Update on Clinical Features and Brain Abnormalities in Neurogenetics Syndromes

ERIC Educational Resources Information Center

Jackowski, Andrea Parolin; Laureano, Maura Regina; Del'Aquilla, Marco Antonio; de Moura, Luciana Monteiro; Assuncao, Idaiane; Silva, Ivaldo; Schwartzman, Jose Salomao

2011-01-01

Neuroimaging methods represent a critical tool in efforts to join the study of the neurobiology of genes with the neurobiology of behaviour, and to understand the neurodevelopmental pathways that give rise to cognitive and behavioural impairments. This article reviews the clinical features and highlights studies with a focus on the relevant…

Motivation factors for suicidal behavior and their clinical relevance in admitted psychiatric patients.

PubMed

Hayashi, Naoki; Igarashi, Miyabi; Imai, Atsushi; Yoshizawa, Yuka; Asamura, Kaori; Ishikawa, Yoichi; Tokunaga, Taro; Ishimoto, Kayo; Tatebayashi, Yoshitaka; Harima, Hirohiko; Kumagai, Naoki; Ishii, Hidetoki; Okazaki, Yuji

2017-01-01

Suicidal behavior (SB) is a major, worldwide health concern. To date there is limited understanding of the associated motivational aspects which accompany this self-initiated conduct. To develop a method for identifying motivational features associated with SB by studying admitted psychiatric patients, and to examine their clinical relevance. By performing a factor analytic study using data obtained from a patient sample exhibiting high suicidality and a variety of SB methods, Motivations for SB Scale (MSBS) was constructed to measure the features. Data included assessments of DSM-IV psychiatric and personality disorders, suicide intent, depressive symptomatology, overt aggression, recent life events (RLEs) and methods of SB, collated from structured interviews. Association of identified features with clinical variables was examined by correlation analyses and MANCOVA. Factor analyses elicited a 4-factor solution composed of Interpersonal-testing (IT), Interpersonal-change (IC), Self-renunciation (SR) and Self-sustenance (SS). These factors were classified according to two distinctions, namely interpersonal vs. intra-personal directedness, and the level of assumed influence by SB or the relationship to prevailing emotions. Analyses revealed meaningful links between patient features and clinical variables. Interpersonal-motivations (IT and IC) were associated with overt aggression, low suicidality and RLE discord or conflict, while SR was associated with depression, high suicidality and RLE separation or death. Borderline personality disorder showed association with IC and SS. When self-strangulation was set as a reference SB method, self-cutting and overdose-taking were linked to IT and SS, respectively. The factors extracted in this study largely corresponded to factors from previous studies, implying that they may be useful in a wider clinical context. The association of these features with SB-related factors suggests that they constitute an integral part of the process leading to SB. These results provide a base for further research into clinical strategies for patient management and therapy.

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

PubMed

Gentry, Amanda Elswick; Jackson-Cook, Colleen K; Lyon, Debra E; Archer, Kellie J

2015-01-01

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

Automated analysis of free speech predicts psychosis onset in high-risk youths

PubMed Central

Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

2015-01-01

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. PMID:27336038

A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform

NASA Astrophysics Data System (ADS)

Wu, T. Y.; Lin, S. F.

2013-10-01

Automatic suspected lesion extraction is an important application in computer-aided diagnosis (CAD). In this paper, we propose a method to automatically extract the suspected parotid regions for clinical evaluation in head and neck CT images. The suspected lesion tissues in low contrast tissue regions can be localized with feature-based segmentation (FBS) based on local texture features, and can be delineated with accuracy by modified active contour models (ACM). At first, stationary wavelet transform (SWT) is introduced. The derived wavelet coefficients are applied to derive the local features for FBS, and to generate enhanced energy maps for ACM computation. Geometric shape features (GSFs) are proposed to analyze each soft tissue region segmented by FBS; the regions with higher similarity GSFs with the lesions are extracted and the information is also applied as the initial conditions for fine delineation computation. Consequently, the suspected lesions can be automatically localized and accurately delineated for aiding clinical diagnosis. The performance of the proposed method is evaluated by comparing with the results outlined by clinical experts. The experiments on 20 pathological CT data sets show that the true-positive (TP) rate on recognizing parotid lesions is about 94%, and the dimension accuracy of delineation results can also approach over 93%.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of design features to enhance utilization and acceptance of systems for Internet-based decision support at the point of care.

PubMed Central

Gadd, C. S.; Baskaran, P.; Lobach, D. F.

1998-01-01

Extensive utilization of point-of-care decision support systems will be largely dependent on the development of user interaction capabilities that make them effective clinical tools in patient care settings. This research identified critical design features of point-of-care decision support systems that are preferred by physicians, through a multi-method formative evaluation of an evolving prototype of an Internet-based clinical decision support system. Clinicians used four versions of the system--each highlighting a different functionality. Surveys and qualitative evaluation methodologies assessed clinicians' perceptions regarding system usability and usefulness. Our analyses identified features that improve perceived usability, such as telegraphic representations of guideline-related information, facile navigation, and a forgiving, flexible interface. Users also preferred features that enhance usefulness and motivate use, such as an encounter documentation tool and the availability of physician instruction and patient education materials. In addition to identifying design features that are relevant to efforts to develop clinical systems for point-of-care decision support, this study demonstrates the value of combining quantitative and qualitative methods of formative evaluation with an iterative system development strategy to implement new information technology in complex clinical settings. Images Figure 1 PMID:9929188

Machine Learning Approach for Classifying Multiple Sclerosis Courses by Combining Clinical Data with Lesion Loads and Magnetic Resonance Metabolic Features.

PubMed

Ion-Mărgineanu, Adrian; Kocevar, Gabriel; Stamile, Claudio; Sima, Diana M; Durand-Dubief, Françoise; Van Huffel, Sabine; Sappey-Marinier, Dominique

2017-01-01

Purpose: The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. Materials and Methods: Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were included in this study. Longitudinal data available for each MS patient included clinical (e.g., age, disease duration, Expanded Disability Status Scale), conventional magnetic resonance imaging and spectroscopic imaging. We extract N -acetyl-aspartate (NAA), Choline (Cho), and Creatine (Cre) concentrations, and we compute three features for each spectroscopic grid by averaging metabolite ratios (NAA/Cho, NAA/Cre, Cho/Cre) over good quality voxels. We built linear mixed-effects models to test for statistically significant differences between MS forms. We test nine binary classification tasks on clinical data, lesion loads, and metabolic features, using a leave-one-patient-out cross-validation method based on 100 random patient-based bootstrap selections. We compute F1-scores and BAR values after tuning Linear Discriminant Analysis (LDA), Support Vector Machines with gaussian kernel (SVM-rbf), and Random Forests. Results: Statistically significant differences were found between the disease starting points of each MS form using four different response variables: Lesion Load, NAA/Cre, NAA/Cho, and Cho/Cre ratios. Training SVM-rbf on clinical and lesion loads yields F1-scores of 71-72% for CIS vs. RR and CIS vs. RR+SP, respectively. For RR vs. PP we obtained good classification results (maximum F1-score of 85%) after training LDA on clinical and metabolic features, while for RR vs. SP we obtained slightly higher classification results (maximum F1-score of 87%) after training LDA and SVM-rbf on clinical, lesion loads and metabolic features. Conclusions: Our results suggest that metabolic features are better at differentiating between relapsing-remitting and primary progressive forms, while lesion loads are better at differentiating between relapsing-remitting and secondary progressive forms. Therefore, combining clinical data with magnetic resonance lesion loads and metabolic features can improve the discrimination between relapsing-remitting and progressive forms.

BRIEF REPORT: Beyond Clinical Experience: Features of Data Collection and Interpretation That Contribute to Diagnostic Accuracy

PubMed Central

Nendaz, Mathieu R; Gut, Anne M; Perrier, Arnaud; Louis-Simonet, Martine; Blondon-Choa, Katherine; Herrmann, François R; Junod, Alain F; Vu, Nu V

2006-01-01

BACKGROUND Clinical experience, features of data collection process, or both, affect diagnostic accuracy, but their respective role is unclear. OBJECTIVE, DESIGN Prospective, observational study, to determine the respective contribution of clinical experience and data collection features to diagnostic accuracy. METHODS Six Internists, 6 second year internal medicine residents, and 6 senior medical students worked up the same 7 cases with a standardized patient. Each encounter was audiotaped and immediately assessed by the subjects who indicated the reasons underlying their data collection. We analyzed the encounters according to diagnostic accuracy, information collected, organ systems explored, diagnoses evaluated, and final decisions made, and we determined predictors of diagnostic accuracy by logistic regression models. RESULTS Several features significantly predicted diagnostic accuracy after correction for clinical experience: early exploration of correct diagnosis (odds ratio [OR] 24.35) or of relevant diagnostic hypotheses (OR 2.22) to frame clinical data collection, larger number of diagnostic hypotheses evaluated (OR 1.08), and collection of relevant clinical data (OR 1.19). CONCLUSION Some features of data collection and interpretation are related to diagnostic accuracy beyond clinical experience and should be explicitly included in clinical training and modeled by clinical teachers. Thoroughness in data collection should not be considered a privileged way to diagnostic success. PMID:17105525

A Clinical Decision Support System Using Ultrasound Textures and Radiologic Features to Distinguish Metastasis From Tumor-Free Cervical Lymph Nodes in Patients With Papillary Thyroid Carcinoma.

PubMed

Abbasian Ardakani, Ali; Reiazi, Reza; Mohammadi, Afshin

2018-03-30

This study investigated the potential of a clinical decision support approach for the classification of metastatic and tumor-free cervical lymph nodes (LNs) in papillary thyroid carcinoma on the basis of radiologic and textural analysis through ultrasound (US) imaging. In this research, 170 metastatic and 170 tumor-free LNs were examined by the proposed clinical decision support method. To discover the difference between the groups, US imaging was used for the extraction of radiologic and textural features. The radiologic features in the B-mode scans included the echogenicity, margin, shape, and presence of microcalcification. To extract the textural features, a wavelet transform was applied. A support vector machine classifier was used to classify the LNs. In the training set data, a combination of radiologic and textural features represented the best performance with sensitivity, specificity, accuracy, and area under the curve (AUC) values of 97.14%, 98.57%, 97.86%, and 0.994, respectively, whereas the classification based on radiologic and textural features alone yielded lower performance, with AUCs of 0.964 and 0.922. On testing the data set, the proposed model could classify the tumor-free and metastatic LNs with an AUC of 0.952, which corresponded to sensitivity, specificity, and accuracy of 93.33%, 96.66%, and 95.00%. The clinical decision support method based on textural and radiologic features has the potential to characterize LNs via 2-dimensional US. Therefore, it can be used as a supplementary technique in daily clinical practice to improve radiologists' understanding of conventional US imaging for characterizing LNs. © 2018 by the American Institute of Ultrasound in Medicine.

Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

PubMed Central

White, James Robert; Nagarajan, Niranjan; Pop, Mihai

2009-01-01

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them. We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/. PMID:19360128

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram

PubMed Central

Chu, Catherine. J.; Chan, Arthur; Song, Dan; Staley, Kevin J.; Stufflebeam, Steven M.; Kramer, Mark A.

2017-01-01

Summary Background High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. New Method The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. Results We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. Comparison with Existing Method The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Conclusions Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. PMID:27988323

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors

PubMed Central

Kim, Hyungjin; Park, Sang Joon; Kim, Miso; Kim, Tae Min; Kim, Dong-Wan; Heo, Dae Seog; Goo, Jin Mo

2017-01-01

Purpose To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction. Methods In this institutional review board–approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index. Results Roundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001). Conclusions Radiomic features enable PFS estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors. PMID:29099855

A Method for Analyzing Commonalities in Clinical Trial Target Populations

PubMed Central

He, Zhe; Carini, Simona; Hao, Tianyong; Sim, Ida; Weng, Chunhua

2014-01-01

ClinicalTrials.gov presents great opportunities for analyzing commonalities in clinical trial target populations to facilitate knowledge reuse when designing eligibility criteria of future trials or to reveal potential systematic biases in selecting population subgroups for clinical research. Towards this goal, this paper presents a novel data resource for enabling such analyses. Our method includes two parts: (1) parsing and indexing eligibility criteria text; and (2) mining common eligibility features and attributes of common numeric features (e.g., A1c). We designed and built a database called “Commonalities in Target Populations of Clinical Trials” (COMPACT), which stores structured eligibility criteria and trial metadata in a readily computable format. We illustrate its use in an example analytic module called CONECT using COMPACT as the backend. Type 2 diabetes is used as an example to analyze commonalities in the target populations of 4,493 clinical trials on this disease. PMID:25954450

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features

PubMed Central

Ho, King Chung; Speier, William; El-Saden, Suzie; Arnold, Corey W.

2017-01-01

Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient’s treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden representations from the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional imaging features. Finally, we discuss a strategy to visualize the learned features from the proposed deep learning model. The cross-validation results show that our best classifier achieved an area under the curve of 0.68, which improves significantly over current clinical methods (0.58), demonstrating the potential benefit of using advanced machine learning methods in TSS classification. PMID:29854156

TU-CD-BRB-01: Normal Lung CT Texture Features Improve Predictive Models for Radiation Pneumonitis

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

Krafft, S; The University of Texas Graduate School of Biomedical Sciences, Houston, TX; Briere, T

2015-06-15

Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less

Stabilizing l1-norm prediction models by supervised feature grouping.

PubMed

Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

2016-02-01

Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making. Copyright © 2015 Elsevier Inc. All rights reserved.

New clinical opportunities for retinal vascular imaging: adaptive optics to OCT angiography

NASA Astrophysics Data System (ADS)

Rosen, Richard; Chui, Toco; Weitz, Rishard; Dubra, Alfredo; Carroll, Joseph; Garcia, Patricia; Pinhas, Alexander; Scripsema, Nicole; Mo, Shelley; Agemy, Steven; Krawitz, Brian

2018-03-01

As techniques of retinal imaging have evolved, anatomic features that were only assessable in the laboratory have become available in the clinic for patient care. The retinal capillaries were initially described on microscope sections in the pathology laboratory. As optical methods have advanced these features have become part of the routine clinical landscape inspected daily by physicians. This paper briefly traces the evolution of these techniques and shows how they fit into the modern diagnostic armamentarium of ophthalmic retinal care.

EHR based Genetic Testing Knowledge Base (iGTKB) Development

PubMed Central

2015-01-01

Background The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to fill this gap is to develop an intelligent genetic test recommendation system that not only can provide a comprehensive view of genetic tests as education resources, but also can recommend the most appropriate genetic tests to patients based on clinical evidence. In this study, we developed an EHR based Genetic Testing Knowledge Base for Individualized Medicine (iGTKB). Methods We extracted genetic testing information and patient medical records from EHR systems at Mayo Clinic. Clinical features have been semi-automatically annotated from the clinical notes by applying a Natural Language Processing (NLP) tool, MedTagger suite. To prioritize clinical features for each genetic test, we compared odds ratio across four population groups. Genetic tests, genetic disorders and clinical features with their odds ratios have been applied to establish iGTKB, which is to be integrated into the Genetic Testing Ontology (GTO). Results Overall, there are five genetic tests operated with sample size greater than 100 in 2013 at Mayo Clinic. A total of 1,450 patients who was tested by one of the five genetic tests have been selected. We assembled 243 clinical features from the Human Phenotype Ontology (HPO) for these five genetic tests. There are 60 clinical features with at least one mention in clinical notes of patients taking the test. Twenty-eight clinical features with high odds ratio (greater than 1) have been selected as dominant features and deposited into iGTKB with their associated information about genetic tests and genetic disorders. Conclusions In this study, we developed an EHR based genetic testing knowledge base, iGTKB. iGTKB will be integrated into the GTO by providing relevant clinical evidence, and ultimately to support development of genetic testing recommendation system, iGenetics. PMID:26606281

Clinical Features and Associated Likelihood of Primary Ciliary Dyskinesia in Children and Adolescents

PubMed Central

Ferkol, Thomas W.; Davis, Stephanie D.; Lee, Hye-Seung; Rosenfeld, Margaret; Dell, Sharon D.; Sagel, Scott D.; Milla, Carlos; Olivier, Kenneth N.; Sullivan, Kelli M.; Zariwala, Maimoona A.; Pittman, Jessica E.; Shapiro, Adam J.; Carson, Johnny L.; Krischer, Jeffrey; Hazucha, Milan J.

2016-01-01

Rationale: Primary ciliary dyskinesia (PCD), a genetically heterogeneous, recessive disorder of motile cilia, is associated with distinct clinical features. Diagnostic tests, including ultrastructural analysis of cilia, nasal nitric oxide measurements, and molecular testing for mutations in PCD genes, have inherent limitations. Objectives: To define a statistically valid combination of systematically defined clinical features that strongly associates with PCD in children and adolescents. Methods: Investigators at seven North American sites in the Genetic Disorders of Mucociliary Clearance Consortium prospectively and systematically assessed individuals (aged 0–18 yr) referred due to high suspicion for PCD. The investigators defined specific clinical questions for the clinical report form based on expert opinion. Diagnostic testing was performed using standardized protocols and included nasal nitric oxide measurement, ciliary biopsy for ultrastructural analysis of cilia, and molecular genetic testing for PCD-associated genes. Final diagnoses were assigned as “definite PCD” (hallmark ultrastructural defects and/or two mutations in a PCD-associated gene), “probable/possible PCD” (no ultrastructural defect or genetic diagnosis, but compatible clinical features and nasal nitric oxide level in PCD range), and “other diagnosis or undefined.” Criteria were developed to define early childhood clinical features on the basis of responses to multiple specific queries. Each defined feature was tested by logistic regression. Sensitivity and specificity analyses were conducted to define the most robust set of clinical features associated with PCD. Measurements and Main Results: From 534 participants 18 years of age and younger, 205 were identified as having “definite PCD” (including 164 with two mutations in a PCD-associated gene), 187 were categorized as “other diagnosis or undefined,” and 142 were defined as having “probable/possible PCD.” Participants with “definite PCD” were compared with the “other diagnosis or undefined” group. Four criteria-defined clinical features were statistically predictive of PCD: laterality defect; unexplained neonatal respiratory distress; early-onset, year-round nasal congestion; and early-onset, year-round wet cough (adjusted odds ratios of 7.7, 6.6, 3.4, and 3.1, respectively). The sensitivity and specificity based on the number of criteria-defined clinical features were four features, 0.21 and 0.99, respectively; three features, 0.50 and 0.96, respectively; and two features, 0.80 and 0.72, respectively. Conclusions: Systematically defined early clinical features could help identify children, including infants, likely to have PCD. Clinical trial registered with ClinicalTrials.gov (NCT00323167). PMID:27070726

A multiscale product approach for an automatic classification of voice disorders from endoscopic high-speed videos.

PubMed

Unger, Jakob; Schuster, Maria; Hecker, Dietmar J; Schick, Bernhard; Lohscheller, Joerg

2013-01-01

Direct observation of vocal fold vibration is indispensable for a clinical diagnosis of voice disorders. Among current imaging techniques, high-speed videoendoscopy constitutes a state-of-the-art method capturing several thousand frames per second of the vocal folds during phonation. Recently, a method for extracting descriptive features from phonovibrograms, a two-dimensional image containing the spatio-temporal pattern of vocal fold dynamics, was presented. The derived features are closely related to a clinically established protocol for functional assessment of pathologic voices. The discriminative power of these features for different pathologic findings and configurations has not been assessed yet. In the current study, a collective of 220 subjects is considered for two- and multi-class problems of healthy and pathologic findings. The performance of the proposed feature set is compared to conventional feature reduction routines and was found to clearly outperform these. As such, the proposed procedure shows great potential for diagnostical issues of vocal fold disorders.

Cause and Effect: Testing a Mechanism and Method for the Cognitive Integration of Basic Science.

PubMed

Kulasegaram, Kulamakan; Manzone, Julian C; Ku, Cheryl; Skye, Aimee; Wadey, Veronica; Woods, Nicole N

2015-11-01

Methods of integrating basic science with clinical knowledge are still debated in medical training. One possibility is increasing the spatial and temporal proximity of clinical content to basic science. An alternative model argues that teaching must purposefully expose relationships between the domains. The authors compared different methods of integrating basic science: causal explanations linking basic science to clinical features, presenting both domains separately but in proximity, and simply presenting clinical features First-year undergraduate health professions students were randomized to four conditions: (1) science-causal explanations (SC), (2) basic science before clinical concepts (BC), (3) clinical concepts before basic science (CB), and (4) clinical features list only (FL). Based on assigned conditions, participants were given explanations for four disorders in neurology or rheumatology followed by a memory quiz and diagnostic test consisting of 12 cases which were repeated after one week. Ninety-four participants completed the study. No difference was found on memory test performance, but on the diagnostic test, a condition by time interaction was found (F[3,88] = 3.05, P < .03, ηp = 0.10). Although all groups had similar immediate performance, the SC group had a minimal decrease in performance on delayed testing; the CB and FL groups had the greatest decreases. These results suggest that creating proximity between basic science and clinical concepts may not guarantee cognitive integration. Although cause-and-effect explanations may not be possible for all domains, making explicit and specific connections between domains will likely facilitate the benefits of integration for learners.

A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram.

PubMed

Chu, Catherine J; Chan, Arthur; Song, Dan; Staley, Kevin J; Stufflebeam, Steven M; Kramer, Mark A

2017-02-01

High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable. Copyright © 2016 Elsevier B.V. All rights reserved.

Radiological and endoscopic imaging methods in the management of cystic pancreatic neoplasms.

PubMed

Aslan, Ahmet; Inan, Ibrahim; Orman, Süleyman; Aslan, Mine; Acar, Murat

2017-01-01

The management of cystic pancreatic neoplasm (CPN) is a clinical dilemma because of its clinical presentations and malignant potential. Surgery is the best treatment choice ; however, pancreatic surgery still has high complication rates, even in experienced centers. Imaging methods have a definitive role in the management of CPN and computed tomography, magnetic resonance imaging, and endoscopic ultrasonography are the preferred methods since they can reveal the suspicious features for malignancy. Therefore, radiologists, gastroenterologists, endoscopists, and surgeons should be aware of the common features of CPN, its discrete presentations on imaging methods, and the limitations of these modalities in the management of the disease. This study aims to review the radiological and endoscopic imaging methods used for the management of CPN. © Acta Gastro-Enterologica Belgica.

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Mutual information based feature selection for medical image retrieval

NASA Astrophysics Data System (ADS)

Zhi, Lijia; Zhang, Shaomin; Li, Yan

2018-04-01

In this paper, authors propose a mutual information based method for lung CT image retrieval. This method is designed to adapt to different datasets and different retrieval task. For practical applying consideration, this method avoids using a large amount of training data. Instead, with a well-designed training process and robust fundamental features and measurements, the method in this paper can get promising performance and maintain economic training computation. Experimental results show that the method has potential practical values for clinical routine application.

Markerless motion estimation for motion-compensated clinical brain imaging

NASA Astrophysics Data System (ADS)

Kyme, Andre Z.; Se, Stephen; Meikle, Steven R.; Fulton, Roger R.

2018-05-01

Motion-compensated brain imaging can dramatically reduce the artifacts and quantitative degradation associated with voluntary and involuntary subject head motion during positron emission tomography (PET), single photon emission computed tomography (SPECT) and computed tomography (CT). However, motion-compensated imaging protocols are not in widespread clinical use for these modalities. A key reason for this seems to be the lack of a practical motion tracking technology that allows for smooth and reliable integration of motion-compensated imaging protocols in the clinical setting. We seek to address this problem by investigating the feasibility of a highly versatile optical motion tracking method for PET, SPECT and CT geometries. The method requires no attached markers, relying exclusively on the detection and matching of distinctive facial features. We studied the accuracy of this method in 16 volunteers in a mock imaging scenario by comparing the estimated motion with an accurate marker-based method used in applications such as image guided surgery. A range of techniques to optimize performance of the method were also studied. Our results show that the markerless motion tracking method is highly accurate (<2 mm discrepancy against a benchmarking system) on an ethnically diverse range of subjects and, moreover, exhibits lower jitter and estimation of motion over a greater range than some marker-based methods. Our optimization tests indicate that the basic pose estimation algorithm is very robust but generally benefits from rudimentary background masking. Further marginal gains in accuracy can be achieved by accounting for non-rigid motion of features. Efficiency gains can be achieved by capping the number of features used for pose estimation provided that these features adequately sample the range of head motion encountered in the study. These proof-of-principle data suggest that markerless motion tracking is amenable to motion-compensated brain imaging and holds good promise for a practical implementation in clinical PET, SPECT and CT systems.

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

Aghaei, Faranak; Tan, Maxine; Liu, Hong

Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from bothmore » tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.« less

[Clinical features of patients with Becker muscular dystrophy and deletions of the rod domain of dystrophin gene].

PubMed

Wang, Yanyun; Zhu, Yuling; Yang, Juan; Li, Yaqin; Sun, Jiangwen; Zhan, Yixin; Zhang, Cheng

2018-02-10

OBJECTIVE To explore the clinical features of patients carrying deletions of the rod domain of the dystrophin gene. METHODS Clinical data of 12 Chinese patients with Becker muscular dystrophy (BMD) and such deletions was reviewed. RESULTS Most patients complained of muscle weakness of lower limbs. Two patients had muscle cramps, one had increased creatine kinase (CK) level, and one had dilated cardiomyopathy. CONCLUSION Compared with DMD, the clinical features of BMD are much more variable, particularly for those carrying deletions of the rod domain of the dystrophin gene. Muscular weakness may not be the sole complaint of BMD. The diagnosis of BMD cannot be excluded by moderately elevated CK. For male patients with dilated cardiomyopathy, the possibility of BMD should be considered.

