three screening questions were significant predictors of low health numeracy. However, education level was a significant predictor of low health numeracy, with an AUC (95% CI) of 0.811 (0.720-0.902). This measure had a specificity of 95.3% at the cutoff of 12 years of education (<12 versus > or = 12 years of education) but was non-sensitive. Common single-item questions used to screen for written health literacy are ineffective screening tools for health numeracy. However, low education level is a specific predictor of low health numeracy.

Screening for Sleep Apnoea in Mild Cognitive Impairment: The Utility of the Multivariable Apnoea Prediction Index

PubMed Central

Wilson, Georgina; Terpening, Zoe; Wong, Keith; Grunstein, Ron; Norrie, Louisa; Lewis, Simon J. G.; Naismith, Sharon L.

2014-01-01

Purpose. Mild cognitive impairment (MCI) is considered an “at risk” state for dementia and efforts are needed to target modifiable risk factors, of which Obstructive sleep apnoea (OSA) is one. This study aims to evaluate the predictive utility of the multivariate apnoea prediction index (MAPI), a patient self-report survey, to assess OSA in MCI. Methods. Thirty-seven participants with MCI and 37 age-matched controls completed the MAPI and underwent polysomnography (PSG). Correlations were used to compare the MAPI and PSG measures including oxygen desaturation index and apnoea-hypopnoea index (AHI). Receiver-operating characteristics (ROC) curve analyses were performed using various cut-off scores for apnoea severity. Results. In controls, there was a significant moderate correlation between higher MAPI scores and more severe apnoea (AHI: r = 0.47, P = 0.017). However, this relationship was not significant in the MCI sample. ROC curve analysis indicated much lower area under the curve (AUC) in the MCI sample compared to the controls across all AHI severity cut-off scores. Conclusions. In older people, the MAPI moderately correlates with AHI severity but only in those who are cognitively intact. Development of further screening tools is required in order to accurately screen for OSA in MCI. PMID:24551457

Regional rainfall thresholds for landslide occurrence using a centenary database

NASA Astrophysics Data System (ADS)

Vaz, Teresa; Luís Zêzere, José; Pereira, Susana; Cruz Oliveira, Sérgio; Garcia, Ricardo A. C.; Quaresma, Ivânia

2018-04-01

This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.