Towards a taxonomy for integrated care: a mixed-methods study

PubMed Central

Valentijn, Pim P.; Boesveld, Inge C.; van der Klauw, Denise M.; Ruwaard, Dirk; Struijs, Jeroen N.; Molema, Johanna J.W.; Bruijnzeels, Marc A.; Vrijhoef, Hubertus JM.

2015-01-01

Introduction Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. Methods First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. Results The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. Discussion This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective. PMID:25759607

Cardiovascular Disease and 10-Year Mortality in Postmenopausal Women with Clinical Features of Polycystic Ovary Syndrome

PubMed Central

Shaw, Leslee J.; Azziz, Ricardo; Stanczyk, Frank Z.; Sopko, George; Braunstein, Glenn D.; Kelsey, Sheryl F.; Kip, Kevin E.; Cooper-DeHoff, Rhonda M.; Johnson, B. Delia; Vaccarino, Viola; Reis, Steven E.; Bittner, Vera; Hodgson, T. Keta; Rogers, William; Pepine, Carl J.

2016-01-01

Abstract Background: Women with polycystic ovary syndrome (PCOS) have greater cardiac risk factor clustering but the link with mortality is incompletely described. Objective: To evaluate outcomes in 295 postmenopausal women enrolled in the National Institutes of Health–National Heart, Lung, and Blood Institute (NIH-NHLBI) sponsored Women's Ischemia Syndrome Evaluation (WISE) study according to clinical features of PCOS. Materials and Methods: A total of 25/295 (8%) women had clinical features of PCOS defined by a premenopausal history of irregular menses and current biochemical evidence of hyperandrogenemia, defined as the top quartile of androstenedione (≥701 pg/mL), testosterone (≥30.9 ng/dL), or free testosterone (≥4.5 pg/mL). Cox proportional hazard model estimated death (n = 80). Results: Women with clinical features of PCOS had an earlier menopause (p = 0.01), were more often smokers (p < 0.04), and trended toward more angiographic coronary artery disease (CAD) (p = 0.07) than women without these features. Cumulative 10-year mortality was 28% for women with (n = 25) versus 27% without clinical features of PCOS (n = 270) (p = 0.85). PCOS was not a significant predictor (p = NS) in prognostic models including diabetes, waist circumference, hypertension, and angiographic CAD. Conclusion: From this longer-term follow up of a relatively small cohort of postmenopausal women with suspected ischemia, the prevalence of PCOS is similar to the general population, and clinical features of PCOS are not associated with CAD or mortality. These findings question whether identification of clinical features of PCOS in postmenopausal women who already have known cardiovascular disease provides any additional opportunity for risk factor intervention. PMID:27267867

Evaluation of features to support safety and quality in general practice clinical software

PubMed Central

2011-01-01

Background Electronic prescribing is now the norm in many countries. We wished to find out if clinical software systems used by general practitioners in Australia include features (functional capabilities and other characteristics) that facilitate improved patient safety and care, with a focus on quality use of medicines. Methods Seven clinical software systems used in general practice were evaluated. Fifty software features that were previously rated as likely to have a high impact on safety and/or quality of care in general practice were tested and are reported here. Results The range of results for the implementation of 50 features across the 7 clinical software systems was as follows: 17-31 features (34-62%) were fully implemented, 9-13 (18-26%) partially implemented, and 9-20 (18-40%) not implemented. Key findings included: Access to evidence based drug and therapeutic information was limited. Decision support for prescribing was available but varied markedly between systems. During prescribing there was potential for medicine mis-selection in some systems, and linking a medicine with its indication was optional. The definition of 'current medicines' versus 'past medicines' was not always clear. There were limited resources for patients, and some medicines lists for patients were suboptimal. Results were provided to the software vendors, who were keen to improve their systems. Conclusions The clinical systems tested lack some of the features expected to support patient safety and quality of care. Standards and certification for clinical software would ensure that safety features are present and that there is a minimum level of clinical functionality that clinicians could expect to find in any system.

Novel method to predict body weight in children based on age and morphological facial features.

PubMed

Huang, Ziyin; Barrett, Jeffrey S; Barrett, Kyle; Barrett, Ryan; Ng, Chee M

2015-04-01

A new and novel approach of predicting the body weight of children based on age and morphological facial features using a three-layer feed-forward artificial neural network (ANN) model is reported. The model takes in four parameters, including age-based CDC-inferred median body weight and three facial feature distances measured from digital facial images. In this study, thirty-nine volunteer subjects with age ranging from 6-18 years old and BW ranging from 18.6-96.4 kg were used for model development and validation. The final model has a mean prediction error of 0.48, a mean squared error of 18.43, and a coefficient of correlation of 0.94. The model shows significant improvement in prediction accuracy over several age-based body weight prediction methods. Combining with a facial recognition algorithm that can detect, extract and measure the facial features used in this study, mobile applications that incorporate this body weight prediction method may be developed for clinical investigations where access to scales is limited. © 2014, The American College of Clinical Pharmacology.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

PubMed Central

Chen, Huaidong; Chen, Wei; Liu, Chenglin; Zhang, Le; Su, Jing; Zhou, Xiaobo

2016-01-01

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center’s electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient’s cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered “ER module”, which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets. PMID:27427091

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

NASA Astrophysics Data System (ADS)

Chen, Huaidong; Chen, Wei; Liu, Chenglin; Zhang, Le; Su, Jing; Zhou, Xiaobo

2016-07-01

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center’s electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient’s cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered “ER module”, which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.

Prevalence of upper airway obstruction in patients with apparently asymptomatic euthyroid multi nodular goitre

PubMed Central

Menon, Sunil K.; Jagtap, Varsha S.; Sarathi, Vijaya; Lila, Anurag R.; Bandgar, Tushar R.; Menon, Padmavathy S; Shah, Nalini S.

2011-01-01

Aims: To study the prevalence of upper airway obstruction (UAO) in “apparently asymptomatic” patients with euthyroid multinodular goitre (MNG) and find correlation between clinical features, UAO on pulmonary function test (PFT) and tracheal narrowing on computerised tomography (CT). Materials and Methods: Consecutive patients with apparently asymptomatic euthyroid MNG attending thyroid clinic in a tertiary centre underwent clinical examination to elicit features of UAO, PFT, and CT of neck and chest. Statistical Analysis Used: Statistical analysis was done with SPSS version 11.5 using paired t-test, Chi square test, and Fisher's exact test. P value of <0.05 was considered to be significant. Results: Fifty-six patients (52 females and four males) were studied. The prevalence of UAO (PFT) and significant tracheal narrowing (CT) was 14.3%. and 9.3%, respectively. Clinical features failed to predict UAO or significant tracheal narrowing. Tracheal narrowing (CT) did not correlate with UAO (PFT). Volume of goitre significantly correlated with degree of tracheal narrowing. Conclusions: Clinical features do not predict UAO on PFT or tracheal narrowing on CT in apparently asymptomatic patients with euthyroid MNG. PMID:21966649

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Acne severity grading: determining essential clinical components and features using a Delphi consensus.

PubMed

Tan, Jerry; Wolfe, Barat; Weiss, Jonathan; Stein-Gold, Linda; Bikowski, Joseph; Del Rosso, James; Webster, Guy F; Lucky, Anne; Thiboutot, Diane; Wilkin, Jonathan; Leyden, James; Chren, Mary-Margaret

2012-08-01

There are multiple global scales for acne severity grading but no singular standard. Our objective was to determine the essential clinical components (content items) and features (property-related items) for an acne global grading scale for use in research and clinical practice using an iterative method, the Delphi process. Ten acne experts were invited to participate in a Web-based Delphi survey comprising 3 iterative rounds of questions. In round 1, the experts identified the following clinical components (primary acne lesions, number of lesions, extent, regional involvement, secondary lesions, and patient experiences) and features (clinimetric properties, ease of use, categorization of severity based on photographs or text, and acceptance by all stakeholders). In round 2, consensus for inclusion in the scale was established for primary lesions, number, sites, and extent; as well as clinimetric properties and ease of use. In round 3, consensus for inclusion was further established for categorization and acceptance. Patient experiences were excluded and no consensus was achieved for secondary lesions. The Delphi panel consisted solely of the United States (U.S.)-based acne experts. Using an established method for achieving consensus, experts in acne vulgaris concluded that an ideal acne global grading scale would comprise the essential clinical components of primary acne lesions, their quantity, extent, and facial and extrafacial sites of involvement; with features of clinimetric properties, categorization, efficiency, and acceptance. Copyright © 2011 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Mirror movements in parkinsonism: evaluation of a new clinical sign

PubMed Central

Espay, A; Li, J; Johnston, L; Chen, R; Lang, A

2005-01-01

Background: Mirror movements (MM) are not widely appreciated in parkinsonism and no report has evaluated this clinical sign in detail. Objectives: To define the parkinsonian clinical features associated with MM in patients with early, asymmetric parkinsonism. Methods: Twenty seven patients with early Parkinson's disease were evaluated using a standardised videotaping protocol. MM were scored from blinded video assessment using a clinical scale that rates the amplitude, distribution, and proportion of mirroring in the less affected limb. Parkinsonian features were combined into axial and lateralised scores using related items of the Unified Parkinson's Disease Rating Scale. Results: MM were present in 24 of 27 patients. There was a significant linear correlation between the degree of asymmetry of motor deficits and MM on the less affected side. The effect of asymmetry was greater when the proportional rather than the absolute motor difference between sides was largest. Asymmetry in leg rigidity was the most important examination feature in the prediction of contralateral foot mirroring. Conclusions: MM are a clinical feature of the unaffected or less affected side in mild asymmetric parkinsonism. Their presence may be a useful clinical finding in early parkinsonism. PMID:16170075

Molecular and Clinical Characterization of Chikungunya Virus Infections in Southeast Mexico.

PubMed

Galán-Huerta, Kame A; Martínez-Landeros, Erik; Delgado-Gallegos, Juan L; Caballero-Sosa, Sandra; Malo-García, Iliana R; Fernández-Salas, Ildefonso; Ramos-Jiménez, Javier; Rivas-Estilla, Ana M

2018-05-09

Chikungunya fever is an arthropod-borne infection caused by Chikungunya virus (CHIKV). Even though clinical features of Chikungunya fever in the Mexican population have been described before, there is no detailed information. The aim of this study was to perform a full description of the clinical features in confirmed Chikungunya-infected patients and describe the molecular epidemiology of CHIKV. We evaluated febrile patients who sought medical assistance in Tapachula, Chiapas, Mexico, from June through July 2015. Infection was confirmed with molecular and serological methods. Viruses were isolated and the E1 gene was sequenced. Phylogeny reconstruction was inferred using maximum-likelihood and maximum clade credibility approaches. We studied 52 patients with confirmed CHIKV infection. They were more likely to have wrist, metacarpophalangeal, and knee arthralgia. Two combinations of clinical features were obtained to differentiate between Chikungunya fever and acute undifferentiated febrile illness. We obtained 10 CHIKV E1 sequences that grouped with the Asian lineage. Seven strains diverged from the formerly reported. Patients infected with the divergent CHIKV strains showed a broader spectrum of clinical manifestations. We defined the complete clinical features of Chikungunya fever in patients from Southeastern Mexico. Our results demonstrate co-circulation of different CHIKV strains in the state of Chiapas.

Postmenopausal Women with a History of Irregular Menses and Elevated Androgen Measurements at High Risk for Worsening Cardiovascular Event-Free Survival: Results from the National Institutes of Health—National Heart, Lung, and Blood Institute Sponsored Women’s Ischemia Syndrome Evaluation

PubMed Central

Shaw, Leslee J.; Bairey Merz, C. Noel; Azziz, Ricardo; Stanczyk, Frank Z.; Sopko, George; Braunstein, Glenn D.; Kelsey, Sheryl F.; Kip, Kevin E.; Cooper-DeHoff, Rhonda M.; Johnson, B. Delia; Vaccarino, Viola; Reis, Steven E.; Bittner, Vera; Hodgson, T. Keta; Rogers, William; Pepine, Carl J.

2008-01-01

Background: Women with polycystic ovary syndrome (PCOS) have a greater clustering of cardiac risk factors. However, the link between PCOS and cardiovascular (CV) disease is incompletely described. Objective: The aim of this analysis was to evaluate the risk of CV events in 390 postmenopausal women enrolled in the National Institutes of Health–National Heart, Lung, and Blood Institute (NIH-NHLBI) sponsored Women’s Ischemia Syndrome Evaluation (WISE) study according to clinical features of PCOS. Methods: A total of 104 women had clinical features of PCOS defined by a premenopausal history of irregular menses and current biochemical evidence of hyperandrogenemia. Hyperandrogenemia was defined as the top quartile of androstenedione (≥701 pg/ml), testosterone (≥30.9 ng/dl), or free testosterone (≥4.5 pg/ml). Cox proportional hazard model was fit to estimate CV death or myocardial infarction (n = 55). Results: Women with clinical features of PCOS were more often diabetic (P < 0.0001), obese (P = 0.005), had the metabolic syndrome (P < 0.0001), and had more angiographic coronary artery disease (CAD) (P = 0.04) compared to women without clinical features of PCOS. Cumulative 5-yr CV event-free survival was 78.9% for women with clinical features of PCOS (n = 104) vs. 88.7% for women without clinical features of PCOS (n = 286) (P = 0.006). PCOS remained a significant predictor (P < 0.01) in prognostic models including diabetes, waist circumference, hypertension, and angiographic CAD as covariates. Conclusion: Among postmenopausal women evaluated for suspected ischemia, clinical features of PCOS are associated with more angiographic CAD and worsening CV event-free survival. Identification of postmenopausal women with clinical features of PCOS may provide an opportunity for risk factor intervention for the prevention of CAD and CV events. PMID:18182456

SU-F-R-53: CT-Based Radiomics Analysis of Non-Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

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

Huynh, E; Coroller, T; Narayan, V

Purpose: Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non-small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high-risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients. Methods: Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I-II NSCLC patients treated with SBRT.more » Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p-values were corrected for multiple testing using the false discovery rate. Results: Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617). Conclusion: Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high-risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome. R.M. had consulting interest with Amgen (ended in 2015).« less

Clinical and molecular features of human rhinovirus C

PubMed Central

Bochkov, Yury A.; Gern, James E.

2012-01-01

A newly discovered group of human rhinoviruses (HRVs) has been classified as the HRV-C species based on distinct genomic features. HRV-Cs circulate worldwide, and are important causes of upper and lower respiratory illnesses. Methods to culture and produce these viruses have recently been developed, and should enable identification of unique features of HRV-C replication and biology. PMID:22285901

Detrended fluctuation analysis for major depressive disorder.

PubMed

Mumtaz, Wajid; Malik, Aamir Saeed; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Amin, Hafeezullah

2015-01-01

Clinical utility of Electroencephalography (EEG) based diagnostic studies is less clear for major depressive disorder (MDD). In this paper, a novel machine learning (ML) scheme was presented to discriminate the MDD patients and healthy controls. The proposed method inherently involved feature extraction, selection, classification and validation. The EEG data acquisition involved eyes closed (EC) and eyes open (EO) conditions. At feature extraction stage, the de-trended fluctuation analysis (DFA) was performed, based on the EEG data, to achieve scaling exponents. The DFA was performed to analyzes the presence or absence of long-range temporal correlations (LRTC) in the recorded EEG data. The scaling exponents were used as input features to our proposed system. At feature selection stage, 3 different techniques were used for comparison purposes. Logistic regression (LR) classifier was employed. The method was validated by a 10-fold cross-validation. As results, we have observed that the effect of 3 different reference montages on the computed features. The proposed method employed 3 different types of feature selection techniques for comparison purposes as well. The results show that the DFA analysis performed better in LE data compared with the IR and AR data. In addition, during Wilcoxon ranking, the AR performed better than LE and IR. Based on the results, it was concluded that the DFA provided useful information to discriminate the MDD patients and with further validation can be employed in clinics for diagnosis of MDD.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

PubMed Central

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Fu, Jing-Jing; Xu, Zhao-Xia; Guo, Rui; Qian, Peng

2012-01-01

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:22719781

Lesion classification using clinical and visual data fusion by multiple kernel learning

NASA Astrophysics Data System (ADS)

Kisilev, Pavel; Hashoul, Sharbell; Walach, Eugene; Tzadok, Asaf

2014-03-01

To overcome operator dependency and to increase diagnosis accuracy in breast ultrasound (US), a lot of effort has been devoted to developing computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Unfortunately, the efficacy of such CAD systems is limited since they rely on correct automatic lesions detection and localization, and on robustness of features computed based on the detected areas. In this paper we propose a new approach to boost the performance of a Machine Learning based CAD system, by combining visual and clinical data from patient files. We compute a set of visual features from breast ultrasound images, and construct the textual descriptor of patients by extracting relevant keywords from patients' clinical data files. We then use the Multiple Kernel Learning (MKL) framework to train SVM based classifier to discriminate between benign and malignant cases. We investigate different types of data fusion methods, namely, early, late, and intermediate (MKL-based) fusion. Our database consists of 408 patient cases, each containing US images, textual description of complaints and symptoms filled by physicians, and confirmed diagnoses. We show experimentally that the proposed MKL-based approach is superior to other classification methods. Even though the clinical data is very sparse and noisy, its MKL-based fusion with visual features yields significant improvement of the classification accuracy, as compared to the image features only based classifier.

Medical student appraisal: searching on smartphones.

PubMed

Khalifian, S; Markman, T; Sampognaro, P; Mitchell, S; Weeks, S; Dattilo, J

2013-01-01

The rapidly growing industry for mobile medical applications provides numerous smartphone resources designed for healthcare professionals. However, not all applications are equally useful in addressing the questions of early medical trainees. Three popular, free, mobile healthcare applications were evaluated along with a Google(TM) web search on both Apple(TM) and Android(TM) devices. Six medical students at a large academic hospital evaluated each application for a one-week period while on various clinical rotations. Google(TM) was the most frequently used search method and presented multimedia resources but was inefficient for obtaining clinical management information. Epocrates(TM) Pill ID feature was praised for its clinical utility. Medscape(TM) had the highest satisfaction of search and excelled through interactive educational features. Micromedex(TM) offered both FDA and off-label dosing for drugs. Google(TM) was the preferred search method for questions related to basic disease processes and multimedia resources, but was inadequate for clinical management. Caution should also be exercised when using Google(TM) in front of patients. Medscape(TM) was the most appealing application due to a broad scope of content and educational features relevant to medical trainees. Students should also be cognizant of how mobile technology may be perceived by their evaluators to avoid false impressions.

Mining hidden data to predict patient prognosis: texture feature extraction and machine learning in mammography

NASA Astrophysics Data System (ADS)

Leighs, J. A.; Halling-Brown, M. D.; Patel, M. N.

2018-03-01

The UK currently has a national breast cancer-screening program and images are routinely collected from a number of screening sites, representing a wealth of invaluable data that is currently under-used. Radiologists evaluate screening images manually and recall suspicious cases for further analysis such as biopsy. Histological testing of biopsy samples confirms the malignancy of the tumour, along with other diagnostic and prognostic characteristics such as disease grade. Machine learning is becoming increasingly popular for clinical image classification problems, as it is capable of discovering patterns in data otherwise invisible. This is particularly true when applied to medical imaging features; however clinical datasets are often relatively small. A texture feature extraction toolkit has been developed to mine a wide range of features from medical images such as mammograms. This study analysed a dataset of 1,366 radiologist-marked, biopsy-proven malignant lesions obtained from the OPTIMAM Medical Image Database (OMI-DB). Exploratory data analysis methods were employed to better understand extracted features. Machine learning techniques including Classification and Regression Trees (CART), ensemble methods (e.g. random forests), and logistic regression were applied to the data to predict the disease grade of the analysed lesions. Prediction scores of up to 83% were achieved; sensitivity and specificity of the models trained have been discussed to put the results into a clinical context. The results show promise in the ability to predict prognostic indicators from the texture features extracted and thus enable prioritisation of care for patients at greatest risk.

Incorporating clinical metadata with digital image features for automated identification of cutaneous melanoma.

PubMed

Liu, Z; Sun, J; Smith, M; Smith, L; Warr, R

2013-11-01

Computer-assisted diagnosis (CAD) of malignant melanoma (MM) has been advocated to help clinicians to achieve a more objective and reliable assessment. However, conventional CAD systems examine only the features extracted from digital photographs of lesions. Failure to incorporate patients' personal information constrains the applicability in clinical settings. To develop a new CAD system to improve the performance of automatic diagnosis of melanoma, which, for the first time, incorporates digital features of lesions with important patient metadata into a learning process. Thirty-two features were extracted from digital photographs to characterize skin lesions. Patients' personal information, such as age, gender and, lesion site, and their combinations, was quantified as metadata. The integration of digital features and metadata was realized through an extended Laplacian eigenmap, a dimensionality-reduction method grouping lesions with similar digital features and metadata into the same classes. The diagnosis reached 82.1% sensitivity and 86.1% specificity when only multidimensional digital features were used, but improved to 95.2% sensitivity and 91.0% specificity after metadata were incorporated appropriately. The proposed system achieves a level of sensitivity comparable with experienced dermatologists aided by conventional dermoscopes. This demonstrates the potential of our method for assisting clinicians in diagnosing melanoma, and the benefit it could provide to patients and hospitals by greatly reducing unnecessary excisions of benign naevi. This paper proposes an enhanced CAD system incorporating clinical metadata into the learning process for automatic classification of melanoma. Results demonstrate that the additional metadata and the mechanism to incorporate them are useful for improving CAD of melanoma. © 2013 British Association of Dermatologists.

Tissue feature-based intra-fractional motion tracking for stereoscopic x-ray image guided radiotherapy

NASA Astrophysics Data System (ADS)

Xie, Yaoqin; Xing, Lei; Gu, Jia; Liu, Wu

2013-06-01

Real-time knowledge of tumor position during radiation therapy is essential to overcome the adverse effect of intra-fractional organ motion. The goal of this work is to develop a tumor tracking strategy by effectively utilizing the inherent image features of stereoscopic x-ray images acquired during dose delivery. In stereoscopic x-ray image guided radiation delivery, two orthogonal x-ray images are acquired either simultaneously or sequentially. The essence of markerless tumor tracking is the reliable identification of inherent points with distinct tissue features on each projection image and their association between two images. The identification of the feature points on a planar x-ray image is realized by searching for points with high intensity gradient. The feature points are associated by using the scale invariance features transform descriptor. The performance of the proposed technique is evaluated by using images of a motion phantom and four archived clinical cases acquired using either a CyberKnife equipped with a stereoscopic x-ray imaging system, or a LINAC equipped with an onboard kV imager and an electronic portal imaging device. In the phantom study, the results obtained using the proposed method agree with the measurements to within 2 mm in all three directions. In the clinical study, the mean error is 0.48 ± 0.46 mm for four patient data with 144 sequential images. In this work, a tissue feature-based tracking method for stereoscopic x-ray image guided radiation therapy is developed. The technique avoids the invasive procedure of fiducial implantation and may greatly facilitate the clinical workflow.

Identifying clinical features in primary care electronic health record studies: methods for codelist development.

PubMed

Watson, Jessica; Nicholson, Brian D; Hamilton, Willie; Price, Sarah

2017-11-22

Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. The codelist was used to estimate the frequency of shortness of breath in a cohort of 28 216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13 091 episodes of shortness of breath were identified in the cohort of 28 216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review. © 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.

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets.

PubMed

Rouillard, Andrew D; Hurle, Mark R; Agarwal, Pankaj

2018-05-01

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC = 0.57 and AUPR = 0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub at https://github.com/arouillard/omic-features-successful-targets.

Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

PubMed

Lasko, Thomas A; Denny, Joshua C; Levy, Mia A

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies.

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

PubMed Central

Lasko, Thomas A.; Denny, Joshua C.; Levy, Mia A.

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies. PMID:23826094

Texture-specific bag of visual words model and spatial cone matching-based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images.

PubMed

Xu, Yingying; Lin, Lanfen; Hu, Hongjie; Wang, Dan; Zhu, Wenchao; Wang, Jian; Han, Xian-Hua; Chen, Yen-Wei

2018-01-01

The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected. The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system. The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

Lumbar hernia in South Korea: different from that in foreign literature?

PubMed

Park, S H; Chung, H S; Song, S H

2015-10-01

This study aimed to analyze the clinical features of lumbar hernia reported in South Korea and compare these features with those reported in foreign literature. From January 1968 through December 2013, 13 cases reported in South Korea were included in the study. The variables compared were age, sex, main symptoms at hospital visit, etiology, location, herniated contents, lateralization, defect size, diagnostic methods, surgical methods, surgical opinions, and recurrence. In the South Korean cases, women outnumbered men (3.3:1) and no significant differences were found in the herniated side (left:right, 1.1:1). In contrast, in the foreign cases, men outnumbered women (3:1) and left-sided hernia was dominant (2:1). Moreover, in most of the foreign cases, patients were aged 50-70 years, whereas in the South Korean cases, none of the patients were in their 50 s. However, no substantial differences were found in etiology, anatomical locations, symptoms, and herniated contents. This research revealed that few clinical features of lumbar hernias in South Korea differ from those reported in foreign literature. Thirteen cases were analyzed in the present study, and results obtained from such a small sample size cannot be generalized with certainty. Therefore, more cases should be collected for a definitive analysis. Despite this limitation, this study is important because it is the first attempt to collect and analyze the clinical features of lumbar hernia in South Korea. This study will serve as a basis for future studies investigating the clinical features of lumbar hernia cases in South Korea.

Linking metabolic network features to phenotypes using sparse group lasso.

PubMed

Samal, Satya Swarup; Radulescu, Ovidiu; Weber, Andreas; Fröhlich, Holger

2017-11-01

Integration of metabolic networks with '-omics' data has been a subject of recent research in order to better understand the behaviour of such networks with respect to differences between biological and clinical phenotypes. Under the conditions of steady state of the reaction network and the non-negativity of fluxes, metabolic networks can be algebraically decomposed into a set of sub-pathways often referred to as extreme currents (ECs). Our objective is to find the statistical association of such sub-pathways with given clinical outcomes, resulting in a particular instance of a self-contained gene set analysis method. In this direction, we propose a method based on sparse group lasso (SGL) to identify phenotype associated ECs based on gene expression data. SGL selects a sparse set of feature groups and also introduces sparsity within each group. Features in our model are clusters of ECs, and feature groups are defined based on correlations among these features. We apply our method to metabolic networks from KEGG database and study the association of network features to prostate cancer (where the outcome is tumor and normal, respectively) as well as glioblastoma multiforme (where the outcome is survival time). In addition, simulations show the superior performance of our method compared to global test, which is an existing self-contained gene set analysis method. R code (compatible with version 3.2.5) is available from http://www.abi.bit.uni-bonn.de/index.php?id=17. samal@combine.rwth-aachen.de or frohlich@bit.uni-bonn.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A phantom design for assessment of detectability in PET imaging

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

Wollenweber, Scott D., E-mail: scott.wollenweber@g

2016-09-15

Purpose: The primary clinical role of positron emission tomography (PET) imaging is the detection of anomalous regions of {sup 18}F-FDG uptake, which are often indicative of malignant lesions. The goal of this work was to create a task-configurable fillable phantom for realistic measurements of detectability in PET imaging. Design goals included simplicity, adjustable feature size, realistic size and contrast levels, and inclusion of a lumpy (i.e., heterogeneous) background. Methods: The detection targets were hollow 3D-printed dodecahedral nylon features. The exostructure sphere-like features created voids in a background of small, solid non-porous plastic (acrylic) spheres inside a fillable tank. The featuresmore » filled at full concentration while the background concentration was reduced due to filling only between the solid spheres. Results: Multiple iterations of feature size and phantom construction were used to determine a configuration at the limit of detectability for a PET/CT system. A full-scale design used a 20 cm uniform cylinder (head-size) filled with a fixed pattern of features at a contrast of approximately 3:1. Known signal-present and signal-absent PET sub-images were extracted from multiple scans of the same phantom and with detectability in a challenging (i.e., useful) range. These images enabled calculation and comparison of the quantitative observer detectability metrics between scanner designs and image reconstruction methods. The phantom design has several advantages including filling simplicity, wall-less contrast features, the control of the detectability range via feature size, and a clinically realistic lumpy background. Conclusions: This phantom provides a practical method for testing and comparison of lesion detectability as a function of imaging system, acquisition parameters, and image reconstruction methods and parameters.« less

Role of Altered mGluR Activity in Cognitive Impairments in TSC: Implications for a Novel Method of Treatment

DTIC Science & Technology

2012-04-01

defining factor. The most common clinical features are mental retardation, epilepsy, autism , anxiety and mood disorders. Fragile X syndrome (FXS...another form of inherited mental retardation and autism , shares many of the same molecular and clinical features as TSC. Much of the pathophysiology in FXS...modulation of mGluR activity with PAMs may serve as a therapeutic intervention for the treatment of TSC. 15. SUBJECT TERMS autism , Tuberous Sclerosis

Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics

PubMed Central

Tyan, Marina; Fernández Pozo, Rubén; Toledano, Doroteo; Lopez Gonzalo, Eduardo; Alcazar Ramirez, Jose Daniel; Hernandez Gomez, Luis Alfonso

2017-01-01

Background Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients’ facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA. Objective The objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender. Methods All the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered. Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed. Results The results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak. Conclusions In accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population. PMID:29109068

Molecular and Clinical Characterization of Chikungunya Virus Infections in Southeast Mexico

PubMed Central

Martínez-Landeros, Erik; Delgado-Gallegos, Juan L.; Caballero-Sosa, Sandra; Malo-García, Iliana R.

2018-01-01

Chikungunya fever is an arthropod-borne infection caused by Chikungunya virus (CHIKV). Even though clinical features of Chikungunya fever in the Mexican population have been described before, there is no detailed information. The aim of this study was to perform a full description of the clinical features in confirmed Chikungunya-infected patients and describe the molecular epidemiology of CHIKV. We evaluated febrile patients who sought medical assistance in Tapachula, Chiapas, Mexico, from June through July 2015. Infection was confirmed with molecular and serological methods. Viruses were isolated and the E1 gene was sequenced. Phylogeny reconstruction was inferred using maximum-likelihood and maximum clade credibility approaches. We studied 52 patients with confirmed CHIKV infection. They were more likely to have wrist, metacarpophalangeal, and knee arthralgia. Two combinations of clinical features were obtained to differentiate between Chikungunya fever and acute undifferentiated febrile illness. We obtained 10 CHIKV E1 sequences that grouped with the Asian lineage. Seven strains diverged from the formerly reported. Patients infected with the divergent CHIKV strains showed a broader spectrum of clinical manifestations. We defined the complete clinical features of Chikungunya fever in patients from Southeastern Mexico. Our results demonstrate co-circulation of different CHIKV strains in the state of Chiapas. PMID:29747416

Classification of optical coherence tomography images for diagnosing different ocular diseases

NASA Astrophysics Data System (ADS)

Gholami, Peyman; Sheikh Hassani, Mohsen; Kuppuswamy Parthasarathy, Mohana; Zelek, John S.; Lakshminarayanan, Vasudevan

2018-03-01

Optical Coherence tomography (OCT) images provide several indicators, e.g., the shape and the thickness of different retinal layers, which can be used for various clinical and non-clinical purposes. We propose an automated classification method to identify different ocular diseases, based on the local binary pattern features. The database consists of normal and diseased human eye SD-OCT images. We use a multiphase approach for building our classifier, including preprocessing, Meta learning, and active learning. Pre-processing is applied to the data to handle missing features from images and replace them with the mean or median of the corresponding feature. All the features are run through a Correlation-based Feature Subset Selection algorithm to detect the most informative features and omit the less informative ones. A Meta learning approach is applied to the data, in which a SVM and random forest are combined to obtain a more robust classifier. Active learning is also applied to strengthen our classifier around the decision boundary. The primary experimental results indicate that our method is able to differentiate between the normal and non-normal retina with an area under the ROC curve (AUC) of 98.6% and also to diagnose the three common retina-related diseases, i.e., Age-related Macular Degeneration, Diabetic Retinopathy, and Macular Hole, with an AUC of 100%, 95% and 83.8% respectively. These results indicate a better performance of the proposed method compared to most of the previous works in the literature.

SU-F-R-45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre-Treatment and Post-Treatment FDG PET Image for NSCLC Patients

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

Ma, C; Yin, Y

Purpose: The purpose of this research is investigating which texture features extracted from FDG-PET images by gray-level co-occurrence matrix(GLCM) have a higher prognostic value than the other texture features. Methods: 21 non-small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F-FDG PET/CT scans with both pre-treatment and post-treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculatedmore » by using post-treatment features to subtract pre-treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features. Results: The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942. Conclusion: This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.« less

A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.

PubMed

Hasan, Mehedi; Kotov, Alexander; Carcone, April; Dong, Ming; Naar, Sylvie; Hartlieb, Kathryn Brogan

2016-08-01

This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), Random Forest (RF), AdaBoost, DiscLDA, Conditional Random Fields (CRF) and Convolutional Neural Network (CNN) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the Linguistic Inquiry and Word Count dictionaries) features. We found out that, when the number of classes is large, the performance of CNN and CRF is inferior to SVM. When only lexical features were used, interview transcripts were automatically annotated by SVM with the highest classification accuracy among all classifiers of 70.8%, 61% and 53.7% based on the codebooks consisting of 17, 20 and 41 codes, respectively. Using contextual and semantic features, as well as their combination, in addition to lexical ones, improved the accuracy of SVM for annotation of utterances in motivational interview transcripts with a codebook consisting of 17 classes to 71.5%, 74.2%, and 75.1%, respectively. Our results demonstrate the potential of using machine learning methods in conjunction with lexical, semantic and contextual features for automatic annotation of clinical interview transcripts with near-human accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

Image-based modeling of tumor shrinkage in head and neck radiation therapy1

PubMed Central

Chao, Ming; Xie, Yaoqin; Moros, Eduardo G.; Le, Quynh-Thu; Xing, Lei

2010-01-01

Purpose: Understanding the kinetics of tumor growth∕shrinkage represents a critical step in quantitative assessment of therapeutics and realization of adaptive radiation therapy. This article presents a novel framework for image-based modeling of tumor change and demonstrates its performance with synthetic images and clinical cases. Methods: Due to significant tumor tissue content changes, similarity-based models are not suitable for describing the process of tumor volume changes. Under the hypothesis that tissue features in a tumor volume or at the boundary region are partially preserved, the kinetic change was modeled in two steps: (1) Autodetection of homologous tissue features shared by two input images using the scale invariance feature transformation (SIFT) method; and (2) establishment of a voxel-to-voxel correspondence between the images for the remaining spatial points by interpolation. The correctness of the tissue feature correspondence was assured by a bidirectional association procedure, where SIFT features were mapped from template to target images and reversely. A series of digital phantom experiments and five head and neck clinical cases were used to assess the performance of the proposed technique. Results: The proposed technique can faithfully identify the known changes introduced when constructing the digital phantoms. The subsequent feature-guided thin plate spline calculation reproduced the “ground truth” with accuracy better than 1.5 mm. For the clinical cases, the new algorithm worked reliably for a volume change as large as 30%. Conclusions: An image-based tumor kinetic algorithm was developed to model the tumor response to radiation therapy. The technique provides a practical framework for future application in adaptive radiation therapy. PMID:20527569

Interstitial Features at Chest CT Enhance the Deleterious Effects of Emphysema in the COPDGene Cohort.

PubMed

Ash, Samuel Y; Harmouche, Rola; Ross, James C; Diaz, Alejandro A; Rahaghi, Farbod N; Sanchez-Ferrero, Gonzalo Vegas; Putman, Rachel K; Hunninghake, Gary M; Onieva, Jorge Onieva; Martinez, Fernando J; Choi, Augustine M; Bowler, Russell P; Lynch, David A; Hatabu, Hiroto; Bhatt, Surya P; Dransfield, Mark T; Wells, J Michael; Rosas, Ivan O; San Jose Estepar, Raul; Washko, George R

2018-06-05

Purpose To determine if interstitial features at chest CT enhance the effect of emphysema on clinical disease severity in smokers without clinical pulmonary fibrosis. Materials and Methods In this retrospective cohort study, an objective CT analysis tool was used to measure interstitial features (reticular changes, honeycombing, centrilobular nodules, linear scar, nodular changes, subpleural lines, and ground-glass opacities) and emphysema in 8266 participants in a study of chronic obstructive pulmonary disease (COPD) called COPDGene (recruited between October 2006 and January 2011). Additive differences in patients with emphysema with interstitial features and in those without interstitial features were analyzed by using t tests, multivariable linear regression, and Kaplan-Meier analysis. Multivariable linear and Cox regression were used to determine if interstitial features modified the effect of continuously measured emphysema on clinical measures of disease severity and mortality. Results Compared with individuals with emphysema alone, those with emphysema and interstitial features had a higher percentage predicted forced expiratory volume in 1 second (absolute difference, 6.4%; P < .001), a lower percentage predicted diffusing capacity of lung for carbon monoxide (DLCO) (absolute difference, 7.4%; P = .034), a 0.019 higher right ventricular-to-left ventricular (RVLV) volume ratio (P = .029), a 43.2-m shorter 6-minute walk distance (6MWD) (P < .001), a 5.9-point higher St George's Respiratory Questionnaire (SGRQ) score (P < .001), and 82% higher mortality (P < .001). In addition, interstitial features modified the effect of emphysema on percentage predicted DLCO, RVLV volume ratio, 6WMD, SGRQ score, and mortality (P for interaction < .05 for all). Conclusion In smokers, the combined presence of interstitial features and emphysema was associated with worse clinical disease severity and higher mortality than was emphysema alone. In addition, interstitial features enhanced the deleterious effects of emphysema on clinical disease severity and mortality. © RSNA, 2018 Online supplemental material is available for this article.

Reversible Nerve Conduction Block Using Kilohertz Frequency Alternating Current

PubMed Central

Kilgore, Kevin L.; Bhadra, Niloy

2013-01-01

Objectives The features and clinical applications of balanced-charge kilohertz frequency alternating currents (KHFAC) are reviewed. Preclinical studies of KHFAC block have demonstrated that it can produce an extremely rapid and reversible block of nerve conduction. Recent systematic analysis and experimentation utilizing KHFAC block has resulted in a significant increase in interest in KHFAC block, both scientifically and clinically. Materials and Methods We review the history and characteristics of KHFAC block, the methods used to investigate this type of block, the experimental evaluation of block, and the electrical parameters and electrode designs needed to achieve successful block. We then analyze the existing clinical applications of high frequency currents, comparing the early results with the known features of KHFAC block. Results Although many features of KHFAC block have been characterized, there is still much that is unknown regarding the response of neural structures to rapidly fluctuating electrical fields. The clinical reports to date do not provide sufficient information to properly evaluate the mechanisms that result in successful or unsuccessful treatment. Conclusions KHFAC nerve block has significant potential as a means of controlling nerve activity for the purpose of treating disease. However, early clinical studies in the use of high frequency currents for the treatment of pain have not been designed to elucidate mechanisms or allow direct comparisons to preclinical data. We strongly encourage the careful reporting of the parameters utilized in these clinical studies, as well as the development of outcome measures that could illuminate the mechanisms of this modality. PMID:23924075

Deep Learning: A Primer for Radiologists.

PubMed

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Towards a taxonomy for integrated care: a mixed-methods study.

PubMed

Valentijn, Pim P; Boesveld, Inge C; van der Klauw, Denise M; Ruwaard, Dirk; Struijs, Jeroen N; Molema, Johanna J W; Bruijnzeels, Marc A; Vrijhoef, Hubertus Jm

2015-01-01

Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective.

Features of Effective Medical Knowledge Resources to Support Point of Care Learning: A Focus Group Study

PubMed Central

Cook, David A.; Sorensen, Kristi J.; Hersh, William; Berger, Richard A.; Wilkinson, John M.

2013-01-01

Objective Health care professionals access various information sources to quickly answer questions that arise in clinical practice. The features that favorably influence the selection and use of knowledge resources remain unclear. We sought to better understand how clinicians select among the various knowledge resources available to them, and from this to derive a model for an effective knowledge resource. Methods We conducted 11 focus groups at an academic medical center and outlying community sites. We included a purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians. We transcribed focus group discussions and analyzed these using a constant comparative approach to inductively identify features that influence the selection of knowledge resources. Results We identified nine features that influence users' selection of knowledge resources, namely efficiency (with sub-features of comprehensiveness, searchability, and brevity), integration with clinical workflow, credibility, user familiarity, capacity to identify a human expert, reflection of local care processes, optimization for the clinical question (e.g., diagnosis, treatment options, drug side effect), currency, and ability to support patient education. No single existing resource exemplifies all of these features. Conclusion The influential features identified in this study will inform the development of knowledge resources, and could serve as a framework for future research in this field. PMID:24282535

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting.

PubMed

Tong, Tong; Ledig, Christian; Guerrero, Ricardo; Schuh, Andreas; Koikkalainen, Juha; Tolonen, Antti; Rhodius, Hanneke; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W; Soininen, Hilkka; Remes, Anne M; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; Baroni, Marta; Lötjönen, Jyrki; Flier, Wiesje van der; Rueckert, Daniel

2017-01-01

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

Examining Curricular Integration Strategies To Optimize Learning Of The Anatomical Sciences

NASA Astrophysics Data System (ADS)

Lisk, Kristina Adriana Ayako

Background: Integration of basic and clinical science knowledge is essential to clinical practice. Although the importance of these two knowledge domains is well-recognized, successfully supporting the development of learners' integrated basic and clinical science knowledge, remains an educational challenge. In this dissertation, I examine curricular integration strategies to optimize learning of the anatomical sciences. Objectives: The studies were designed to achieve the following research aims: 1) to objectively identify clinically relevant content for an integrated musculoskeletal anatomy curriculum; 2) to examine the value of integrated anatomy and clinical science instruction compared to clinical science instruction alone on novices' diagnostic accuracy and diagnostic reasoning process; 3) to compare the effect of integrating and segregating anatomy and clinical science instruction along with a learning strategy (self-explanation) on novices' diagnostic accuracy. Methods: A modified Delphi was used to objectively select clinically relevant content for an integrated musculoskeletal anatomy curriculum. Two experimental studies were created to compare different instructional strategies to optimize learning of the curricular content. In both of these studies, novice learners were taught the clinical features of musculoskeletal pathologies using different learning approaches. Diagnostic performance was measured immediately after instruction and one-week later. Results: The results show that the Delphi method is an effective strategy to select clinically relevant content for integrated anatomy curricula. The findings also demonstrate that novices who were explicitly taught the clinical features of musculoskeletal diseases using causal basic science descriptions had superior diagnostic accuracy and a better understanding of the relative importance of key clinical features for disease categories. Conclusions: This research demonstrates how integration strategies can be applied at multiple levels of the curriculum. Further, this work shows the value of cognitive integration of anatomy and clinical science and it emphasizes the importance of purposefully linking the anatomical and clinical sciences in day-to-day teaching.

A three-dimensional comparison of a morphometric and conventional cephalometric midsagittal planes for craniofacial asymmetry.

PubMed

Damstra, Janalt; Fourie, Zacharias; De Wit, Marnix; Ren, Yijin

2012-02-01

Morphometric methods are used in biology to study object symmetry in living organisms and to determine the true plane of symmetry. The aim of this study was to determine if there are clinical differences between three-dimensional (3D) cephalometric midsagittal planes used to describe craniofacial asymmetry and a true symmetry plane derived from a morphometric method based on visible facial features. The sample consisted of 14 dry skulls (9 symmetric and 5 asymmetric) with metallic markers which were imaged with cone-beam computed tomography. An error study and statistical analysis were performed to validate the morphometric method. The morphometric and conventional cephalometric planes were constructed and compared. The 3D cephalometric planes constructed as perpendiculars to the Frankfort horizontal plane resembled the morphometric plane the most in both the symmetric and asymmetric groups with mean differences of less than 1.00 mm for most variables. However, the standard deviations were often large and clinically significant for these variables. There were clinically relevant differences (>1.00 mm) between the different 3D cephalometric midsagittal planes and the true plane of symmetry determined by the visible facial features. The difference between 3D cephalometric midsagittal planes and the true plane of symmetry determined by the visible facial features were clinically relevant. Care has to be taken using cephalometric midsagittal planes for diagnosis and treatment planning of craniofacial asymmetry as they might differ from the true plane of symmetry as determined by morphometrics.

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

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

Zhou, Z; Folkert, M; Iyengar, P

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PETmore » and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.« less

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

PubMed Central

Guo, Yanrong; Gao, Yaozong

2016-01-01

Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

Differential diagnosis of neurodegenerative diseases using structural MRI data

PubMed Central

Koikkalainen, Juha; Rhodius-Meester, Hanneke; Tolonen, Antti; Barkhof, Frederik; Tijms, Betty; Lemstra, Afina W.; Tong, Tong; Guerrero, Ricardo; Schuh, Andreas; Ledig, Christian; Rueckert, Daniel; Soininen, Hilkka; Remes, Anne M.; Waldemar, Gunhild; Hasselbalch, Steen; Mecocci, Patrizia; van der Flier, Wiesje; Lötjönen, Jyrki

2016-01-01

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making. PMID:27104138

Discriminative feature representation: an effective postprocessing solution to low dose CT imaging

NASA Astrophysics Data System (ADS)

Chen, Yang; Liu, Jin; Hu, Yining; Yang, Jian; Shi, Luyao; Shu, Huazhong; Gui, Zhiguo; Coatrieux, Gouenou; Luo, Limin

2017-03-01

This paper proposes a concise and effective approach termed discriminative feature representation (DFR) for low dose computerized tomography (LDCT) image processing, which is currently a challenging problem in medical imaging field. This DFR method assumes LDCT images as the superposition of desirable high dose CT (HDCT) 3D features and undesirable noise-artifact 3D features (the combined term of noise and artifact features induced by low dose scan protocols), and the decomposed HDCT features are used to provide the processed LDCT images with higher quality. The target HDCT features are solved via the DFR algorithm using a featured dictionary composed by atoms representing HDCT features and noise-artifact features. In this study, the featured dictionary is efficiently built using physical phantom images collected from the same CT scanner as the target clinical LDCT images to process. The proposed DFR method also has good robustness in parameter setting for different CT scanner types. This DFR method can be directly applied to process DICOM formatted LDCT images, and has good applicability to current CT systems. Comparative experiments with abdomen LDCT data validate the good performance of the proposed approach. This research was supported by National Natural Science Foundation under grants (81370040, 81530060), the Fundamental Research Funds for the Central Universities, and the Qing Lan Project in Jiangsu Province.

Clinical applications of angiocardiography

NASA Technical Reports Server (NTRS)

Dodge, H. T.; Sandler, H.

1974-01-01

Several tables are presented giving left ventricular (LV) data for normal patients and patients with heart disease of varied etiologies, pointing out the salient features. Graphs showing LV pressure-volume relationships (compliance) are presented and discussed. The method developed by Rackley et al. (1964) for determining left ventricular mass in man is described, and limitations to the method are discussed. Some clinical methods for determining LV oxygen consumption are briefly described, and the relation of various abnormalities of ventricular performance to coronary artery disease and ischemic heart disease is characterized.

TU-D-207B-02: Delta-Radiomics: The Prognostic Value of Therapy-Induced Changes in Radiomics Features for Stage III Non-Small Cell Lung Cancer Patients

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

Fave, X; Court, L; UT Health Science Center, Graduate School of Biomedical Sciences, Houston, TX

Purpose: To determine how radiomics features change during radiation therapy and whether those changes (delta-radiomics features) can improve prognostic models built with clinical factors. Methods: 62 radiomics features, including histogram, co-occurrence, run-length, gray-tone difference, and shape features, were calculated from pretreatment and weekly intra-treatment CTs for 107 stage III NSCLC patients (5–9 images per patient). Image preprocessing for each feature was determined using the set of pretreatment images: bit-depth resample and/or a smoothing filter were tested for their impact on volume-correlation and significance of each feature in univariate cox regression models to maximize their information content. Next, the optimized featuresmore » were calculated from the intratreatment images and tested in linear mixed-effects models to determine which features changed significantly with dose-fraction. The slopes in these significant features were defined as delta-radiomics features. To test their prognostic potential multivariate cox regression models were fitted, first using only clinical features and then clinical+delta-radiomics features for overall-survival, local-recurrence, and distant-metastases. Leave-one-out cross validation was used for model-fitting and patient predictions. Concordance indices(c-index) and p-values for the log-rank test with patients stratified at the median were calculated. Results: Approximately one-half of the 62 optimized features required no preprocessing, one-fourth required smoothing, and one-fourth required smoothing and resampling. From these, 54 changed significantly during treatment. For overall-survival, the c-index improved from 0.52 for clinical factors alone to 0.62 for clinical+delta-radiomics features. For distant-metastases, the c-index improved from 0.53 to 0.58, while for local-recurrence it did not improve. Patient stratification significantly improved (p-value<0.05) for overallsurvival and distant-metastases when delta-radiomics features were included. The delta-radiomics versions of autocorrelation, kurtosis, and compactness were selected most frequently in leave-one-out iterations. Conclusion: Weekly changes in radiomics features can potentially be used to evaluate treatment response and predict patient outcomes. High-risk patients could be recommended for dose escalation or consolidation chemotherapy. This project was funded in part by grants from the National Cancer Institute (NCI) and the Cancer Prevention Research Institute of Texas (CPRIT).« less

Enzyme-potentiated desensitization in otolaryngic allergy.

PubMed

Pulec, Jack L

2002-03-01

This is a preliminary report of a new method of treating otolaryngic allergy with enzyme-potentiated desensitization (EPD). The nature of EPD and its use in otolaryngology are described. Thirty-six patients have been treated and followed in a private medical practice since February 1997. This article reviews the clinical features of EPD and provides six cases as examples; the clinical features described include allergic rhinitis, serous otitis media, asthma, dermatitis, fixed food allergy, and Ménière's disease. EPD is an effective technique for the treatment of otolaryngic allergy and offers advantages over conventional techniques.

Finger extension weakness and downbeat nystagmus motor neuron disease syndrome: A novel motor neuron disorder?

PubMed Central

Delva, Aline; Thakore, Nimish; Pioro, Erik P.; Poesen, Koen; Saunders‐Pullman, Rachel; Meijer, Inge A.; Rucker, Janet C.; Kissel, John T.

2017-01-01

ABSTACT Introduction: Disturbances of eye movements are infrequently encountered in motor neuron diseases (MNDs) or motor neuropathies, and there is no known syndrome that combines progressive muscle weakness with downbeat nystagmus. Methods: To describe the core clinical features of a syndrome of MND associated with downbeat nystagmus, clinical features were collected from 6 patients. Results: All patients had slowly progressive muscle weakness and wasting in combination with downbeat nystagmus, which was clinically most obvious in downward and lateral gaze. Onset was in the second to fourth decade with finger extension weakness, progressing to other distal and sometimes more proximal muscles. Visual complaints were not always present. Electrodiagnostic testing showed signs of regional motor axonal loss in all patients. Discussion: The etiology of this syndrome remains elusive. Because finger extension weakness and downbeat nystagmus are the discriminating clinical features of this MND, we propose the name FEWDON‐MND syndrome. Muscle Nerve 56: 1164–1168, 2017 PMID:28440863

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

PubMed

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

PubMed

Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua

2015-01-01

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis

PubMed Central

2013-01-01

Background Colorectal cancer is the third leading cause of cancer deaths in the United States. The initial assessment of colorectal cancer involves clinical staging that takes into account the extent of primary tumor invasion, determining the number of lymph nodes with metastatic cancer and the identification of metastatic sites in other organs. Advanced clinical stage indicates metastatic cancer, either in regional lymph nodes or in distant organs. While the genomic and genetic basis of colorectal cancer has been elucidated to some degree, less is known about the identity of specific cancer genes that are associated with advanced clinical stage and metastasis. Methods We compiled multiple genomic data types (mutations, copy number alterations, gene expression and methylation status) as well as clinical meta-data from The Cancer Genome Atlas (TCGA). We used an elastic-net regularized regression method on the combined genomic data to identify genetic aberrations and their associated cancer genes that are indicators of clinical stage. We ranked candidate genes by their regression coefficient and level of support from multiple assay modalities. Results A fit of the elastic-net regularized regression to 197 samples and integrated analysis of four genomic platforms identified the set of top gene predictors of advanced clinical stage, including: WRN, SYK, DDX5 and ADRA2C. These genetic features were identified robustly in bootstrap resampling analysis. Conclusions We conducted an analysis integrating multiple genomic features including mutations, copy number alterations, gene expression and methylation. This integrated approach in which one considers all of these genomic features performs better than any individual genomic assay. We identified multiple genes that robustly delineate advanced clinical stage, suggesting their possible role in colorectal cancer metastatic progression. PMID:24308539

A machine learning heuristic to identify biologically relevant and minimal biomarker panels from omics data

PubMed Central

2015-01-01

Background Investigations into novel biomarkers using omics techniques generate large amounts of data. Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods. A key component of typical machine learning pipelines for omics data is feature selection, which is used to reduce the raw high-dimensional data into a tractable number of features. Feature selection needs to balance the objective of using as few features as possible, while maintaining high predictive power. This balance is crucial when the goal of data analysis is the identification of highly accurate but small panels of biomarkers with potential clinical utility. In this paper we propose a heuristic for the selection of very small feature subsets, via an iterative feature elimination process that is guided by rule-based machine learning, called RGIFE (Rule-guided Iterative Feature Elimination). We use this heuristic to identify putative biomarkers of osteoarthritis (OA), articular cartilage degradation and synovial inflammation, using both proteomic and transcriptomic datasets. Results and discussion Our RGIFE heuristic increased the classification accuracies achieved for all datasets when no feature selection is used, and performed well in a comparison with other feature selection methods. Using this method the datasets were reduced to a smaller number of genes or proteins, including those known to be relevant to OA, cartilage degradation and joint inflammation. The results have shown the RGIFE feature reduction method to be suitable for analysing both proteomic and transcriptomics data. Methods that generate large ‘omics’ datasets are increasingly being used in the area of rheumatology. Conclusions Feature reduction methods are advantageous for the analysis of omics data in the field of rheumatology, as the applications of such techniques are likely to result in improvements in diagnosis, treatment and drug discovery. PMID:25923811

Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance

NASA Astrophysics Data System (ADS)

Gao, Bin; Liu, Wanyu; Wang, Liang; Liu, Zhengjun; Croisille, Pierre; Delachartre, Philippe; Clarysse, Patrick

2016-12-01

Cine-MRI is widely used for the analysis of cardiac function in clinical routine, because of its high soft tissue contrast and relatively short acquisition time in comparison with other cardiac MRI techniques. The gray level distribution in cardiac cine-MRI is relatively homogenous within the myocardium, and can therefore make motion quantification difficult. To ensure that the motion estimation problem is well posed, more image features have to be considered. This work is inspired by a method previously developed for color image processing. The monogenic signal provides a framework to estimate the local phase, orientation, and amplitude, of an image, three features which locally characterize the 2D intensity profile. The independent monogenic features are combined into a 3D matrix for motion estimation. To improve motion estimation accuracy, we chose the zero-mean normalized cross-correlation as a matching measure, and implemented a bilateral filter for denoising and edge-preservation. The monogenic features distance is used in lieu of the color space distance in the bilateral filter. Results obtained from four realistic simulated sequences outperformed two other state of the art methods even in the presence of noise. The motion estimation errors (end point error) using our proposed method were reduced by about 20% in comparison with those obtained by the other tested methods. The new methodology was evaluated on four clinical sequences from patients presenting with cardiac motion dysfunctions and one healthy volunteer. The derived strain fields were analyzed favorably in their ability to identify myocardial regions with impaired motion.

Clinical features of olfactory disorders in patients seeking medical consultation

PubMed Central

Chen, Guowei; Wei, Yongxiang; Miao, Xutao; Li, Kunyan; Ren, Yuanyuan; Liu, Jia

2013-01-01

Background Olfactory disorders are common complaints in ENT clinics. We investigated causes and relevant features of olfactory disorders and the need for gustatory testing in patients with olfactory dysfunction. Material/Methods A total of 140 patients seeking medical consultations were enrolled. All patients were asked about their olfactory disorders in a structured interview of medical history and underwent thorough otolaryngologic examinations and imaging of the head. Results Causes of olfactory disorders were classified as: upper respiratory tract infection (URTI), sinonasal diseases (NSD), head trauma, idiopathic, endoscopic sinus surgery, congenital anosmia, and other causes. Each of the various causes of olfactory dysfunction had its own distinct clinical features. Nineteen of 54 patients whose gustation was assessed had gustatory disorders. Conclusions The leading causes of olfactory dysfunction were URTI, NSD, head trauma, and idiopathic causes. Gustatory disorders were fairly common in patients with olfactory dysfunction. High priority should be given to complaints of olfactory disorders. PMID:23748259

Feature-Based Correlation and Topological Similarity for Interbeat Interval Estimation Using Ultrawideband Radar.

PubMed

Sakamoto, Takuya; Imasaka, Ryohei; Taki, Hirofumi; Sato, Toru; Yoshioka, Mototaka; Inoue, Kenichi; Fukuda, Takeshi; Sakai, Hiroyuki

2016-04-01

The objectives of this paper are to propose a method that can accurately estimate the human heart rate (HR) using an ultrawideband (UWB) radar system, and to determine the performance of the proposed method through measurements. The proposed method uses the feature points of a radar signal to estimate the HR efficiently and accurately. Fourier- and periodicity-based methods are inappropriate for estimation of instantaneous HRs in real time because heartbeat waveforms are highly variable, even within the beat-to-beat interval. We define six radar waveform features that enable correlation processing to be performed quickly and accurately. In addition, we propose a feature topology signal that is generated from a feature sequence without using amplitude information. This feature topology signal is used to find unreliable feature points, and thus, to suppress inaccurate HR estimates. Measurements were taken using UWB radar, while simultaneously performing electrocardiography measurements in an experiment that was conducted on nine participants. The proposed method achieved an average root-mean-square error in the interbeat interval of 7.17 ms for the nine participants. The results demonstrate the effectiveness and accuracy of the proposed method. The significance of this study for biomedical research is that the proposed method will be useful in the realization of a remote vital signs monitoring system that enables accurate estimation of HR variability, which has been used in various clinical settings for the treatment of conditions such as diabetes and arterial hypertension.

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Advanced image fusion algorithms for Gamma Knife treatment planning. Evaluation and proposal for clinical use.

PubMed

Apostolou, N; Papazoglou, Th; Koutsouris, D

2006-01-01

Image fusion is a process of combining information from multiple sensors. It is a useful tool implemented in the treatment planning programme of Gamma Knife Radiosurgery. In this paper we evaluate advanced image fusion algorithms for Matlab platform and head images. We develop nine level grayscale image fusion methods: average, principal component analysis (PCA), discrete wavelet transform (DWT) and Laplacian, filter - subtract - decimate (FSD), contrast, gradient, morphological pyramid and a shift invariant discrete wavelet transform (SIDWT) method in Matlab platform. We test these methods qualitatively and quantitatively. The quantitative criteria we use are the Root Mean Square Error (RMSE), the Mutual Information (MI), the Standard Deviation (STD), the Entropy (H), the Difference Entropy (DH) and the Cross Entropy (CEN). The qualitative are: natural appearance, brilliance contrast, presence of complementary features and enhancement of common features. Finally we make clinically useful suggestions.

Quantitative Imaging Biomarkers: A Review of Statistical Methods for Technical Performance Assessment

PubMed Central

2017-01-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers (QIBs) to measure changes in these features. Critical to the performance of a QIB in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method and metrics used to assess a QIB for clinical use. It is therefore, difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America (RSNA) and the Quantitative Imaging Biomarker Alliance (QIBA) with technical, radiological and statistical experts developed a set of technical performance analysis methods, metrics and study designs that provide terminology, metrics and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of QIB performance studies so that results from multiple studies can be compared, contrasted or combined. PMID:24919831

Is Early-onset in Major Depression a Predictor of Specific Clinical Features with More Impaired Social Function?

PubMed Central

Liu, Yan-Hong; Chen, Lin; Su, Yun-Ai; Fang, Yi-Ru; Srisurapanont, Manit; Hong, Jin Pyo; Hatim, Ahmad; Chua, Hong Choon; Bautista, Dianne; Si, Tian-Mei

2015-01-01

Background: Early-onset major depressive disorder (MDD) (EOD) is often particularly malignant due to its special clinical features, accompanying impaired social function, protracted recovery time, and frequent recurrence. This study aimed to observe the effects of age onset on clinical characteristics and social function in MDD patients in Asia. Methods: In total, 547 out-patients aged 18–65 years who were from 13 study sites in five Asian countries were included. These patients had MDD diagnose according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria. Clinical features and social function were assessed using Symptom Checklist-90-revised (SCL-90-R) and Sheehan Disability Scale (SDS). Quality of life was assessed by a 36-item Short-form Health Survey (SF-36). Analyses were performed using a continuous or dichotomous (cut-off: 30 years) age-of-onset indicator. Results: Early-onset MDD (EOD, <30 years) was associated with longer illness (P = 0.003), unmarried status (P < 0.001), higher neuroticism (P ≤ 0.002) based on the SCL-90-R, and more limited social function and mental health (P = 0.006, P = 0.007) based on the SF-36 and SDS. The impairment of social function and clinical severity were more prominent at in-patients with younger onset ages. Special clinical features and more impaired social function and quality of life were associated with EOD, as in western studies. Conclusions: EOD often follows higher levels of neuroticism. Age of onset of MDD may be a predictor of clinical features and impaired social function, allowing earlier diagnosis and treatment. PMID:25758278

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

PubMed Central

Nyflot, Matthew J.; Yang, Fei; Byrd, Darrin; Bowen, Stephen R.; Sandison, George A.; Kinahan, Paul E.

2015-01-01

Abstract. Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes. PMID:26251842

Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards.

PubMed

Nyflot, Matthew J; Yang, Fei; Byrd, Darrin; Bowen, Stephen R; Sandison, George A; Kinahan, Paul E

2015-10-01

Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.

Value of multiplex PCR to determine the bacterial and viral aetiology of pneumonia in school-age children.

PubMed

Aydemir, Yusuf; Aydemir, Özlem; Pekcan, Sevgi; Özdemir, Mehmet

2017-02-01

Conventional methods for the aetiological diagnosis of community-acquired pneumonia (CAP) are often insufficient owing to low sensitivity and the long wait for the results of culture and particularly serology, and it often these methods establish a diagnosis in only half of cases. To evaluate the most common bacterial and viral agents in CAP using a fast responsive PCR method and investigate the relationship between clinical/laboratory features and aetiology, thereby contributing to empirical antibiotic selection and reduction of treatment failure. In children aged 4-15 years consecutively admitted with a diagnosis of CAP, the 10 most commonly detected bacterial and 12 most commonly detected viral agents were investigated by induced sputum using bacterial culture and multiplex PCR methods. Clinical and laboratory features were compared between bacterial and viral pneumonia. In 78 patients, at least one virus was detected in 38 (48.7%) and at least one bacterium in 32 (41%). In addition, both bacteria and viruses were detected in 16 (20.5%) patients. Overall, the agent detection rate was 69.2%. The most common viruses were respiratory syncytial virus and influenza and the most frequently detected bacteria were S. pneumoniae and H. influenzae. PCR was superior to culture for bacterial isolation (41% vs 13%, respectively). Fever, wheezing and radiological features were not helpful in differentiating between bacterial and viral CAP. White blood cell count, CRP and ESR values were significantly higher in the bacterial/mixed aetiology group than in the viral aetiology group. In CAP, multiplex PCR is highly reliable, superior in detecting multiple pathogens and rapidly identifies aetiological agents. Clinical features are poor for differentiation between bacterial and viral infections. The use of PCR methods allow physicians to provide more appropriate antimicrobial therapy, resulting in a better response to treatment, and it may be possible for use as a routine service if costs can be reduced.

WE-D-BRD-01: Innovation in Radiation Therapy Delivery: Advanced Digital Linac Features

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

Xing, L; Wong, J; Li, R

2014-06-15

Last few years has witnessed significant advances in linac technology and therapeutic dose delivery method. Digital linacs equipped with high dose rate FFF beams have been clinically implemented in a number of hospitals. Gated VMAT is becoming increasingly popular in treating tumors affected by respiratory motion. This session is devoted to update the audience with these technical advances and to present our experience in clinically implementing the new linacs and dose delivery methods. Topics to be covered include, technical features of new generation of linacs from different vendors, dosimetric characteristics and clinical need for FFF-beam based IMRT and VMAT, respiration-gatedmore » VMAT, the concept and implementation of station parameter optimized radiation therapy (SPORT), beam level imaging and onboard image guidance tools. Emphasis will be on providing fundamental understanding of the new treatment delivery and image guidance strategies, control systems, and the associated dosimetric characteristics. Commissioning and acceptance experience on these new treatment delivery technologies will be reported. Clinical experience and challenges encountered during the process of implementation of the new treatment techniques and future applications of the systems will also be highlighted. Learning Objectives: Present background knowledge of emerging digital linacs and summarize their key geometric and dosimetric features. SPORT as an emerging radiation therapy modality specifically designed to take advantage of digital linacs. Discuss issues related to the acceptance and commissioning of the digital linacs and FFF beams. Describe clinical utility of the new generation of digital linacs and their future applications.« less

Feasibility of streamlining an interactive Bayesian-based diagnostic support tool designed for clinical practice

NASA Astrophysics Data System (ADS)

Chen, Po-Hao; Botzolakis, Emmanuel; Mohan, Suyash; Bryan, R. N.; Cook, Tessa

2016-03-01

In radiology, diagnostic errors occur either through the failure of detection or incorrect interpretation. Errors are estimated to occur in 30-35% of all exams and contribute to 40-54% of medical malpractice litigations. In this work, we focus on reducing incorrect interpretation of known imaging features. Existing literature categorizes cognitive bias leading a radiologist to an incorrect diagnosis despite having correctly recognized the abnormal imaging features: anchoring bias, framing effect, availability bias, and premature closure. Computational methods make a unique contribution, as they do not exhibit the same cognitive biases as a human. Bayesian networks formalize the diagnostic process. They modify pre-test diagnostic probabilities using clinical and imaging features, arriving at a post-test probability for each possible diagnosis. To translate Bayesian networks to clinical practice, we implemented an entirely web-based open-source software tool. In this tool, the radiologist first selects a network of choice (e.g. basal ganglia). Then, large, clearly labeled buttons displaying salient imaging features are displayed on the screen serving both as a checklist and for input. As the radiologist inputs the value of an extracted imaging feature, the conditional probabilities of each possible diagnosis are updated. The software presents its level of diagnostic discrimination using a Pareto distribution chart, updated with each additional imaging feature. Active collaboration with the clinical radiologist is a feasible approach to software design and leads to design decisions closely coupling the complex mathematics of conditional probability in Bayesian networks with practice.

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

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

Magome, T; Haga, A; Igaki, H

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyomore » Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.« less

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.

PubMed

Yu, Hui; Jing, Wenwen; Iriya, Rafael; Yang, Yunze; Syal, Karan; Mo, Manni; Grys, Thomas E; Haydel, Shelley E; Wang, Shaopeng; Tao, Nongjian

2018-05-15

Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from both bacteria spiked urine and clinical infected urine samples for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.

PubMed

Chebouba, Lokmane; Boughaci, Dalila; Guziolowski, Carito

2018-06-04

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.

A novel method for morphological pleomorphism and heterogeneity quantitative measurement: Named cell feature level co-occurrence matrix.

PubMed

Saito, Akira; Numata, Yasushi; Hamada, Takuya; Horisawa, Tomoyoshi; Cosatto, Eric; Graf, Hans-Peter; Kuroda, Masahiko; Yamamoto, Yoichiro

2016-01-01

Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

Implementing psychophysiology in clinical assessments of adolescent social anxiety: use of rater judgments based on graphical representations of psychophysiology.

PubMed

De Los Reyes, Andres; Augenstein, Tara M; Aldao, Amelia; Thomas, Sarah A; Daruwala, Samantha; Kline, Kathryn; Regan, Timothy

2015-01-01

Social stressor tasks induce adolescents' social distress as indexed by low-cost psychophysiological methods. Unknown is how to incorporate these methods within clinical assessments. Having assessors judge graphical depictions of psychophysiological data may facilitate detections of data patterns that may be difficult to identify using judgments about numerical depictions of psychophysiological data. Specifically, the Chernoff Face method involves graphically representing data using features on the human face (eyes, nose, mouth, and face shape). This method capitalizes on humans' abilities to discern subtle variations in facial features. Using adolescent heart rate norms and Chernoff Faces, we illustrated a method for implementing psychophysiology within clinical assessments of adolescent social anxiety. Twenty-two clinic-referred adolescents completed a social anxiety self-report and provided psychophysiological data using wireless heart rate monitors during a social stressor task. We graphically represented participants' psychophysiological data and normative adolescent heart rates. For each participant, two undergraduate coders made comparative judgments between the dimensions (eyes, nose, mouth, and face shape) of two Chernoff Faces. One Chernoff Face represented a participant's heart rate within a context (baseline, speech preparation, or speech-giving). The second Chernoff Face represented normative heart rate data matched to the participant's age. Using Chernoff Faces, coders reliably and accurately identified contextual variation in participants' heart rate responses to social stress. Further, adolescents' self-reported social anxiety symptoms predicted Chernoff Face judgments, and judgments could be differentiated by social stress context. Our findings have important implications for implementing psychophysiology within clinical assessments of adolescent social anxiety.

Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.

PubMed

Pan, Xiaoyong; Hu, Xiaohua; Zhang, Yu Hang; Feng, Kaiyan; Wang, Shao Peng; Chen, Lei; Huang, Tao; Cai, Yu Dong

2018-04-12

Atrioventricular septal defect (AVSD) is a clinically significant subtype of congenital heart disease (CHD) that severely influences the health of babies during birth and is associated with Down syndrome (DS). Thus, exploring the differences in functional genes in DS samples with and without AVSD is a critical way to investigate the complex association between AVSD and DS. In this study, we present a computational method to distinguish DS patients with AVSD from those without AVSD using the newly proposed self-normalizing neural network (SNN). First, each patient was encoded by using the copy number of probes on chromosome 21. The encoded features were ranked by the reliable Monte Carlo feature selection (MCFS) method to obtain a ranked feature list. Based on this feature list, we used a two-stage incremental feature selection to construct two series of feature subsets and applied SNNs to build classifiers to identify optimal features. Results show that 2737 optimal features were obtained, and the corresponding optimal SNN classifier constructed on optimal features yielded a Matthew's correlation coefficient (MCC) value of 0.748. For comparison, random forest was also used to build classifiers and uncover optimal features. This method received an optimal MCC value of 0.582 when top 132 features were utilized. Finally, we analyzed some key features derived from the optimal features in SNNs found in literature support to further reveal their essential roles.

Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment 18F-FDG PET/CT Imaging.

PubMed

Beukinga, Roelof J; Hulshoff, Jan B; van Dijk, Lisanne V; Muijs, Christina T; Burgerhof, Johannes G M; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Slump, Cornelis H; Mul, Véronique E M; Plukker, John Th M

2017-05-01

Adequate prediction of tumor response to neoadjuvant chemoradiotherapy (nCRT) in esophageal cancer (EC) patients is important in a more personalized treatment. The current best clinical method to predict pathologic complete response is SUV max in 18 F-FDG PET/CT imaging. To improve the prediction of response, we constructed a model to predict complete response to nCRT in EC based on pretreatment clinical parameters and 18 F-FDG PET/CT-derived textural features. Methods: From a prospectively maintained single-institution database, we reviewed 97 consecutive patients with locally advanced EC and a pretreatment 18 F-FDG PET/CT scan between 2009 and 2015. All patients were treated with nCRT (carboplatin/paclitaxel/41.4 Gy) followed by esophagectomy. We analyzed clinical, geometric, and pretreatment textural features extracted from both 18 F-FDG PET and CT. The current most accurate prediction model with SUV max as a predictor variable was compared with 6 different response prediction models constructed using least absolute shrinkage and selection operator regularized logistic regression. Internal validation was performed to estimate the model's performances. Pathologic response was defined as complete versus incomplete response (Mandard tumor regression grade system 1 vs. 2-5). Results: Pathologic examination revealed 19 (19.6%) complete and 78 (80.4%) incomplete responders. Least absolute shrinkage and selection operator regularization selected the clinical parameters: histologic type and clinical T stage, the 18 F-FDG PET-derived textural feature long run low gray level emphasis, and the CT-derived textural feature run percentage. Introducing these variables to a logistic regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 compared with 0.58 in the SUV max model. The discrimination slopes were 0.17 compared with 0.01, respectively. After internal validation, the AUCs decreased to 0.74 and 0.54, respectively. Conclusion: The predictive values of the constructed models were superior to the standard method (SUV max ). These results can be considered as an initial step in predicting tumor response to nCRT in locally advanced EC. Further research in refining the predictive value of these models is needed to justify omission of surgery. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures.

PubMed

Colen, Rivka; Foster, Ian; Gatenby, Robert; Giger, Mary Ellen; Gillies, Robert; Gutman, David; Heller, Matthew; Jain, Rajan; Madabhushi, Anant; Madhavan, Subha; Napel, Sandy; Rao, Arvind; Saltz, Joel; Tatum, James; Verhaak, Roeland; Whitman, Gary

2014-10-01

The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Medical image retrieval system using multiple features from 3D ROIs

NASA Astrophysics Data System (ADS)

Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

2012-02-01

Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.

PubMed

Li, Yang; Cui, Weigang; Luo, Meilin; Li, Ke; Wang, Lina

2018-01-25

The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Effects of additional team-based learning on students' clinical reasoning skills: a pilot study.

PubMed

Jost, Meike; Brüstle, Peter; Giesler, Marianne; Rijntjes, Michel; Brich, Jochen

2017-07-14

In the field of Neurology good clinical reasoning skills are essential for successful diagnosing and treatment. Team-based learning (TBL), an active learning and small group instructional strategy, is a promising method for fostering these skills. The aim of this pilot study was to examine the effects of a supplementary TBL-class on students' clinical decision-making skills. Fourth- and fifth-year medical students participated in this pilot study (static-group comparison design). The non-treatment group (n = 15) did not receive any additional training beyond regular teaching in the neurology course. The treatment group (n = 11) took part in a supplementary TBL-class optimized for teaching clinical reasoning in addition to the regular teaching in the neurology course. Clinical decision making skills were assessed using a key-feature problem examination. Factual and conceptual knowledge was assessed by a multiple-choice question examination. The TBL-group performed significantly better than the non-TBL-group (p = 0.026) in the key-feature problem examination. No significant differences between the results of the multiple-choice question examination of both groups were found. In this pilot study participants of a supplementary TBL-class significantly improved clinical decision-making skills, indicating that TBL may be an appropriate method for teaching clinical decision making in neurology. Further research is needed for replication in larger groups and other clinical fields.

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images.

PubMed

Sihong Chen; Jing Qin; Xing Ji; Baiying Lei; Tianfu Wang; Dong Ni; Jie-Zhi Cheng

2017-03-01

The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images. We regard that there may exist relations among the semantic features of "spiculation", "texture", "margin", etc., that can be explored with the MTL. The Lung Image Database Consortium (LIDC) data is adopted in this study for the rich annotation resources. The LIDC nodules were quantitatively scored w.r.t. 9 semantic features from 12 radiologists of several institutes in U.S.A. By treating each semantic feature as an individual task, the MTL schemes select and map the heterogeneous computational features toward the radiologists' ratings with cross validation evaluation schemes on the randomly selected 2400 nodules from the LIDC dataset. The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists' ratings than the scores from single-task LASSO and elastic net regression methods. The proposed semantic attribute scoring scheme may provide richer quantitative assessments of nodules for better support of diagnostic decision and management. Meanwhile, the capability of the automatic association of medical image contents with the clinical semantic terms by our method may also assist the development of medical search engine.

Features of asthma which provide meaningful insights for understanding the disease heterogeneity.

PubMed

Deliu, M; Yavuz, T S; Sperrin, M; Belgrave, D; Sahiner, U M; Sackesen, C; Kalayci, O; Custovic, A

2018-01-01

Data-driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results. To develop a framework for the discovery of stable and clinically meaningful asthma subtypes. We performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process. Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age of onset, allergic sensitization, severity, and recent exacerbations. In Model 3 (HC using these four features), cluster stability improved substantially. The cluster assignment changed, providing more clinically interpretable results. In a 5-cluster model, we labelled the clusters as: "Difficult asthma" (n = 132); "Early-onset mild atopic" (n = 210); "Early-onset mild non-atopic: (n = 153); "Late-onset" (n = 105); and "Exacerbation-prone asthma" (n = 13). Multinomial regression demonstrated that lung function was significantly diminished among children with "Difficult asthma"; blood eosinophilia was a significant feature of "Difficult," "Early-onset mild atopic," and "Late-onset asthma." Children with moderate-to-severe asthma were present in each cluster. An integrative approach of blending the data with clinical expert domain knowledge identified four features, which may be informative for ascertaining asthma endotypes. These findings suggest that variables which are key determinants of asthma presence, severity, or control may not be the most informative for determining asthma subtypes. Our results indicate that exacerbation-prone asthma may be a separate asthma endotype and that severe asthma is not a single entity, but an extreme end of the spectrum of several different asthma endotypes. © 2017 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling.

PubMed

Leger, Stefan; Zwanenburg, Alex; Pilz, Karoline; Lohaus, Fabian; Linge, Annett; Zöphel, Klaus; Kotzerke, Jörg; Schreiber, Andreas; Tinhofer, Inge; Budach, Volker; Sak, Ali; Stuschke, Martin; Balermpas, Panagiotis; Rödel, Claus; Ganswindt, Ute; Belka, Claus; Pigorsch, Steffi; Combs, Stephanie E; Mönnich, David; Zips, Daniel; Krause, Mechthild; Baumann, Michael; Troost, Esther G C; Löck, Steffen; Richter, Christian

2017-10-16

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g. C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

[Technologies for Complex Intelligent Clinical Data Analysis].

PubMed

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B; Sia, C W

2016-10-01

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies

PubMed Central

Köhler, Sebastian; Schulz, Marcel H.; Krawitz, Peter; Bauer, Sebastian; Dölken, Sandra; Ott, Claus E.; Mundlos, Christine; Horn, Denise; Mundlos, Stefan; Robinson, Peter N.

2009-01-01

The differential diagnostic process attempts to identify candidate diseases that best explain a set of clinical features. This process can be complicated by the fact that the features can have varying degrees of specificity, as well as by the presence of features unrelated to the disease itself. Depending on the experience of the physician and the availability of laboratory tests, clinical abnormalities may be described in greater or lesser detail. We have adapted semantic similarity metrics to measure phenotypic similarity between queries and hereditary diseases annotated with the use of the Human Phenotype Ontology (HPO) and have developed a statistical model to assign p values to the resulting similarity scores, which can be used to rank the candidate diseases. We show that our approach outperforms simpler term-matching approaches that do not take the semantic interrelationships between terms into account. The advantage of our approach was greater for queries containing phenotypic noise or imprecise clinical descriptions. The semantic network defined by the HPO can be used to refine the differential diagnosis by suggesting clinical features that, if present, best differentiate among the candidate diagnoses. Thus, semantic similarity searches in ontologies represent a useful way of harnessing the semantic structure of human phenotypic abnormalities to help with the differential diagnosis. We have implemented our methods in a freely available web application for the field of human Mendelian disorders. PMID:19800049

Effect of finite sample size on feature selection and classification: a simulation study.

PubMed

Way, Ted W; Sahiner, Berkman; Hadjiiski, Lubomir M; Chan, Heang-Ping

2010-02-01

The small number of samples available for training and testing is often the limiting factor in finding the most effective features and designing an optimal computer-aided diagnosis (CAD) system. Training on a limited set of samples introduces bias and variance in the performance of a CAD system relative to that trained with an infinite sample size. In this work, the authors conducted a simulation study to evaluate the performances of various combinations of classifiers and feature selection techniques and their dependence on the class distribution, dimensionality, and the training sample size. The understanding of these relationships will facilitate development of effective CAD systems under the constraint of limited available samples. Three feature selection techniques, the stepwise feature selection (SFS), sequential floating forward search (SFFS), and principal component analysis (PCA), and two commonly used classifiers, Fisher's linear discriminant analysis (LDA) and support vector machine (SVM), were investigated. Samples were drawn from multidimensional feature spaces of multivariate Gaussian distributions with equal or unequal covariance matrices and unequal means, and with equal covariance matrices and unequal means estimated from a clinical data set. Classifier performance was quantified by the area under the receiver operating characteristic curve Az. The mean Az values obtained by resubstitution and hold-out methods were evaluated for training sample sizes ranging from 15 to 100 per class. The number of simulated features available for selection was chosen to be 50, 100, and 200. It was found that the relative performance of the different combinations of classifier and feature selection method depends on the feature space distributions, the dimensionality, and the available training sample sizes. The LDA and SVM with radial kernel performed similarly for most of the conditions evaluated in this study, although the SVM classifier showed a slightly higher hold-out performance than LDA for some conditions and vice versa for other conditions. PCA was comparable to or better than SFS and SFFS for LDA at small samples sizes, but inferior for SVM with polynomial kernel. For the class distributions simulated from clinical data, PCA did not show advantages over the other two feature selection methods. Under this condition, the SVM with radial kernel performed better than the LDA when few training samples were available, while LDA performed better when a large number of training samples were available. None of the investigated feature selection-classifier combinations provided consistently superior performance under the studied conditions for different sample sizes and feature space distributions. In general, the SFFS method was comparable to the SFS method while PCA may have an advantage for Gaussian feature spaces with unequal covariance matrices. The performance of the SVM with radial kernel was better than, or comparable to, that of the SVM with polynomial kernel under most conditions studied.

Using experience sampling methods/ecological momentary assessment (ESM/EMA) in clinical assessment and clinical research: introduction to the special section.

PubMed

Trull, Timothy J; Ebner-Priemer, Ulrich W

2009-12-01

This article introduces the special section on experience sampling methods and ecological momentary assessment in clinical assessment. We review the conceptual basis for experience sampling methods (ESM; Csikszentmihalyi & Larson, 1987) and ecological momentary assessment (EMA; Stone & Shiffman, 1994). Next, we highlight several advantageous features of ESM/EMA as applied to psychological assessment and clinical research. We provide a brief overview of the articles in this special section, each of which focuses on 1 of the following major classes of psychological disorders: mood disorders and mood dysregulation (Ebner-Priemer & Trull, 2009), anxiety disorders (Alpers, 2009), substance use disorders (Shiffman, 2009), and psychosis (Oorschot, Kwapil, Delespaul, & Myin-Germeys, 2009). Finally, we discuss prospects, future challenges, and limitations of ESM/EMA.

Age at onset of major depressive disorder in Han Chinese women: Relationship with clinical features and family history☆

PubMed Central

Yang, Fuzhong; Li, Yihan; Xie, Dong; Shao, Chunhong; Ren, Jianer; Wu, Wenyuan; Zhang, Ning; Zhang, Zhen; Zou, Ying; Zhang, Jiulong; Qiao, Dongdong; Gao, Chengge; Li, Youhui; Hu, Jian; Deng, Hong; Wang, Gang; Du, Bo; Wang, Xumei; Liu, Tiebang; Gan, Zhaoyu; Peng, Juyi; Wei, Bo; Pan, Jiyang; Chen, Honghui; Sun, Shufan; Jia, Hong; Liu, Ying; Chen, Qiaoling; Wang, Xueyi; Cao, Juling; Lv, Luxian; Chen, Yunchun; Ha, Baowei; Ning, Yuping; Chen, YiPing; Kendler, Kenneth S.; Flint, Jonathan; Shi, Shenxun

2011-01-01

Background Individuals with early-onset depression may be a clinically distinct group with particular symptom patterns, illness course, comorbidity and family history. This question has not been previously investigated in a Han Chinese population. Methods We examined the clinical features of 1970 Han Chinese women with DSM-IV major depressive disorder (MDD) between 30 and 60 years of age across China. Analysis of linear, logistic and multiple logistic regression models was used to determine the association between age at onset (AAO) with continuous, binary and discrete characteristic clinical features of MDD. Results Earlier AAO was associated with more suicidal ideation and attempts and higher neuroticism, but fewer sleep, appetite and weight changes. Patients with an earlier AAO were more likely to suffer a chronic course (longer illness duration, more MDD episodes and longer index episode), increased rates of MDD in their parents and a lower likelihood of marriage. They tend to have higher comorbidity with anxiety disorders (general anxiety disorder, social phobia and agoraphobia) and dysthymia. Conclusions Early AAO in MDD may be an index of a more severe, highly comorbid and familial disorder. Our findings indicate that the features of MDD in China are similar to those reported elsewhere in the world. PMID:21782247

Pseudo progression identification of glioblastoma with dictionary learning.

PubMed

Zhang, Jian; Yu, Hengyong; Qian, Xiaohua; Liu, Keqin; Tan, Hua; Yang, Tielin; Wang, Maode; Li, King Chuen; Chan, Michael D; Debinski, Waldemar; Paulsson, Anna; Wang, Ge; Zhou, Xiaobo

2016-06-01

Although the use of temozolomide in chemoradiotherapy is effective, the challenging clinical problem of pseudo progression has been raised in brain tumor treatment. This study aims to distinguish pseudo progression from true progression. Between 2000 and 2012, a total of 161 patients with glioblastoma multiforme (GBM) were treated with chemoradiotherapy at our hospital. Among the patients, 79 had their diffusion tensor imaging (DTI) data acquired at the earliest diagnosed date of pseudo progression or true progression, and 23 had both DTI data and genomic data. Clinical records of all patients were kept in good condition. Volumetric fractional anisotropy (FA) images obtained from the DTI data were decomposed into a sequence of sparse representations. Then, a feature selection algorithm was applied to extract the critical features from the feature matrix to reduce the size of the feature matrix and to improve the classification accuracy. The proposed approach was validated using the 79 samples with clinical DTI data. Satisfactory results were obtained under different experimental conditions. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.87 for a given dictionary with 1024 atoms. For the subgroup of 23 samples, genomics data analysis was also performed. Results implied further perspective on pseudo progression classification. The proposed method can determine pseudo progression and true progression with improved accuracy. Laboring segmentation is no longer necessary because this skillfully designed method is not sensitive to tumor location. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variability in objective and subjective measures affects baseline values in studies of patients with COPD

PubMed Central

Ha, Jae Wook; Couper, David J.; O’Neal, Wanda K.; Barr, R. Graham; Bleecker, Eugene R.; Carretta, Elizabeth E.; Cooper, Christopher B.; Doerschuk, Claire M.; Drummond, M Bradley; Han, MeiLan K.; Hansel, Nadia N.; Kim, Victor; Kleerup, Eric C.; Martinez, Fernando J.; Rennard, Stephen I.; Tashkin, Donald; Woodruff, Prescott G.; Paine, Robert; Curtis, Jeffrey L.; Kanner, Richard E.

2017-01-01

Rationale Understanding the reliability and repeatability of clinical measurements used in the diagnosis, treatment and monitoring of disease progression is of critical importance across all disciplines of clinical practice and in clinical trials to assess therapeutic efficacy and safety. Objectives Our goal is to understand normal variability for assessing true changes in health status and to more accurately utilize this data to differentiate disease characteristics and outcomes. Methods Our study is the first study designed entirely to establish the repeatability of a large number of instruments utilized for the clinical assessment of COPD in the same subjects over the same period. We utilized SPIROMICS participants (n = 98) that returned to their clinical center within 6 weeks of their baseline visit to repeat complete baseline assessments. Demographics, spirometry, questionnaires, complete blood cell counts (CBC), medical history, and emphysema status by computerized tomography (CT) imaging were obtained. Results Pulmonary function tests (PFTs) were highly repeatable (ICC’s >0.9) but the 6 minute walk (6MW) was less so (ICC = 0.79). Among questionnaires, the Saint George’s Respiratory Questionnaire (SGRQ) was most repeatable. Self-reported clinical features, such as exacerbation history, and features of chronic bronchitis, often produced kappa values <0.6. Reported age at starting smoking and average number of cigarettes smoked were modestly repeatable (kappa = 0.76 and 0.79). Complete blood counts (CBC) variables produced intraclass correlation coefficients (ICC) values between 0.6 and 0.8. Conclusions PFTs were highly repeatable, while subjective measures and subject recall were more variable. Analyses using features with poor repeatability could lead to misclassification and outcome errors. Hence, care should be taken when interpreting change in clinical features based on measures with low repeatability. Efforts to improve repeatability of key clinical features such as exacerbation history and chronic bronchitis are warranted. PMID:28934249

A Pocock Approach to Sequential Meta-Analysis of Clinical Trials

ERIC Educational Resources Information Center

Shuster, Jonathan J.; Neu, Josef

2013-01-01

Three recent papers have provided sequential methods for meta-analysis of two-treatment randomized clinical trials. This paper provides an alternate approach that has three desirable features. First, when carried out prospectively (i.e., we only have the results up to the time of our current analysis), we do not require knowledge of the…

Self-limiting Atypical Antipsychotics-induced Edema: Clinical Cases and Systematic Review

PubMed Central

Umar, Musa Usman; Abdullahi, Aminu Taura

2016-01-01

A number of atypical antipsychotics have been associated with peripheral edema. The exact cause is not known. We report two cases of olanzapine-induced edema and a brief review of atypical antipsychotic-induced edema, possible risk factors, etiology, and clinical features. The recommendation is given on different methods of managing this side effect. PMID:27335511

Self-limiting Atypical Antipsychotics-induced Edema: Clinical Cases and Systematic Review.

PubMed

Umar, Musa Usman; Abdullahi, Aminu Taura

2016-01-01

A number of atypical antipsychotics have been associated with peripheral edema. The exact cause is not known. We report two cases of olanzapine-induced edema and a brief review of atypical antipsychotic-induced edema, possible risk factors, etiology, and clinical features. The recommendation is given on different methods of managing this side effect.

Girls with Social and/or Attention Deficits: A Descriptive Study of 100 Clinic Attenders

ERIC Educational Resources Information Center

Kopp, Svenny; Kelly, Kristina Berg; Gillberg, Christopher

2010-01-01

Objective: Examine clinical correlates and distinguishing features of autism spectrum disorders (ASD), ADHD, and tic disorders in girls referred for social impairment, attention/academic deficits, and/or tics. Method: One hundred 3- to 18-year-old girls referred for social impairment and attention symptoms were assessed in detail. Sixty of these…

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods.

PubMed

Liang, Ja-Der; Ping, Xiao-Ou; Tseng, Yi-Ju; Huang, Guan-Tarn; Lai, Feipei; Yang, Pei-Ming

2014-12-01

Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment. From January 2007 to December 2009, 83 newly diagnosed HCC patients receiving RFA as their first treatment were enrolled. Five feature selection methods including genetic algorithm (GA), simulated annealing (SA) algorithm, random forests (RF) and hybrid methods (GA+RF and SA+RF) were utilized for selecting an important subset of features from a total of 16 clinical features. These feature selection methods were combined with support vector machine (SVM) for developing predictive models with better performance. Five-fold cross-validation was used to train and test SVM models. The developed SVM-based predictive models with hybrid feature selection methods and 5-fold cross-validation had averages of the sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the ROC curve as 67%, 86%, 82%, 69%, 90%, and 0.69, respectively. The SVM derived predictive model can provide suggestive high-risk recurrent patients, who should be closely followed up after complete RFA treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

PubMed

Lo, P; Young, S; Kim, H J; Brown, M S; McNitt-Gray, M F

2016-08-01

To investigate the effects of dose level and reconstruction method on density and texture based features computed from CT lung nodules. This study had two major components. In the first component, a uniform water phantom was scanned at three dose levels and images were reconstructed using four conventional filtered backprojection (FBP) and four iterative reconstruction (IR) methods for a total of 24 different combinations of acquisition and reconstruction conditions. In the second component, raw projection (sinogram) data were obtained for 33 lung nodules from patients scanned as a part of their clinical practice, where low dose acquisitions were simulated by adding noise to sinograms acquired at clinical dose levels (a total of four dose levels) and reconstructed using one FBP kernel and two IR kernels for a total of 12 conditions. For the water phantom, spherical regions of interest (ROIs) were created at multiple locations within the water phantom on one reference image obtained at a reference condition. For the lung nodule cases, the ROI of each nodule was contoured semiautomatically (with manual editing) from images obtained at a reference condition. All ROIs were applied to their corresponding images reconstructed at different conditions. For 17 of the nodule cases, repeat contours were performed to assess repeatability. Histogram (eight features) and gray level co-occurrence matrix (GLCM) based texture features (34 features) were computed for all ROIs. For the lung nodule cases, the reference condition was selected to be 100% of clinical dose with FBP reconstruction using the B45f kernel; feature values calculated from other conditions were compared to this reference condition. A measure was introduced, which the authors refer to as Q, to assess the stability of features across different conditions, which is defined as the ratio of reproducibility (across conditions) to repeatability (across repeat contours) of each feature. The water phantom results demonstrated substantial variability among feature values calculated across conditions, with the exception of histogram mean. Features calculated from lung nodules demonstrated similar results with histogram mean as the most robust feature (Q ≤ 1), having a mean and standard deviation Q of 0.37 and 0.22, respectively. Surprisingly, histogram standard deviation and variance features were also quite robust. Some GLCM features were also quite robust across conditions, namely, diff. variance, sum variance, sum average, variance, and mean. Except for histogram mean, all features have a Q of larger than one in at least one of the 3% dose level conditions. As expected, the histogram mean is the most robust feature in their study. The effects of acquisition and reconstruction conditions on GLCM features vary widely, though trending toward features involving summation of product between intensities and probabilities being more robust, barring a few exceptions. Overall, care should be taken into account for variation in density and texture features if a variety of dose and reconstruction conditions are used for the quantification of lung nodules in CT, otherwise changes in quantification results may be more reflective of changes due to acquisition and reconstruction conditions than in the nodule itself.

Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

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

Zhang, Hao; Tan, Shan; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan

2014-01-01

Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changesmore » resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.« less

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis

PubMed Central

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. Objective The aims were to describe how to: (i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and (ii) automatically identify the features that best distinguish the groups. Methods The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described – simple or complex; presentation order – which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo18 were used,which included 200 healthy Brazilians of both genders. Results and Conclusion A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods. PMID:29213908

Clinical and prognostic subforms of new daily-persistent headache

PubMed Central

Grosberg, B.M.; Napchan, U.; Crystal, S.C.; Lipton, R.B.

2010-01-01

Background: According to the International Classification of Headache Disorders (ICHD)–2, primary daily headaches unremitting from onset are classified as new daily-persistent headache (NDPH) only if migraine features are absent. When migraine features are present, classification is problematic. Methods: We developed a revised NDPH definition not excluding migraine features (NDPH-R), and applied it to consecutive patients seen at the Montefiore Headache Center. We divided this group into patients meeting ICHD-2 criteria (NDPH-ICHD) and those with too many migraine features for ICHD-2 (NDPH-mf). We compared clinical and demographic features in these groups, identifying 3 prognostic subgroups: persisting, remitting, and relapsing-remitting. Remitting and relapsing-remitting patients were combined into a nonpersisting group. Results: Of 71 NDPH-R patients, 31 (43.7%) also met NDPH-ICHD-2 criteria. The NDPH-mf and the NDPH-ICHD-2 groups were similar in most clinical features though the NDPH-mf group was younger, included more women, and had a higher frequency of depression. The groups were similar in the prevalence of allodynia, triptan responsiveness, and prognosis. NDPH-R prognostic subforms were also very similar, although the persisting subform was more likely to be of white race, to have anxiety or depression, and to have a younger onset age. Conclusions: Current International Classification of Headache Disorders (ICHD)–2 criteria exclude the majority of patients with primary headache unremitting from onset. The proposed criteria for revised new daily-persistent headache definition not excluding migraine features (NDPH-R) classify these patients into a relatively homogeneous group based on demographics, clinical features, and prognosis. Both new daily-persistent headache with too many migraine features for ICHD-2 and new daily-persistent headache meeting ICHD-2 criteria include patients in equal proportions that fall into the persisting, remitting, and relapsing-remitting subgroups. Our criteria for NDPH-R should be considered for inclusion in ICHD-3. PMID:20421580

Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering.

PubMed

Rodríguez-Sotelo, J L; Peluffo-Ordoñez, D; Cuesta-Frau, D; Castellanos-Domínguez, G

2012-10-01

The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Neuropathologic features associated with Alzheimer disease diagnosis

PubMed Central

Grinberg, L.T.; Miller, B.; Kawas, C.; Yaffe, K.

2011-01-01

Objective: To examine whether the association between clinical Alzheimer disease (AD) diagnosis and neuropathology and the precision by which neuropathology differentiates people with clinical AD from those with normal cognition varies by age. Methods: We conducted a cross-sectional analysis of 2,014 older adults (≥70 years at death) from the National Alzheimer's Coordinating Center database with clinical diagnosis of normal cognition (made ≤1 year before death, n = 419) or AD (at ≥65 years, n = 1,595) and a postmortem neuropathologic examination evaluating AD pathology (neurofibrillary tangles, neuritic plaques) and non-AD pathology (diffuse plaques, amyloid angiopathy, Lewy bodies, macrovascular disease, microvascular disease). We used adjusted logistic regression to analyze the relationship between clinical AD diagnosis and neuropathologic features, area under the receiver operating characteristic curve (c statistic) to evaluate how precisely neuropathology differentiates between cognitive diagnoses, and an interaction to identify effect modification by age group. Results: In a model controlling for coexisting neuropathologic features, the relationship between clinical AD diagnosis and neurofibrillary tangles was significantly weaker with increasing age (p < 0.001 for interaction). The aggregate of all neuropathologic features more strongly differentiated people with clinical AD from those without in younger age groups (70–74 years: c statistic, 95% confidence interval: 0.93, 0.89–0.96; 75–84 years: 0.95, 0.87–0.95; ≥85 years: 0.83, 0.80–0.87). Non-AD pathology significantly improved precision of differentiation across all age groups (p < 0.004). Conclusion: Clinical AD diagnosis was more weakly associated with neurofibrillary tangles among the oldest old compared to younger age groups, possibly due to less accurate clinical diagnosis, better neurocompensation, or unaccounted pathology among the oldest old. PMID:22031532

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Citation Sentiment Analysis in Clinical Trial Papers

PubMed Central

Xu, Jun; Zhang, Yaoyun; Wu, Yonghui; Wang, Jingqi; Dong, Xiao; Xu, Hua

2015-01-01

In scientific writing, positive credits and negative criticisms can often be seen in the text mentioning the cited papers, providing useful information about whether a study can be reproduced or not. In this study, we focus on citation sentiment analysis, which aims to determine the sentiment polarity that the citation context carries towards the cited paper. A citation sentiment corpus was annotated first on clinical trial papers. The effectiveness of n-gram and sentiment lexicon features, and problem-specified structure features for citation sentiment analysis were then examined using the annotated corpus. The combined features from the word n-grams, the sentiment lexicons and the structure information achieved the highest Micro F-score of 0.860 and Macro-F score of 0.719, indicating that it is feasible to use machine learning methods for citation sentiment analysis in biomedical publications. A comprehensive comparison between citation sentiment analysis of clinical trial papers and other general domains were conducted, which additionally highlights the unique challenges within this domain. PMID:26958274

Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment.

PubMed

Raunig, David L; McShane, Lisa M; Pennello, Gene; Gatsonis, Constantine; Carson, Paul L; Voyvodic, James T; Wahl, Richard L; Kurland, Brenda F; Schwarz, Adam J; Gönen, Mithat; Zahlmann, Gudrun; Kondratovich, Marina V; O'Donnell, Kevin; Petrick, Nicholas; Cole, Patricia E; Garra, Brian; Sullivan, Daniel C

2015-02-01

Technological developments and greater rigor in the quantitative measurement of biological features in medical images have given rise to an increased interest in using quantitative imaging biomarkers to measure changes in these features. Critical to the performance of a quantitative imaging biomarker in preclinical or clinical settings are three primary metrology areas of interest: measurement linearity and bias, repeatability, and the ability to consistently reproduce equivalent results when conditions change, as would be expected in any clinical trial. Unfortunately, performance studies to date differ greatly in designs, analysis method, and metrics used to assess a quantitative imaging biomarker for clinical use. It is therefore difficult or not possible to integrate results from different studies or to use reported results to design studies. The Radiological Society of North America and the Quantitative Imaging Biomarker Alliance with technical, radiological, and statistical experts developed a set of technical performance analysis methods, metrics, and study designs that provide terminology, metrics, and methods consistent with widely accepted metrological standards. This document provides a consistent framework for the conduct and evaluation of quantitative imaging biomarker performance studies so that results from multiple studies can be compared, contrasted, or combined. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Quality assurance of a gimbaled head swing verification using feature point tracking.

PubMed

Miura, Hideharu; Ozawa, Shuichi; Enosaki, Tsubasa; Kawakubo, Atsushi; Hosono, Fumika; Yamada, Kiyoshi; Nagata, Yasushi

2017-01-01

To perform dynamic tumor tracking (DTT) for clinical applications safely and accurately, gimbaled head swing verification is important. We propose a quantitative gimbaled head swing verification method for daily quality assurance (QA), which uses feature point tracking and a web camera. The web camera was placed on a couch at the same position for every gimbaled head swing verification, and could move based on a determined input function (sinusoidal patterns; amplitude: ± 20 mm; cycle: 3 s) in the pan and tilt directions at isocenter plane. Two continuous images were then analyzed for each feature point using the pyramidal Lucas-Kanade (LK) method, which is an optical flow estimation algorithm. We used a tapped hole as a feature point of the gimbaled head. The period and amplitude were analyzed to acquire a quantitative gimbaled head swing value for daily QA. The mean ± SD of the period were 3.00 ± 0.03 (range: 3.00-3.07) s and 3.00 ± 0.02 (range: 3.00-3.07) s in the pan and tilt directions, respectively. The mean ± SD of the relative displacement were 19.7 ± 0.08 (range: 19.6-19.8) mm and 18.9 ± 0.2 (range: 18.4-19.5) mm in the pan and tilt directions, respectively. The gimbaled head swing was reliable for DTT. We propose a quantitative gimbaled head swing verification method for daily QA using the feature point tracking method and a web camera. Our method can quantitatively assess the gimbaled head swing for daily QA from baseline values, measured at the time of acceptance and commissioning. © 2016 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

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

Fan, J; Fan, J; Hu, W

Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditionalmore » probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.« less

Gram stain method shows better sensitivity than clinical criteria for detection of bacterial vaginosis in surveillance of pregnant, low-income women in a clinical setting.

PubMed Central

Tam, M T; Yungbluth, M; Myles, T

1998-01-01

OBJECTIVE: The purpose of the study is to determine whether the Gram stain method is superior to the clinical criteria for the diagnosis of bacterial vaginosis in low-income pregnant women seen in a resident clinic setting. The clinical criteria is the current diagnostic method employed to diagnose bacterial vaginosis. STUDY DESIGN: In this study, 51 pregnant women with vaginal discharge were prospectively evaluated. All were screened using the clinical criteria, Gram stain method, and culture of the discharge. The modified scoring system instituted by Nugent et al. (J Clin Microbiol 29:297-301, 1991) was employed in reading the Gram stain smears. The clinical criteria were then compared with the Gram stain method. Isolation of moderate to many Gardnerella vaginalis growth by culture was used as the confirmatory finding. RESULTS: Sensitivity of the Gram stain method (91%) was significantly higher than that of the clinical criteria (46%), (sign test P = 0.0023, < 0.01). The Gram stain method also has both a low false-negative (4%) and high negative predictive value (96%), making it an ideal diagnostic test. CONCLUSION: The Gram stain method is a rapid and cost-effective test that is also highly reproducible and readily available in many laboratories. These features make the Gram stain method a more desirable screening procedure for bacterial vaginosis in a clinic population. PMID:9894174

ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

PubMed Central

Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

2012-01-01

Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

NASA Astrophysics Data System (ADS)

Leijenaar, Ralph T. H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J. C.; Troost, Esther G. C.; Boellaard, Ronald; Aerts, Hugo J. W. L.; Gillies, Robert J.; Lambin, Philippe

2015-08-01

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) RB, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, RB was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values-which was used as a surrogate for textural feature interpretation-between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

What to do with thyroid nodules showing benign cytology and BRAF(V600E) mutation? A study based on clinical and radiologic features using a highly sensitive analytic method.

PubMed

Kim, Soo-Yeon; Kim, Eun-Kyung; Kwak, Jin Young; Moon, Hee Jung; Yoon, Jung Hyun

2015-02-01

BRAF(V600E) mutation analysis has been used as a complementary diagnostic tool to ultrasonography-guided, fine-needle aspiration (US-FNA) in the diagnosis of thyroid nodule with high specificity reported up to 100%. When highly sensitive analytic methods are used, however, false-positive results of BRAF(V600E) mutation analysis have been reported. In this study, we investigated the clinical, US features, and outcome of patients with thyroid nodules with benign cytology but positive BRAF(V600E) mutation using highly sensitive analytic methods from US-FNA. This study included 22 nodules in 22 patients (3 men, 19 women; mean age, 53 years) with benign cytology but positive BRAF(V600E) mutation from US-FNA. US features were categorized according to the internal components, echogenicity, margin, calcifications, and shape. Suspicious US features included markedly hypoechogenicity, noncircumscribed margins, micro or mixed calcifications, and nonparallel shape. Nodules were considered to have either concordant or discordant US features to benign cytology. Medical records and imaging studies were reviewed for final cytopathology results and outcomes during follow-up. Among the 22 nodules, 17 nodules were reviewed. Fifteen of 17 nodules were malignant, and 2 were benign. The benign nodules were confirmed as adenomatous hyperplasia with underlying lymphocytic thyroiditis and a fibrotic nodule with dense calcification. Thirteen of the 15 malignant nodules had 2 or more suspicious US features, and all 15 nodules were considered to have discordant cytology considering suspicious US features. Five nodules had been followed with US or US-FNA without resection, and did not show change in size or US features on follow-up US examinations. BRAF(V600E) mutation analysis is a highly sensitive diagnostic tool in the diagnosis of papillary thyroid carcinomas. In the management of thyroid nodules with benign cytology but positive BRAF(V600E) mutation, thyroidectomy should be considered in nodules which have 2 or more suspicious US features and are considered discordant on image-cytology correlation. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods.

PubMed

Parkinson, Craig; Foley, Kieran; Whybra, Philip; Hills, Robert; Roberts, Ashley; Marshall, Chris; Staffurth, John; Spezi, Emiliano

2018-04-11

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification. Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated. Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group. Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

Morphometrical study on senile larynx.

PubMed

Zieliński, R

2001-01-01

The aim of the study was a morphometrical macroscopic evaluation of senile larynges, according to its usefulness in ORL diagnostic and operational methods. Larynx preparations were taken from cadavers of both sexes, of age 65 and over, about 24 hours after death. Clinically important laryngeal diameters were collected using common morphometrical methods. A few body features were also being gathered. Computer statistical methods were used in data assessment, including basic statistics and linear correlations between diameters and between diameters and body features. The data presented in the study may be very helpful in evaluation of diagnostic methods. It may also help in selection of right operational tool' sizes, the most appropriate operational technique choice, preoperative preparations and designing and building virtual and plastic models for physicians' training.

How well do services for young people with long term conditions deliver features proposed to improve transition?

PubMed

Colver, A; Pearse, R; Watson, R M; Fay, M; Rapley, T; Mann, K D; Le Couteur, A; Parr, J R; McConachie, H

2018-05-08

For young people with long-term conditions, transition from child to adult-oriented health services is a critical period which, if not managed well, may lead to poor outcomes. There are features of transition services which guidance and research suggest improve outcomes. We studied nine such features, calling them 'proposed beneficial features': age-banded clinic; meet adult team before transfer; promotion of health self-efficacy; written transition plan; appropriate parent involvement; key worker; coordinated team; holistic life-skills training; transition manager for clinical team. We aimed to describe the extent to which service providers offer these nine features, and to compare this with young people's reported experience of them. A longitudinal, mixed methods study followed 374 young people as their care moved from child to adult health services. Participants had type 1 diabetes, cerebral palsy or autism spectrum disorder with additional mental health difficulties. Data are reported from the first two visits, one year apart. Three hundred four (81.3%) of the young people took part in the second visit (128 with diabetes, 91 with autism, 85 with cerebral palsy). Overall, the nine proposed beneficial features of transition services were poorly provided. Fewer than half of services stated they provided an age-banded clinic, written transition plan, transition manager for clinical team, a protocol for promotion of health self-efficacy, or holistic life-skills training. To varying degrees, young people reported that they had not experienced the features which services said they provided. For instance, the agreement for written transition plan, holistic life-skills training and key worker, was 30, 43 and 49% respectively. Agreement was better for appropriate parent involvement, age-banded clinic, promotion of health self-efficacy and coordinated team at 77, 77, 80 and 69% respectively. Variation in the meaning of the features as experienced by young people and families was evident from qualitative interviews and observations. UK services provide only some of the nine proposed beneficial features for supporting healthcare transition of young people with long term conditions. Observational studies or trials which examine the influence of features of transition services on outcomes should ensure that the experiences of young people and families are captured, and not rely on service specifications.

Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic.

PubMed

Cai, Sophie; Elze, Tobias; Bex, Peter J; Wiggs, Janey L; Pasquale, Louis R; Shen, Lucy Q

2017-04-01

To assess the clinical validity of visual field (VF) archetypal analysis, a previously developed machine learning method for decomposing any Humphrey VF (24-2) into a weighted sum of clinically recognizable VF loss patterns. For each of 16 previously identified VF loss patterns ("archetypes," denoted AT1 through AT16), we screened 30,995 reliable VFs to select 10-20 representative patients whose VFs had the highest decomposition coefficients for each archetype. VF global indices and patient ocular and demographic features were extracted retrospectively. Based on resemblances between VF archetypes and clinically observed VF patterns, hypotheses were generated for associations between certain VF archetypes and clinical features, such as an association between AT6 (central island, representing severe VF loss) and large cup-to-disk ratio (CDR). Distributions of the selected clinical features were compared between representative eyes of certain archetypes and all other eyes using the two-tailed t-test or Fisher exact test. 243 eyes from 243 patients were included, representative of AT1 through AT16. CDR was more often ≥ 0.7 among eyes representative of AT6 (central island; p = 0.002), AT10 (inferior arcuate defect; p = 0.048), AT14 (superior paracentral defect; p = 0.016), and AT16 (inferior paracentral defect; p = 0.016) than other eyes. CDR was more often < 0.7 among eyes representative of AT1 (no focal defect; p < 0.001) and AT2 (superior defect; p = 0.027), which was also associated with ptosis (p < 0.001). AT12 (temporal hemianopia) was associated with history of stroke (p = 0.022). AT11 (concentric peripheral defect) trended toward association with trial lens correction > 6D (p = 0.069). Shared clinical features between computationally derived VF archetypes and clinically observed VF patterns support the clinical validity of VF archetypal analysis.

Big Data and Clinicians: A Review on the State of the Science

PubMed Central

Wang, Weiqi

2014-01-01

Background In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. Objective The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. Methods We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years. Results This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data. Conclusions Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data. PMID:25600256

Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

PubMed Central

Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad; Greulich, Heidi; Ambrogio, Lauren; Schouten-van Meeteren, Netteke; Zhou, Tianni; Buxton, Allen; Kool, Marcel; Meyerson, Matthew; Pomeroy, Scott L.; Mesirov, Jill P.

2011-01-01

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification. PMID:21357789

Diagnostic analysis of liver B ultrasonic texture features based on LM neural network

NASA Astrophysics Data System (ADS)

Chi, Qingyun; Hua, Hu; Liu, Menglin; Jiang, Xiuying

2017-03-01

In this study, B ultrasound images of 124 benign and malignant patients were randomly selected as the study objects. The B ultrasound images of the liver were treated by enhanced de-noising. By constructing the gray level co-occurrence matrix which reflects the information of each angle, Principal Component Analysis of 22 texture features were extracted and combined with LM neural network for diagnosis and classification. Experimental results show that this method is a rapid and effective diagnostic method for liver imaging, which provides a quantitative basis for clinical diagnosis of liver diseases.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

PubMed

Kallenberg, Michiel; Petersen, Kersten; Nielsen, Mads; Ng, Andrew Y; Pengfei Diao; Igel, Christian; Vachon, Celine M; Holland, Katharina; Winkel, Rikke Rass; Karssemeijer, Nico; Lillholm, Martin

2016-05-01

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

[Clinical characteristics and surgical management in patients with third and fourth branchial anomalies].

PubMed

Li, Y X; He, X G; Wang, Y; Yang, X

2016-08-05

Objective: To analysize the clinical characteristics as well as the effect and methods of the surgical treatment in patiets with the third and fourth branchial anomalies. Method: The clinical data of 25 patients diagnosed as third and fourth branchial cleft fistula by pathological method were analyzed retrospectively.Two of 25 patients had undergone fistulectomy simply.Based on the embryologicc and anatomic features of branchial anomalies,23 of 25 patients had received different types of selective neck dissection.All of lesions were confirmed as branchial cleft fistula by pathology.All patients were received the examinations of Esophagus myelography,MRI and CT preoperatively. Result: The features of the third and the fourth bianchial fistula were as following:most patients suffered from recurrent neck abscess and had undergone incision and drainage. Esophagus myelography and CT were important auxiliary examination for branchial anomalies.No recurrent and complications were found in all patients by using treatment of selective neck dissection (23/25 cases) and fistulectomy simply(2/25 cases) within 12 to 36 months following-up,postoperatively. Conclusion: Branchial anomalies is characterized by recurrent acute abscess,acute thyroiditis or fistula secretion inferior to neck.Complete removal of branchial lesions and inflammatory granuloma using selective neck dissection is a safty and effective treatment for recurrent branchial anomalies. Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

PubMed

Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

2016-05-01

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

Identifying the domains of context important to implementation science: a study protocol.

PubMed

Squires, Janet E; Graham, Ian D; Hutchinson, Alison M; Michie, Susan; Francis, Jill J; Sales, Anne; Brehaut, Jamie; Curran, Janet; Ivers, Noah; Lavis, John; Linklater, Stefanie; Fenton, Shannon; Noseworthy, Thomas; Vine, Jocelyn; Grimshaw, Jeremy M

2015-09-28

There is growing recognition that "context" can and does modify the effects of implementation interventions aimed at increasing healthcare professionals' use of research evidence in clinical practice. However, conceptual clarity about what exactly comprises "context" is lacking. The purpose of this research program is to develop, refine, and validate a framework that identifies the key domains of context (and their features) that can facilitate or hinder (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. A multi-phased investigation of context using mixed methods will be conducted. The first phase is a concept analysis of context using the Walker and Avant method to distinguish between the defining and irrelevant attributes of context. This phase will result in a preliminary framework for context that identifies its important domains and their features according to the published literature. The second phase is a secondary analysis of qualitative data from 13 studies of interviews with 312 healthcare professionals on the perceived barriers and enablers to their application of research evidence in clinical practice. These data will be analyzed inductively using constant comparative analysis. For the third phase, we will conduct semi-structured interviews with key health system stakeholders and change agents to elicit their knowledge and beliefs about the contextual features that influence the effectiveness of implementation interventions and healthcare professionals' use of evidence in clinical practice. Results from all three phases will be synthesized using a triangulation protocol to refine the context framework drawn from the concept analysis. The framework will then be assessed for content validity using an iterative Delphi approach with international experts (researchers and health system stakeholders/change agents). This research program will result in a framework that identifies the domains of context and their features that can facilitate or hinder: (1) healthcare professionals' use of evidence in clinical practice and (2) the effectiveness of implementation interventions. The framework will increase the conceptual clarity of the term "context" for advancing implementation science, improving healthcare professionals' use of evidence in clinical practice, and providing greater understanding of what interventions are likely to be effective in which contexts.

A practical salient region feature based 3D multi-modality registration method for medical images

NASA Astrophysics Data System (ADS)

Hahn, Dieter A.; Wolz, Gabriele; Sun, Yiyong; Hornegger, Joachim; Sauer, Frank; Kuwert, Torsten; Xu, Chenyang

2006-03-01

We present a novel representation of 3D salient region features and its integration into a hybrid rigid-body registration framework. We adopt scale, translation and rotation invariance properties of those intrinsic 3D features to estimate a transform between underlying mono- or multi-modal 3D medical images. Our method combines advantageous aspects of both feature- and intensity-based approaches and consists of three steps: an automatic extraction of a set of 3D salient region features on each image, a robust estimation of correspondences and their sub-pixel accurate refinement with outliers elimination. We propose a region-growing based approach for the extraction of 3D salient region features, a solution to the problem of feature clustering and a reduction of the correspondence search space complexity. Results of the developed algorithm are presented for both mono- and multi-modal intra-patient 3D image pairs (CT, PET and SPECT) that have been acquired for change detection, tumor localization, and time based intra-person studies. The accuracy of the method is clinically evaluated by a medical expert with an approach that measures the distance between a set of selected corresponding points consisting of both anatomical and functional structures or lesion sites. This demonstrates the robustness of the proposed method to image overlap, missing information and artefacts. We conclude by discussing potential medical applications and possibilities for integration into a non-rigid registration framework.

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

PubMed

Tranchevent, Léon-Charles; Nazarov, Petr V; Kaoma, Tony; Schmartz, Georges P; Muller, Arnaud; Kim, Sang-Yoon; Rajapakse, Jagath C; Azuaje, Francisco

2018-06-07

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

Gait Analysis Using Wearable Sensors

PubMed Central

Tao, Weijun; Liu, Tao; Zheng, Rencheng; Feng, Hutian

2012-01-01

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications. PMID:22438763

Outcomes of Diagnostic Exome Sequencing in Patients With Diagnosed or Suspected Autism Spectrum Disorders.

PubMed

Rossi, Mari; El-Khechen, Dima; Black, Mary Helen; Farwell Hagman, Kelly D; Tang, Sha; Powis, Zöe

2017-05-01

Exome sequencing has recently been proved to be a successful diagnostic method for complex neurodevelopmental disorders. However, the diagnostic yield of exome sequencing for autism spectrum disorders has not been extensively evaluated in large cohorts to date. We performed diagnostic exome sequencing in a cohort of 163 individuals with autism spectrum disorder (66.3%) or autistic features (33.7%). The diagnostic yield observed in patients in our cohort was 25.8% (42 of 163) for positive or likely positive findings in characterized disease genes, while a candidate genetic etiology was reported for an additional 3.3% (4 of 120) of patients. Among the positive findings in the patients with autism spectrum disorder or autistic features, 61.9% were the result of de novo mutations. Patients presenting with psychiatric conditions or ataxia or paraplegia in addition to autism spectrum disorder or autistic features were significantly more likely to receive positive results compared with patients without these clinical features (95.6% vs 27.1%, P < 0.0001; 83.3% vs 21.2%, P < 0.0001, respectively). The majority of the positive findings were in recently identified autism spectrum disorder genes, supporting the importance of diagnostic exome sequencing for patients with autism spectrum disorder or autistic features as the causative genes might evade traditional sequential or panel testing. These results suggest that diagnostic exome sequencing would be an efficient primary diagnostic method for patients with autism spectrum disorders or autistic features. Moreover, our data may aid clinicians to better determine which subset of patients with autism spectrum disorder with additional clinical features would benefit the most from diagnostic exome sequencing. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

NASA Astrophysics Data System (ADS)

Huang, Lijuan; Fan, Ming; Li, Lihua; Zhang, Juan; Shao, Guoliang; Zheng, Bin

2016-03-01

Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79+/-0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

PubMed

Eleftheriadis, Stefanos; Rudovic, Ognjen; Pantic, Maja

2016-10-05

Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. Specifically, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available datasets (CK+, DISFA and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared to the state-of-the-art methods for the task.

Down syndrome detection from facial photographs using machine learning techniques

NASA Astrophysics Data System (ADS)

Zhao, Qian; Rosenbaum, Kenneth; Sze, Raymond; Zand, Dina; Summar, Marshall; Linguraru, Marius George

2013-02-01

Down syndrome is the most commonly occurring chromosomal condition; one in every 691 babies in United States is born with it. Patients with Down syndrome have an increased risk for heart defects, respiratory and hearing problems and the early detection of the syndrome is fundamental for managing the disease. Clinically, facial appearance is an important indicator in diagnosing Down syndrome and it paves the way for computer-aided diagnosis based on facial image analysis. In this study, we propose a novel method to detect Down syndrome using photography for computer-assisted image-based facial dysmorphology. Geometric features based on facial anatomical landmarks, local texture features based on the Contourlet transform and local binary pattern are investigated to represent facial characteristics. Then a support vector machine classifier is used to discriminate normal and abnormal cases; accuracy, precision and recall are used to evaluate the method. The comparison among the geometric, local texture and combined features was performed using the leave-one-out validation. Our method achieved 97.92% accuracy with high precision and recall for the combined features; the detection results were higher than using only geometric or texture features. The promising results indicate that our method has the potential for automated assessment for Down syndrome from simple, noninvasive imaging data.

Psychogenic Tremor: A Video Guide to Its Distinguishing Features

PubMed Central

Thenganatt, Mary Ann; Jankovic, Joseph

2014-01-01

Background Psychogenic tremor is the most common psychogenic movement disorder. It has characteristic clinical features that can help distinguish it from other tremor disorders. There is no diagnostic gold standard and the diagnosis is based primarily on clinical history and examination. Despite proposed diagnostic criteria, the diagnosis of psychogenic tremor can be challenging. While there are numerous studies evaluating psychogenic tremor in the literature, there are no publications that provide a video/visual guide that demonstrate the clinical characteristics of psychogenic tremor. Educating clinicians about psychogenic tremor will hopefully lead to earlier diagnosis and treatment. Methods We selected videos from the database at the Parkinson’s Disease Center and Movement Disorders Clinic at Baylor College of Medicine that illustrate classic findings supporting the diagnosis of psychogenic tremor. Results We include 10 clinical vignettes with accompanying videos that highlight characteristic clinical signs of psychogenic tremor including distractibility, variability, entrainability, suggestibility, and coherence. Discussion Psychogenic tremor should be considered in the differential diagnosis of patients presenting with tremor, particularly if it is of abrupt onset, intermittent, variable and not congruous with organic tremor. The diagnosis of psychogenic tremor, however, should not be simply based on exclusion of organic tremor, such as essential, parkinsonian, or cerebellar tremor, but on positive criteria demonstrating characteristic features. Early recognition and management are critical for good long-term outcome. PMID:25243097

Classifying medical relations in clinical text via convolutional neural networks.

PubMed

He, Bin; Guan, Yi; Dai, Rui

2018-05-16

Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

Extracting BI-RADS Features from Portuguese Clinical Texts.

PubMed

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2012-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method.

Automatic quality control in clinical (1)H MRSI of brain cancer.

PubMed

Pedrosa de Barros, Nuno; McKinley, Richard; Knecht, Urspeter; Wiest, Roland; Slotboom, Johannes

2016-05-01

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1)H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

Automatic classification of written descriptions by healthy adults: An overview of the application of natural language processing and machine learning techniques to clinical discourse analysis.

PubMed

Toledo, Cíntia Matsuda; Cunha, Andre; Scarton, Carolina; Aluísio, Sandra

2014-01-01

Discourse production is an important aspect in the evaluation of brain-injured individuals. We believe that studies comparing the performance of brain-injured subjects with that of healthy controls must use groups with compatible education. A pioneering application of machine learning methods using Brazilian Portuguese for clinical purposes is described, highlighting education as an important variable in the Brazilian scenario. The aims were to describe how to:(i) develop machine learning classifiers using features generated by natural language processing tools to distinguish descriptions produced by healthy individuals into classes based on their years of education; and(ii) automatically identify the features that best distinguish the groups. The approach proposed here extracts linguistic features automatically from the written descriptions with the aid of two Natural Language Processing tools: Coh-Metrix-Port and AIC. It also includes nine task-specific features (three new ones, two extracted manually, besides description time; type of scene described - simple or complex; presentation order - which type of picture was described first; and age). In this study, the descriptions by 144 of the subjects studied in Toledo 18 were used,which included 200 healthy Brazilians of both genders. A Support Vector Machine (SVM) with a radial basis function (RBF) kernel is the most recommended approach for the binary classification of our data, classifying three of the four initial classes. CfsSubsetEval (CFS) is a strong candidate to replace manual feature selection methods.

Image processing and machine learning for fully automated probabilistic evaluation of medical images.

PubMed

Sajn, Luka; Kukar, Matjaž

2011-12-01

The paper presents results of our long-term study on using image processing and data mining methods in a medical imaging. Since evaluation of modern medical images is becoming increasingly complex, advanced analytical and decision support tools are involved in integration of partial diagnostic results. Such partial results, frequently obtained from tests with substantial imperfections, are integrated into ultimate diagnostic conclusion about the probability of disease for a given patient. We study various topics such as improving the predictive power of clinical tests by utilizing pre-test and post-test probabilities, texture representation, multi-resolution feature extraction, feature construction and data mining algorithms that significantly outperform medical practice. Our long-term study reveals three significant milestones. The first improvement was achieved by significantly increasing post-test diagnostic probabilities with respect to expert physicians. The second, even more significant improvement utilizes multi-resolution image parametrization. Machine learning methods in conjunction with the feature subset selection on these parameters significantly improve diagnostic performance. However, further feature construction with the principle component analysis on these features elevates results to an even higher accuracy level that represents the third milestone. With the proposed approach clinical results are significantly improved throughout the study. The most significant result of our study is improvement in the diagnostic power of the whole diagnostic process. Our compound approach aids, but does not replace, the physician's judgment and may assist in decisions on cost effectiveness of tests. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Prognostic Utility of the 21-Gene Assay in Hormone Receptor–Positive Operable Breast Cancer Compared With Classical Clinicopathologic Features

PubMed Central

Goldstein, Lori J.; Gray, Robert; Badve, Sunil; Childs, Barrett H.; Yoshizawa, Carl; Rowley, Steve; Shak, Steven; Baehner, Frederick L.; Ravdin, Peter M.; Davidson, Nancy E.; Sledge, George W.; Perez, Edith A.; Shulman, Lawrence N.; Martino, Silvana; Sparano, Joseph A.

2008-01-01

Purpose Adjuvant! is a standardized validated decision aid that projects outcomes in operable breast cancer based on classical clinicopathologic features and therapy. Genomic classifiers offer the potential to more accurately identify individuals who benefit from chemotherapy than clinicopathologic features. Patients and Methods A sample of 465 patients with hormone receptor (HR) –positive breast cancer with zero to three positive axillary nodes who did (n = 99) or did not have recurrence after chemohormonal therapy had tumor tissue evaluated using a 21-gene assay. Histologic grade and HR expression were evaluated locally and in a central laboratory. Results Recurrence Score (RS) was a highly significant predictor of recurrence, including node-negative and node-positive disease (P < .001 for both) and when adjusted for other clinical variables. RS also predicted recurrence more accurately than clinical variables when integrated by an algorithm modeled after Adjuvant! that was adjusted to 5-year outcomes. The 5-year recurrence rate was only 5% or less for the estimated 46% of patients who have a low RS (< 18). Conclusion The 21-gene assay was a more accurate predictor of relapse than standard clinical features for individual patients with HR-positive operable breast cancer treated with chemohormonal therapy and provides information that is complementary to features typically used in anatomic staging, such as tumor size and lymph node involvement. The 21-gene assay may be used to select low-risk patients for abbreviated chemotherapy regimens similar to those used in our study or high-risk patients for more aggressive regimens or clinical trials evaluating novel treatments. PMID:18678838

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Clinical and microbiological features of Haemophilus influenzae vulvovaginitis in young girls

PubMed Central

Cox, R A; Slack, M P E

2002-01-01

Aims: To define the clinical and microbiological features of vulvovaginitis in prepubertal girls whose genital swabs yielded Haemophilus influenzae. Methods: Laboratory based study and retrospective collection of clinical data from the requesting doctors. Results: Thirty eight isolates of non-capsulate Haemophilus influenzae and one of H parainfluenzae were isolated from 32 girls aged 18 months to 11 years. No other pathogens, such as β haemolytic streptococci or yeasts, were present with H influenzae. The most common biotype was biotype II, comprising 57% of the 26 isolates biotyped. Six children had more than one episode of vulvovaginitis caused by H influenzae and a total of 14 children had recurrent vaginal symptoms. Conclusion: Children who have H influenzae vulvovaginitis are at risk of recurrent symptoms. Biotype II is the one most commonly associated with this condition. PMID:12461068

Rett Syndrome: Revised Diagnostic Criteria and Nomenclature

PubMed Central

Neul, Jeffrey L.; Kaufmann, Walter E.; Glaze, Daniel G.; Christodoulou, John; Clarke, Angus J.; Bahi-Buisson, Nadia; Leonard, Helen; Bailey, Mark E. S.; Schanen, N. Carolyn; Zappella, Michele; Renieri, Alessandra; Huppke, Peter; Percy, Alan K.

2010-01-01

Objective Rett syndrome (RTT) is a severe neurodevelopmental disease that affects approximately 1 in 10,000 live female births and is often caused by mutations in Methyl-CpG-binding protein 2 (MECP2). Despite distinct clinical features, the accumulation of clinical and molecular information in recent years has generated considerable confusion regarding the diagnosis of RTT. The purpose of this work was revise and clarify 2002 consensus criteria for the diagnosis of RTT in anticipation of treatment trials. Method RettSearch members, representing the majority of the international clinical RTT specialists, participated in an iterative process to come to a consensus on a revised and simplified clinical diagnostic criteria for RTT. Results The clinical criteria required for the diagnosis of classic and atypical RTT were clarified and simplified. Guidelines for the diagnosis and molecular evaluation of specific variant forms of RTT were developed. Interpretation These revised criteria provide clarity regarding the key features required for the diagnosis of RTT and reinforce the concept that RTT is a clinical diagnosis based on distinct clinical criteria, independent of molecular findings. We recommend that these criteria and guidelines be utilized in any proposed clinical research. PMID:21154482

Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.

PubMed

Chen, Yinsheng; Li, Zeju; Wu, Guoqing; Yu, Jinhua; Wang, Yuanyuan; Lv, Xiaofei; Ju, Xue; Chen, Zhongping

2018-07-01

Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making. Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model. Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p < 0.05 and 420 features with p < 0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort). Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

PubMed Central

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

Objective To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials and methods Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. Results The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. Discussion With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Conclusions Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC. PMID:23616206

Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

NASA Astrophysics Data System (ADS)

Watari, Chinatsu; Matsuhiro, Mikio; Näppi, Janne J.; Nasirudin, Radin A.; Hironaka, Toru; Kawata, Yoshiki; Niki, Noboru; Yoshida, Hiroyuki

2018-03-01

We investigated the effect of radiomic texture-curvature (RTC) features of lung CT images in the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively collected 70 RA-ILD patients who underwent thin-section lung CT and serial pulmonary function tests. After the extraction of the lung region, we computed hyper-curvature features that included the principal curvatures, curvedness, bright/dark sheets, cylinders, blobs, and curvature scales for the bronchi and the aerated lungs. We also computed gray-level co-occurrence matrix (GLCM) texture features on the segmented lungs. An elastic-net penalty method was used to select and combine these features with a Cox proportional hazards model for predicting the survival of the patient. Evaluation was performed by use of concordance index (C-index) as a measure of prediction performance. The C-index values of the texture features, hyper-curvature features, and the combination thereof (RTC features) in predicting patient survival was estimated by use of bootstrapping with 2,000 replications, and they were compared with an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by means of two-sided t-test. Bootstrap evaluation yielded the following C-index values for the clinical and radiomic features: (a) GAP index: 78.3%; (b) GLCM texture features: 79.6%; (c) hypercurvature features: 80.8%; and (d) RTC features: 86.8%. The RTC features significantly outperformed any of the other predictors (P < 0.001). The Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the RTC features showed statistically significant (P < 0.0001) difference. Thus, the RTC features can provide an effective imaging biomarker for predicting the overall survival of patients with RA-ILD.

Paediatric autoimmune encephalopathies: clinical features, laboratory investigations and outcomes in patients with or without antibodies to known central nervous system autoantigens

PubMed Central

Hacohen, Yael; Wright, Sukhvir; Waters, Patrick; Agrawal, Shakti; Carr, Lucinda; Cross, Helen; De Sousa, Carlos; DeVile, Catherine; Fallon, Penny; Gupta, Rajat; Hedderly, Tammy; Hughes, Elaine; Kerr, Tim; Lascelles, Karine; Lin, Jean-Pierre; Philip, Sunny; Pohl, Keith; Prabahkar, Prab; Smith, Martin; Williams, Ruth; Clarke, Antonia; Hemingway, Cheryl; Wassmer, Evangeline; Vincent, Angela; Lim, Ming J

2013-01-01

Objective To report the clinical and investigative features of children with a clinical diagnosis of probable autoimmune encephalopathy, both with and without antibodies to central nervous system antigens. Method Patients with encephalopathy plus one or more of neuropsychiatric symptoms, seizures, movement disorder or cognitive dysfunction, were identified from 111 paediatric serum samples referred from five tertiary paediatric neurology centres to Oxford for antibody testing in 2007–2010. A blinded clinical review panel identified 48 patients with a diagnosis of probable autoimmune encephalitis whose features are described. All samples were tested/retested for antibodies to N-methyl-D-aspartate receptor (NMDAR), VGKC-complex, LGI1, CASPR2 and contactin-2, GlyR, D1R, D2R, AMPAR, GABA(B)R and glutamic acid decarboxylase. Results Seizures (83%), behavioural change (63%), confusion (50%), movement disorder (38%) and hallucinations (25%) were common. 52% required intensive care support for seizure control or profound encephalopathy. An acute infective organism (15%) or abnormal cerebrospinal fluid (32%), EEG (70%) or MRI (37%) abnormalities were found. One 14-year-old girl had an ovarian teratoma. Serum antibodies were detected in 21/48 (44%) patients: NMDAR 13/48 (27%), VGKC-complex 7/48(15%) and GlyR 1/48(2%). Antibody negative patients shared similar clinical features to those who had specific antibodies detected. 18/34 patients (52%) who received immunotherapy made a complete recovery compared to 4/14 (28%) who were not treated; reductions in modified Rankin Scale for children scores were more common following immunotherapies. Antibody status did not appear to influence the treatment effect. Conclusions Our study outlines the common clinical and paraclinical features of children and adolescents with probable autoimmune encephalopathies. These patients, irrespective of positivity for the known antibody targets, appeared to benefit from immunotherapies and further antibody targets may be defined in the future. PMID:23175854

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI

NASA Astrophysics Data System (ADS)

Alomari, Raja'S.; Corso, Jason J.; Chaudhary, Vipin; Dhillon, Gurmeet

2010-03-01

Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity of 91% and sensitivity of 94%.

Cerebro-facio-thoracic dysplasia (Pascual-Castroviejo syndrome): Identification of a novel mutation, use of facial recognition analysis, and review of the literature

PubMed Central

Tender, Jennifer A.F.; Ferreira, Carlos R.

2018-01-01

BACKGROUND: Cerebro-facio-thoracic dysplasia (CFTD) is a rare, autosomal recessive disorder characterized by facial dysmorphism, cognitive impairment and distinct skeletal anomalies and has been linked to the TMCO1 defect syndrome. OBJECTIVE: To describe two siblings with features consistent with CFTD with a novel homozygous p.Arg114* pathogenic variant in the TMCO1 gene. METHODS: We conducted a literature review and summarized the clinical features and laboratory results of two siblings with a novel pathogenic variant in the TMCO1 gene. Facial recognition analysis was utilized to assess the specificity of facial traits. CONCLUSION: The novel homozygous p.Arg114* pathogenic variant in the TMCO1 gene is responsible for the clinical features of CFTD in two siblings. Facial recognition analysis allows unambiguous distinction of this syndrome against controls. PMID:29682451

Electroconvulsive therapy (ECT) in a six-year-old girl suffering from major depressive disorder with catatonic features.

PubMed

Esmaili, Taghi; Malek, Ayyoub

2007-02-01

ECT is generally both effective and safe in the treatment of adolescents, but treatment using ECT in children of pre-pubertal age has been less reported and is a controversial treatment. This article reports a 6-year-old girl who has been diagnosed as having major depressive disorder with catatonic features and 50% loss of weight due to food refusal. The seven-session ECT treatment with bilateral and brief pulse stimulation was successfully done. Propofol 1% was used for anesthesia. After the third session, the patient's clinical improvement began by eating. As the ECT sessions went on the signs of depression and catatonic features completely resolved. There were no noticeable clinical side effects. ECT should be considered in severe cases of child psychiatric disorders where it is life threatening, as an effective and safe method.

Clinical analysis of genome next-generation sequencing data using the Omicia platform

PubMed Central

Coonrod, Emily M; Margraf, Rebecca L; Russell, Archie; Voelkerding, Karl V; Reese, Martin G

2013-01-01

Aims Next-generation sequencing is being implemented in the clinical laboratory environment for the purposes of candidate causal variant discovery in patients affected with a variety of genetic disorders. The successful implementation of this technology for diagnosing genetic disorders requires a rapid, user-friendly method to annotate variants and generate short lists of clinically relevant variants of interest. This report describes Omicia’s Opal platform, a new software tool designed for variant discovery and interpretation in a clinical laboratory environment. The software allows clinical scientists to process, analyze, interpret and report on personal genome files. Materials & Methods To demonstrate the software, the authors describe the interactive use of the system for the rapid discovery of disease-causing variants using three cases. Results & Conclusion Here, the authors show the features of the Opal system and their use in uncovering variants of clinical significance. PMID:23895124

Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

PubMed

Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

2016-02-01

Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A computer vision framework for finger-tapping evaluation in Parkinson's disease.

PubMed

Khan, Taha; Nyholm, Dag; Westin, Jerker; Dougherty, Mark

2014-01-01

The rapid finger-tapping test (RFT) is an important method for clinical evaluation of movement disorders, including Parkinson's disease (PD). In clinical practice, the naked-eye evaluation of RFT results in a coarse judgment of symptom scores. We introduce a novel computer-vision (CV) method for quantification of tapping symptoms through motion analysis of index-fingers. The method is unique as it utilizes facial features to calibrate tapping amplitude for normalization of distance variation between the camera and subject. The study involved 387 video footages of RFT recorded from 13 patients diagnosed with advanced PD. Tapping performance in these videos was rated by two clinicians between the symptom severity levels ('0: normal' to '3: severe') using the unified Parkinson's disease rating scale motor examination of finger-tapping (UPDRS-FT). Another set of recordings in this study consisted of 84 videos of RFT recorded from 6 healthy controls. These videos were processed by a CV algorithm that tracks the index-finger motion between the video-frames to produce a tapping time-series. Different features were computed from this time series to estimate speed, amplitude, rhythm and fatigue in tapping. The features were trained in a support vector machine (1) to categorize the patient group between UPDRS-FT symptom severity levels, and (2) to discriminate between PD patients and healthy controls. A new representative feature of tapping rhythm, 'cross-correlation between the normalized peaks' showed strong Guttman correlation (μ2=-0.80) with the clinical ratings. The classification of tapping features using the support vector machine classifier and 10-fold cross validation categorized the patient samples between UPDRS-FT levels with an accuracy of 88%. The same classification scheme discriminated between RFT samples of healthy controls and PD patients with an accuracy of 95%. The work supports the feasibility of the approach, which is presumed suitable for PD monitoring in the home environment. The system offers advantages over other technologies (e.g. magnetic sensors, accelerometers, etc.) previously developed for objective assessment of tapping symptoms. Copyright © 2013 Elsevier B.V. All rights reserved.

Optimized approach for Ion Proton RNA sequencing reveals details of RNA splicing and editing features of the transcriptome.

PubMed

Brown, Roger B; Madrid, Nathaniel J; Suzuki, Hideaki; Ness, Scott A

2017-01-01

RNA-sequencing (RNA-seq) has become the standard method for unbiased analysis of gene expression but also provides access to more complex transcriptome features, including alternative RNA splicing, RNA editing, and even detection of fusion transcripts formed through chromosomal translocations. However, differences in library methods can adversely affect the ability to recover these different types of transcriptome data. For example, some methods have bias for one end of transcripts or rely on low-efficiency steps that limit the complexity of the resulting library, making detection of rare transcripts less likely. We tested several commonly used methods of RNA-seq library preparation and found vast differences in the detection of advanced transcriptome features, such as alternatively spliced isoforms and RNA editing sites. By comparing several different protocols available for the Ion Proton sequencer and by utilizing detailed bioinformatics analysis tools, we were able to develop an optimized random primer based RNA-seq technique that is reliable at uncovering rare transcript isoforms and RNA editing features, as well as fusion reads from oncogenic chromosome rearrangements. The combination of optimized libraries and rapid Ion Proton sequencing provides a powerful platform for the transcriptome analysis of research and clinical samples.

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Integrated Cox's model for predicting survival time of glioblastoma multiforme.

PubMed

Ai, Zhibing; Li, Longti; Fu, Rui; Lu, Jing-Min; He, Jing-Dong; Li, Sen

2017-04-01

Glioblastoma multiforme is the most common primary brain tumor and is highly lethal. This study aims to figure out signatures for predicting the survival time of patients with glioblastoma multiforme. Clinical information, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism array data of patients with glioblastoma multiforme were retrieved from The Cancer Genome Atlas. Patients were separated into two groups by using 1 year as a cutoff, and a logistic regression model was used to figure out any variables that can predict whether the patient was able to live longer than 1 year. Furthermore, Cox's model was used to find out features that were correlated with the survival time. Finally, a Cox model integrated the significant clinical variables, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism was built. Although the classification method failed, signatures of clinical features, messenger RNA expression levels, and microRNA expression levels were figured out by using Cox's model. However, no single-nucleotide polymorphisms related to prognosis were found. The selected clinical features were age at initial diagnosis, Karnofsky score, and race, all of which had been suggested to correlate with survival time. Both of the two significant microRNAs, microRNA-221 and microRNA-222, were targeted to p27 Kip1 protein, which implied the important role of p27 Kip1 on the prognosis of glioblastoma multiforme patients. Our results suggested that survival modeling was more suitable than classification to figure out prognostic biomarkers for patients with glioblastoma multiforme. An integrated model containing clinical features, messenger RNA levels, and microRNA expression levels was built, which has the potential to be used in clinics and thus to improve the survival status of glioblastoma multiforme patients.

Clinical Features of Childhood Primary Ciliary Dyskinesia by Genotype and Ultrastructural Phenotype

PubMed Central

Ferkol, Thomas W.; Rosenfeld, Margaret; Lee, Hye-Seung; Dell, Sharon D.; Sagel, Scott D.; Milla, Carlos; Zariwala, Maimoona A.; Pittman, Jessica E.; Shapiro, Adam J.; Carson, Johnny L.; Krischer, Jeffrey P.; Hazucha, Milan J.; Cooper, Matthew L.; Knowles, Michael R.; Leigh, Margaret W.

2015-01-01

Rationale: The relationship between clinical phenotype of childhood primary ciliary dyskinesia (PCD) and ultrastructural defects and genotype is poorly defined. Objectives: To delineate clinical features of childhood PCD and their associations with ultrastructural defects and genotype. Methods: A total of 118 participants younger than 19 years old with PCD were evaluated prospectively at six centers in North America using standardized procedures for diagnostic testing, spirometry, chest computed tomography, respiratory cultures, and clinical phenotyping. Measurements and Main Results: Clinical features included neonatal respiratory distress (82%), chronic cough (99%), and chronic nasal congestion (97%). There were no differences in clinical features or respiratory pathogens in subjects with outer dynein arm (ODA) defects (ODA alone; n = 54) and ODA plus inner dynein arm (IDA) defects (ODA + IDA; n = 18) versus subjects with IDA and central apparatus defects with microtubular disorganization (IDA/CA/MTD; n = 40). Median FEV1 was worse in the IDA/CA/MTD group (72% predicted) versus the combined ODA groups (92% predicted; P = 0.003). Median body mass index was lower in the IDA/CA/MTD group (46th percentile) versus the ODA groups (70th percentile; P = 0.003). For all 118 subjects, median number of lobes with bronchiectasis was three and alveolar consolidation was two. However, the 5- to 11-year-old IDA/CA/MTD group had more lobes of bronchiectasis (median, 5; P = 0.0008) and consolidation (median, 3; P = 0.0001) compared with the ODA groups (median, 3 and 2, respectively). Similar findings were observed when limited to participants with biallelic mutations. Conclusions: Lung disease was heterogeneous across all ultrastructural and genotype groups, but worse in those with IDA/CA/MTD ultrastructural defects, most of whom had biallelic mutations in CCDC39 or CCDC40. PMID:25493340

Clinical Characteristics and Outcome of Patients with Neuroblastoma Presenting Genomic Amplification of Loci Other than MYCN

PubMed Central

Guimier, Anne; Ferrand, Sandrine; Pierron, Gaëlle; Couturier, Jérôme; Janoueix-Lerosey, Isabelle; Combaret, Valérie; Mosseri, Véronique; Thebaud, Estelle; Gambart, Marion; Plantaz, Dominique; Marabelle, Aurélien; Coze, Carole; Rialland, Xavier; Fasola, Sylvie; Lapouble, Eve; Fréneaux, Paul; Peuchmaur, Michel; Michon, Jean; Delattre, Olivier; Schleiermacher, Gudrun

2014-01-01

Background Somatically acquired genomic alterations with MYCN amplification (MNA) are key features of neuroblastoma (NB), the most common extra-cranial malignant tumour of childhood. Little is known about the frequency, clinical characteristics and outcome of NBs harbouring genomic amplification(s) distinct from MYCN. Methods Genomic profiles of 1100 NBs from French centres studied by array-CGH were re-examined specifically to identify regional amplifications. Patients were included if amplifications distinct from the MYCN locus were seen. A subset of NBs treated at Institut Curie and harbouring MNA as determined by array-CGH without other amplification was also studied. Clinical and histology data were retrospectively collected. Results In total, 56 patients were included and categorised into 3 groups. Group 1 (n = 8) presented regional amplification(s) without MNA. Locus 12q13-14 was a recurrent amplified region (4/8 cases). This group was heterogeneous in terms of INSS stages, primary localisations and histology, with atypical clinical features. Group 2 (n = 26) had MNA as well as other regional amplifications. These patients shared clinical features of those of a group of NBs MYCN amplified (Group 3, n = 22). Overall survival for group 1 was better than that of groups 2 and 3 (5 year OS: 87.5%±11% vs 34.9%±7%, log-rank p<0.05). Conclusion NBs harbouring regional amplification(s) without MNA are rare and seem to show atypical features in clinical presentation and genomic profile. Further high resolution genetic explorations are justified in this heterogeneous group, especially when considering these alterations as predictive markers for targeted therapy. PMID:25013904

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

The impact of educational status on the clinical features of major depressive disorder among Chinese women

PubMed Central

Gan, Zhaoyu; Li, Yihan; Xie, Dong; Shao, Chunhong; Yang, Fuzhong; Shen, Yuan; Zhang, Ning; Zhang, Guanghua; Tian, Tian; Yin, Aihua; Chen, Ce; Liu, Jun; Tang, Chunling; Zhang, Zhuoqiu; Liu, Jia; Sang, Wenhua; Wang, Xumei; Liu, Tiebang; Wei, Qinling; Xu, Yong; Sun, Ling; Wang, Sisi; Li, Chang; Hu, Chunmei; Cui, Yanping; Liu, Ying; Li, Ying; Zhao, Xiaochuan; Zhang, Lan; Sun, Lixin; Chen, Yunchun; Zhang, Yueying; Ning, Yuping; Shi, Shenxun; Chen, Yiping; Kendler, Kenneth S.; Flint, Jonathan; Zhang, Jinbei

2012-01-01

Background Years of education are inversely related to the prevalence of major depressive disorder (MDD), but the relationship between the clinical features of MDD and educational status is poorly understood. We investigated this in 1970 Chinese women with recurrent MDD identified in a clinical setting. Methods Clinical and demographic features were obtained from 1970 Han Chinese women with DSM-IV major depression between 30 and 60 years of age across China. Analysis of linear, logistic and multiple logistic regression models were used to determine the association between educational level and clinical features of MDD. Results Subjects with more years of education are more likely to have MDD, with an odds ratio of 1.14 for those with more than ten years. Low educational status is not associated with an increase in the number of episodes, nor with increased rates of co-morbidity with anxiety disorders. Education impacts differentially on the symptoms of depression: lower educational attainment is associated with more biological symptoms and increased suicidal ideation and plans to commit suicide. Limitations Findings may not generalize to males or to other patient populations. Since the threshold for treatment seeking differs as a function of education there may an ascertainment bias in the sample. Conclusions The relationship between symptoms of MDD and educational status in Chinese women is unexpectedly complex. Our findings are inconsistent with the simple hypothesis from European and US reports that low levels of educational attainment increase the risk and severity of MDD. PMID:21824664

Enhancing clinical concept extraction with distributional semantics

PubMed Central

Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

2011-01-01

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. PMID:22085698

A Virtual Mental Health Clinic for University Students: A Qualitative Study of End-User Service Needs and Priorities

PubMed Central

Gulliver, Amelia; Chan, Jade KY; Bennett, Kylie; Griffiths, Kathleen M

2015-01-01

Background Help seeking for mental health problems among university students is low, and Internet-based interventions such as virtual clinics have the potential to provide private, streamlined, and high quality care to this vulnerable group. Objective The objective of this study was to conduct focus groups with university students to obtain input on potential functions and features of a university-specific virtual clinic for mental health. Methods Participants were 19 undergraduate students from an Australian university between 19 and 24 years of age. Focus group discussion was structured by questions that addressed the following topics: (1) the utility and acceptability of a virtual mental health clinic for students, and (2) potential features of a virtual mental health clinic. Results Participants viewed the concept of a virtual clinic for university students favorably, despite expressing concerns about privacy of personal information. Participants expressed a desire to connect with professionals through the virtual clinic, for the clinic to provide information tailored to issues faced by students, and for the clinic to enable peer-to-peer interaction. Conclusions Overall, results of the study suggest the potential for virtual clinics to play a positive role in providing students with access to mental health support. PMID:26543908

Dermoscopic findings in Laugier-Hunziker syndrome.

PubMed

Gencoglan, Gulsum; Gerceker-Turk, Bengu; Kilinc-Karaarslan, Isil; Akalin, Taner; Ozdemir, Fezal

2007-05-01

Laugier-Hunziker syndrome (LHS) is a rare, acquired mucocutaneous hyperpigmentation often associated with longitudinal melanonychia. The clinical behavior of mucocutaneous pigmented lesions ranges from benign to highly malignant. Therefore, in most cases, the clinical diagnosis should be confirmed by further diagnostic methods. Dermoscopy is a noninvasive technique that has been used to make more accurate diagnoses of pigmented skin lesions. Nevertheless, to our knowledge, the dermoscopic features of the pigmented lesions in LHS have not been described previously. Herein, we report a case of LHS together with its dermoscopic features. The clinical examination revealed macular hyperpigmentation on the oral and genital mucosa, conjunctiva, and palmoplantar region together with longitudinal melanonychia. Dermoscopic examination of mucosal lesions on the patient's lips and vulva revealed a parallel pattern. Longitudinal homogeneous pigmentation was observed on the toenails. The pigmented macules on the palms and the sole showed a parallel furrow pattern. A skin biopsy sample taken from the labial lesion was compatible with a diagnosis of mucosal melanosis. By means of this case report, the dermoscopic features of the pigmented lesions in LHS are described for the first time, which facilitates diagnosis with a noninvasive technique. Future reports highlighting the dermoscopic features of this syndrome may simplify the diagnosis of LHS, which is thought to be underdiagnosed.

Recurrent neural networks for breast lesion classification based on DCE-MRIs

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2018-02-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in breast cancer screening, cancer staging, and monitoring response to therapy. Recently, deep learning methods are being rapidly incorporated in image-based breast cancer diagnosis and prognosis. However, most of the current deep learning methods make clinical decisions based on 2-dimentional (2D) or 3D images and are not well suited for temporal image data. In this study, we develop a deep learning methodology that enables integration of clinically valuable temporal components of DCE-MRIs into deep learning-based lesion classification. Our work is performed on a database of 703 DCE-MRI cases for the task of distinguishing benign and malignant lesions, and uses the area under the ROC curve (AUC) as the performance metric in conducting that task. We train a recurrent neural network, specifically a long short-term memory network (LSTM), on sequences of image features extracted from the dynamic MRI sequences. These features are extracted with VGGNet, a convolutional neural network pre-trained on a large dataset of natural images ImageNet. The features are obtained from various levels of the network, to capture low-, mid-, and high-level information about the lesion. Compared to a classification method that takes as input only images at a single time-point (yielding an AUC = 0.81 (se = 0.04)), our LSTM method improves lesion classification with an AUC of 0.85 (se = 0.03).

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

PubMed Central

Maji, Pradipta; Roy, Shaswati

2015-01-01

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961

Direct Microscopy: A Useful Tool to Diagnose Oral Candidiasis in Children and Adolescents.

PubMed

Marty, Mathieu; Bourrat, Emmanuelle; Vaysse, Frédéric; Bonner, Mark; Bailleul-Forestier, Isabelle

2015-12-01

Oral candidiasis is one of the most common opportunistic fungal infections of the oral cavity in human. Among children, this condition represents one of the most frequent affecting the mucosa. Although most diagnoses are made based on clinical signs and features, a microbiological analysis is sometimes necessary. We performed a literature review on the diagnosis of oral candidiasis to identify the techniques most commonly employed in routine clinical practice. A Medline-PubMed search covering the last 10 years was performed. Microbiological techniques were used in cases requiring confirmation of the clinical diagnosis. In such cases, direct microscopy was the method most commonly used for diagnosing candidiasis. Direct microscopy appears as the method of choice for confirming clinical diagnosis and could become a routine chair-side technique.

Characterization of coronary plaque regions in intravascular ultrasound images using a hybrid ensemble classifier.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Shin, Eun Seok; Kim, Sung Min

2018-01-01

The purpose of this study was to propose a hybrid ensemble classifier to characterize coronary plaque regions in intravascular ultrasound (IVUS) images. Pixels were allocated to one of four tissues (fibrous tissue (FT), fibro-fatty tissue (FFT), necrotic core (NC), and dense calcium (DC)) through processes of border segmentation, feature extraction, feature selection, and classification. Grayscale IVUS images and their corresponding virtual histology images were acquired from 11 patients with known or suspected coronary artery disease using 20 MHz catheter. A total of 102 hybrid textural features including first order statistics (FOS), gray level co-occurrence matrix (GLCM), extended gray level run-length matrix (GLRLM), Laws, local binary pattern (LBP), intensity, and discrete wavelet features (DWF) were extracted from IVUS images. To select optimal feature sets, genetic algorithm was implemented. A hybrid ensemble classifier based on histogram and texture information was then used for plaque characterization in this study. The optimal feature set was used as input of this ensemble classifier. After tissue characterization, parameters including sensitivity, specificity, and accuracy were calculated to validate the proposed approach. A ten-fold cross validation approach was used to determine the statistical significance of the proposed method. Our experimental results showed that the proposed method had reliable performance for tissue characterization in IVUS images. The hybrid ensemble classification method outperformed other existing methods by achieving characterization accuracy of 81% for FFT and 75% for NC. In addition, this study showed that Laws features (SSV and SAV) were key indicators for coronary tissue characterization. The proposed method had high clinical applicability for image-based tissue characterization. Copyright © 2017 Elsevier B.V. All rights reserved.

Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method

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

Yan, Shiju; Qian, Wei; Guan, Yubao

2016-06-15

Purpose: This study aims to investigate the potential to improve lung cancer recurrence risk prediction performance for stage I NSCLS patients by integrating oversampling, feature selection, and score fusion techniques and develop an optimal prediction model. Methods: A dataset involving 94 early stage lung cancer patients was retrospectively assembled, which includes CT images, nine clinical and biological (CB) markers, and outcome of 3-yr disease-free survival (DFS) after surgery. Among the 94 patients, 74 remained DFS and 20 had cancer recurrence. Applying a computer-aided detection scheme, tumors were segmented from the CT images and 35 quantitative image (QI) features were initiallymore » computed. Two normalized Gaussian radial basis function network (RBFN) based classifiers were built based on QI features and CB markers separately. To improve prediction performance, the authors applied a synthetic minority oversampling technique (SMOTE) and a BestFirst based feature selection method to optimize the classifiers and also tested fusion methods to combine QI and CB based prediction results. Results: Using a leave-one-case-out cross-validation (K-fold cross-validation) method, the computed areas under a receiver operating characteristic curve (AUCs) were 0.716 ± 0.071 and 0.642 ± 0.061, when using the QI and CB based classifiers, respectively. By fusion of the scores generated by the two classifiers, AUC significantly increased to 0.859 ± 0.052 (p < 0.05) with an overall prediction accuracy of 89.4%. Conclusions: This study demonstrated the feasibility of improving prediction performance by integrating SMOTE, feature selection, and score fusion techniques. Combining QI features and CB markers and performing SMOTE prior to feature selection in classifier training enabled RBFN based classifier to yield improved prediction accuracy.« less

Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning.

PubMed

Ozolek, John A; Tosun, Akif Burak; Wang, Wei; Chen, Cheng; Kolouri, Soheil; Basu, Saurav; Huang, Hu; Rohde, Gustavo K

2014-07-01

Follicular lesions of the thyroid remain significant diagnostic challenges in surgical pathology and cytology. The diagnosis often requires considerable resources and ancillary tests including immunohistochemistry, molecular studies, and expert consultation. Visual analyses of nuclear morphological features, generally speaking, have not been helpful in distinguishing this group of lesions. Here we describe a method for distinguishing between follicular lesions of the thyroid based on nuclear morphology. The method utilizes an optimal transport-based linear embedding for segmented nuclei, together with an adaptation of existing classification methods. We show the method outputs assignments (classification results) which are near perfectly correlated with the clinical diagnosis of several lesion types' lesions utilizing a database of 94 patients in total. Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard. In addition, the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions. Our methods could be incorporated into a tool for pathologists to aid in distinguishing between follicular lesions of the thyroid. In addition, these results could potentially provide nuclear morphological correlates of biological behavior and reduce health care costs by decreasing histotechnician and pathologist time and obviating the need for ancillary testing. Copyright © 2014 Elsevier B.V. All rights reserved.

Extracting BI-RADS Features from Portuguese Clinical Texts

PubMed Central

Nassif, Houssam; Cunha, Filipe; Moreira, Inês C.; Cruz-Correia, Ricardo; Sousa, Eliana; Page, David; Burnside, Elizabeth; Dutra, Inês

2013-01-01

In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BIRADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser’s performance is comparable to the manual method. PMID:23797461

Predict drug permeability to blood–brain-barrier from clinical phenotypes: drug side effects and drug indications

PubMed Central

Gao, Zhen; Chen, Yang; Cai, Xiaoshu; Xu, Rong

2017-01-01

Abstract Motivation: Blood–Brain-Barrier (BBB) is a rigorous permeability barrier for maintaining homeostasis of Central Nervous System (CNS). Determination of compound’s permeability to BBB is prerequisite in CNS drug discovery. Existing computational methods usually predict drug BBB permeability from chemical structure and they generally apply to small compounds passing BBB through passive diffusion. As abundant information on drug side effects and indications has been recorded over time through extensive clinical usage, we aim to explore BBB permeability prediction from a new angle and introduce a novel approach to predict BBB permeability from drug clinical phenotypes (drug side effects and drug indications). This method can apply to both small compounds and macro-molecules penetrating BBB through various mechanisms besides passive diffusion. Results: We composed a training dataset of 213 drugs with known brain and blood steady-state concentrations ratio and extracted their side effects and indications as features. Next, we trained SVM models with polynomial kernel and obtained accuracy of 76.0%, AUC 0.739, and F1 score (macro weighted) 0.760 with Monte Carlo cross validation. The independent test accuracy was 68.3%, AUC 0.692, F1 score 0.676. When both chemical features and clinical phenotypes were available, combining the two types of features achieved significantly better performance than chemical feature based approach (accuracy 85.5% versus 72.9%, AUC 0.854 versus 0.733, F1 score 0.854 versus 0.725; P < e−90). We also conducted de novo prediction and identified 110 drugs in SIDER database having the potential to penetrate BBB, which could serve as start point for CNS drug repositioning research. Availability and Implementation: https://github.com/bioinformatics-gao/CASE-BBB-prediction-Data Contact: rxx@case.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993785

Segmentation of prostate biopsy needles in transrectal ultrasound images

NASA Astrophysics Data System (ADS)

Krefting, Dagmar; Haupt, Barbara; Tolxdorff, Thomas; Kempkensteffen, Carsten; Miller, Kurt

2007-03-01

Prostate cancer is the most common cancer in men. Tissue extraction at different locations (biopsy) is the gold-standard for diagnosis of prostate cancer. These biopsies are commonly guided by transrectal ultrasound imaging (TRUS). Exact location of the extracted tissue within the gland is desired for more specific diagnosis and provides better therapy planning. While the orientation and the position of the needle within clinical TRUS image are limited, the appearing length and visibility of the needle varies strongly. Marker lines are present and tissue inhomogeneities and deflection artefacts may appear. Simple intensity, gradient oder edge-detecting based segmentation methods fail. Therefore a multivariate statistical classificator is implemented. The independent feature model is built by supervised learning using a set of manually segmented needles. The feature space is spanned by common binary object features as size and eccentricity as well as imaging-system dependent features like distance and orientation relative to the marker line. The object extraction is done by multi-step binarization of the region of interest. The ROI is automatically determined at the beginning of the segmentation and marker lines are removed from the images. The segmentation itself is realized by scale-invariant classification using maximum likelihood estimation and Mahalanobis distance as discriminator. The technique presented here could be successfully applied in 94% of 1835 TRUS images from 30 tissue extractions. It provides a robust method for biopsy needle localization in clinical prostate biopsy TRUS images.

Segmentation methods for breast vasculature in dual-energy contrast-enhanced digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Lau, Kristen C.; Lee, Hyo Min; Singh, Tanushriya; Maidment, Andrew D. A.

2015-03-01

Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

PubMed

Yu, Sheng; Liao, Katherine P; Shaw, Stanley Y; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Cai, Tianxi

2015-09-01

Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Essential features influencing collaboration in team-based non-specific back pain rehabilitation: Findings from a mixed methods study

PubMed Central

Hellman, Therese; Jensen, Irene; Bergström, Gunnar; Brämberg, Elisabeth Björk

2016-01-01

ABSTRACT The aim of the study presented in this article was to explore how professionals, without guidelines for implementing interprofessional teamwork, experience the collaboration within team-based rehabilitation for people with back pain and how this collaboration influences their clinical practice. This study employed a mixed methods design. A questionnaire was answered by 383 participants and 17 participants were interviewed. The interviews were analysed using content analysis. The quantitative results showed that the participants were satisfied with their team-based collaboration. Thirty percent reported that staff changes in the past year had influenced their clinical practice, of which 57% reported that these changes had had negative consequences. The qualitative findings revealed that essential features for an effective collaboration were shared basic values and supporting each other. Furthermore, aspects such as having enough time for reflection, staff continuity, and a shared view of the team members’ roles were identified as aspects which influenced the clinical practice. Important clinical implications for nurturing and developing a collaboration in team-based rehabilitation are to create shared basic values and a unified view of all team members’ roles and their contributions to the team. These aspects need to be emphasised on an ongoing basis and not only when the team is formed. PMID:27152534

Image segmentation using joint spatial-intensity-shape features: application to CT lung nodule segmentation

NASA Astrophysics Data System (ADS)

Ye, Xujiong; Siddique, Musib; Douiri, Abdel; Beddoe, Gareth; Slabaugh, Greg

2009-02-01

Automatic segmentation of medical images is a challenging problem due to the complexity and variability of human anatomy, poor contrast of the object being segmented, and noise resulting from the image acquisition process. This paper presents a novel feature-guided method for the segmentation of 3D medical lesions. The proposed algorithm combines 1) a volumetric shape feature (shape index) based on high-order partial derivatives; 2) mean shift clustering in a joint spatial-intensity-shape (JSIS) feature space; and 3) a modified expectation-maximization (MEM) algorithm on the mean shift mode map to merge the neighboring regions (modes). In such a scenario, the volumetric shape feature is integrated into the process of the segmentation algorithm. The joint spatial-intensity-shape features provide rich information for the segmentation of the anatomic structures or lesions (tumors). The proposed method has been evaluated on a clinical dataset of thoracic CT scans that contains 68 nodules. A volume overlap ratio between each segmented nodule and the ground truth annotation is calculated. Using the proposed method, the mean overlap ratio over all the nodules is 0.80. On visual inspection and using a quantitative evaluation, the experimental results demonstrate the potential of the proposed method. It can properly segment a variety of nodules including juxta-vascular and juxta-pleural nodules, which are challenging for conventional methods due to the high similarity of intensities between the nodules and their adjacent tissues. This approach could also be applied to lesion segmentation in other anatomies, such as polyps in the colon.

A Registration Method Based on Contour Point Cloud for 3D Whole-Body PET and CT Images

PubMed Central

Yang, Qiyao; Wang, Zhiguo; Zhang, Guoxu

2017-01-01

The PET and CT fusion image, combining the anatomical and functional information, has important clinical meaning. An effective registration of PET and CT images is the basis of image fusion. This paper presents a multithread registration method based on contour point cloud for 3D whole-body PET and CT images. Firstly, a geometric feature-based segmentation (GFS) method and a dynamic threshold denoising (DTD) method are creatively proposed to preprocess CT and PET images, respectively. Next, a new automated trunk slices extraction method is presented for extracting feature point clouds. Finally, the multithread Iterative Closet Point is adopted to drive an affine transform. We compare our method with a multiresolution registration method based on Mattes Mutual Information on 13 pairs (246~286 slices per pair) of 3D whole-body PET and CT data. Experimental results demonstrate the registration effectiveness of our method with lower negative normalization correlation (NC = −0.933) on feature images and less Euclidean distance error (ED = 2.826) on landmark points, outperforming the source data (NC = −0.496, ED = 25.847) and the compared method (NC = −0.614, ED = 16.085). Moreover, our method is about ten times faster than the compared one. PMID:28316979

A comparative study of new and current methods for dental micro-CT image denoising

PubMed Central

Lashgari, Mojtaba; Qin, Jie; Swain, Michael

2016-01-01

Objectives: The aim of the current study was to evaluate the application of two advanced noise-reduction algorithms for dental micro-CT images and to implement a comparative analysis of the performance of new and current denoising algorithms. Methods: Denoising was performed using gaussian and median filters as the current filtering approaches and the block-matching and three-dimensional (BM3D) method and total variation method as the proposed new filtering techniques. The performance of the denoising methods was evaluated quantitatively using contrast-to-noise ratio (CNR), edge preserving index (EPI) and blurring indexes, as well as qualitatively using the double-stimulus continuous quality scale procedure. Results: The BM3D method had the best performance with regard to preservation of fine textural features (CNREdge), non-blurring of the whole image (blurring index), the clinical visual score in images with very fine features and the overall visual score for all types of images. On the other hand, the total variation method provided the best results with regard to smoothing of images in texture-free areas (CNRTex-free) and in preserving the edges and borders of image features (EPI). Conclusions: The BM3D method is the most reliable technique for denoising dental micro-CT images with very fine textural details, such as shallow enamel lesions, in which the preservation of the texture and fine features is of the greatest importance. On the other hand, the total variation method is the technique of choice for denoising images without very fine textural details in which the clinician or researcher is interested mainly in anatomical features and structural measurements. PMID:26764583

Lumbar spinal stenosis CAD from clinical MRM and MRI based on inter- and intra-context features with a two-level classifier

NASA Astrophysics Data System (ADS)

Koh, Jaehan; Alomari, Raja S.; Chaudhary, Vipin; Dhillon, Gurmeet

2011-03-01

An imaging test has an important role in the diagnosis of lumbar abnormalities since it allows to examine the internal structure of soft tissues and bony elements without the need of an unnecessary surgery and recovery time. For the past decade, among various imaging modalities, magnetic resonance imaging (MRI) has taken the significant part of the clinical evaluation of the lumbar spine. This is mainly due to technological advancements that lead to the improvement of imaging devices in spatial resolution, contrast resolution, and multi-planar capabilities. In addition, noninvasive nature of MRI makes it easy to diagnose many common causes of low back pain such as disc herniation, spinal stenosis, and degenerative disc diseases. In this paper, we propose a method to diagnose lumbar spinal stenosis (LSS), a narrowing of the spinal canal, from magnetic resonance myelography (MRM) images. Our method segments the thecal sac in the preprocessing stage, generates the features based on inter- and intra-context information, and diagnoses lumbar disc stenosis. Experiments with 55 subjects show that our method achieves 91.3% diagnostic accuracy. In the future, we plan to test our method on more subjects.

Defining competency-based evaluation objectives in family medicine

PubMed Central

Lawrence, Kathrine; Allen, Tim; Brailovsky, Carlos; Crichton, Tom; Bethune, Cheri; Donoff, Michel; Laughlin, Tom; Wetmore, Stephen; Carpentier, Marie-Pierre; Visser, Shaun

2011-01-01

Abstract Objective To develop key features for priority topics previously identified by the College of Family Physicians of Canada that, together with skill dimensions and phases of the clinical encounter, broadly describe competence in family medicine. Design Modified nominal group methodology, which was used to develop key features for each priority topic through an iterative process. Setting The College of Family Physicians of Canada. Participants An expert group of 7 family physicians and 1 educational consultant, all of whom had experience in assessing competence in family medicine. Group members represented the Canadian family medicine context with respect to region, sex, language, community type, and experience. Methods The group used a modified Delphi process to derive a detailed operational definition of competence, using multiple iterations until consensus was achieved for the items under discussion. The group met 3 to 4 times a year from 2000 to 2007. Main findings The group analyzed 99 topics and generated 773 key features. There were 2 to 20 (average 7.8) key features per topic; 63% of the key features focused on the diagnostic phase of the clinical encounter. Conclusion This project expands previous descriptions of the process of generating key features for assessment, and removes this process from the context of written examinations. A key-features analysis of topics focuses on higher-order cognitive processes of clinical competence. The project did not define all the skill dimensions of competence to the same degree, but it clearly identified those requiring further definition. This work generates part of a discipline-specific, competency-based definition of family medicine for assessment purposes. It limits the domain for assessment purposes, which is an advantage for the teaching and assessment of learners. A validation study on the content of this work would ensure that it truly reflects competence in family medicine. PMID:21998245

Which ante mortem clinical features predict progressive supranuclear palsy pathology?

PubMed

Respondek, Gesine; Kurz, Carolin; Arzberger, Thomas; Compta, Yaroslau; Englund, Elisabet; Ferguson, Leslie W; Gelpi, Ellen; Giese, Armin; Irwin, David J; Meissner, Wassilios G; Nilsson, Christer; Pantelyat, Alexander; Rajput, Alex; van Swieten, John C; Troakes, Claire; Josephs, Keith A; Lang, Anthony E; Mollenhauer, Brit; Müller, Ulrich; Whitwell, Jennifer L; Antonini, Angelo; Bhatia, Kailash P; Bordelon, Yvette; Corvol, Jean-Christophe; Colosimo, Carlo; Dodel, Richard; Grossman, Murray; Kassubek, Jan; Krismer, Florian; Levin, Johannes; Lorenzl, Stefan; Morris, Huw; Nestor, Peter; Oertel, Wolfgang H; Rabinovici, Gil D; Rowe, James B; van Eimeren, Thilo; Wenning, Gregor K; Boxer, Adam; Golbe, Lawrence I; Litvan, Irene; Stamelou, Maria; Höglinger, Günter U

2017-07-01

Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 International Parkinson and Movement Disorder Society. © 2017 International Parkinson and Movement Disorder Society.

Automatically Detecting Likely Edits in Clinical Notes Created Using Automatic Speech Recognition

PubMed Central

Lybarger, Kevin; Ostendorf, Mari; Yetisgen, Meliha

2017-01-01

The use of automatic speech recognition (ASR) to create clinical notes has the potential to reduce costs associated with note creation for electronic medical records, but at current system accuracy levels, post-editing by practitioners is needed to ensure note quality. Aiming to reduce the time required to edit ASR transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing. We create detection models using logistic regression and conditional random field models, exploring a variety of text-based features that consider the structure of clinical notes and exploit the medical context. Different medical text resources are used to improve feature extraction. Experimental results on a large corpus of practitioner-edited clinical notes show that 67% of sentence-level edits and 45% of word-level edits can be detected with a false detection rate of 15%. PMID:29854187

Primary mediastinal large B-cell lymphoma: Clinical features, prognostic factors and survival with RCHOP in Arab patients in the PET scan era

PubMed Central

Al Shemmari, Salem; Sankaranarayanan, Sreedharan P.; Krishnan, Yamini

2014-01-01

Objective: PMBCL is a distinct type of nonhodgkins lymphoma with specific clinicopathological features. To clarify clinical features, treatment alternatives and outcomes, we evaluated 28 Arab patients treated with chemotherapy or radiotherapy between 2006 and 2011. Patients and Methods: PMBCL lymphoma patients identified according to WHO classification and treated at KCCC between 2006 and 2011 were included in this study. Demographic and clinical data are presented as means or medians. Overall survival was estimated using the Kaplan-Meier method. Survival rates were compared using the log-rank test. A P < 0.05 was considered significant. Results: The median age of the patients was 31 years and the male to female ratio was 2:1. Majority of the patients (75%) presented with stage I/II disease. Most had features of local extension like pleural effusion (18%) and SVCO (39%). Only 11% of the patients had bone marrow involvement at presentation. 96% of the patients required biopsy from the mediastinal mass either by image guided core biopsy (75%) or by surgical biopsy. Most patients were treated by RCHOP and involved field radiotherapy. Patients with positive PET scan after RCHOP chemotherapy received salvage chemotherapy and BEAM autologous marrow transplant. The five year OS for the entire group was 85% while the PFS was 73%. Patients who had PET scan for response evaluation had better OS [P = 0.013] and PFS [P = 0.039] when compared with those patients who received only radiotherapy based on CT scan evaluation. Conclusion: PMBCL is a specific lymphoma entity seen in the young with good survival. The role of PET scan for response evaluation and the type of consolidation therapy needs to be further clarified PMID:25125808

Comparison of Clinical Features and Outcomes in Patients with Extraskeletal Versus Skeletal Localized Ewing Sarcoma: A Report from the Children’s Oncology Group

PubMed Central

Cash, Thomas; McIlvaine, Elizabeth; Krailo, Mark D.; Lessnick, Stephen L.; Lawlor, Elizabeth R.; Laack, Nadia; Sorger, Joel; Marina, Neyssa; Grier, Holcombe E.; Granowetter, Linda; Womer, Richard B.; DuBois, Steven G.

2016-01-01

BACKGROUND The prognostic significance of having extraskeletal vs. skeletal Ewing sarcoma in the setting of modern chemotherapy protocols is unknown. The purpose of this study was to compare the clinical characteristics, biologic features, and outcomes for patients with extraskeletal and skeletal Ewing sarcoma. METHODS Patients had localized Ewing sarcoma (ES) and were treated on two consecutive protocols using 5-drug chemotherapy (INT-0154 and AEWS0031). Patients were analyzed based on having an extraskeletal (n=213) or skeletal (n=826) site of tumor origin. Event-free survival (EFS) was estimated using the Kaplan-Meier method, compared using the log-rank test, and modeled using Cox multivariate regression. RESULTS Patients with extraskeletal Ewing Sarcoma (EES) were more likely to have axial tumors (72% vs. 55%; P < 0.001), less likely to have tumors > 8 cm (9% vs. 17%; P < 0.01), and less likely to be white (81% vs. 87%; P < 0.001) compared to patients with skeletal ES. There was no difference in key genomic features (type of EWSR1 translocation, TP53 mutation, CDKN2A mutation/loss) between groups. After controlling for age, race, and primary site, EES was associated with superior EFS [hazard ratio = 0.69; 95% CI: 0.50–0.95; P = 0.02]. Among patients with EES, age ≥ 18 years, non-white race, and elevated baseline erythrocyte sedimentation rate (ESR) were independently associated with inferior EFS. CONCLUSION Clinical characteristics, but not key tumor genomic features, differ between EES and skeletal ES. Extraskeletal origin is a favorable prognostic factor, independent of age, race, and primary site. PMID:27297500

Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2016-04-01

Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field. Copyright © 2016 Elsevier Inc. All rights reserved.

Recommendations for the Design and Analysis of Treatment Trials for Alcohol Use Disorders

PubMed Central

Witkiewitz, Katie; Finney, John W.; Harris, Alex H.S; Kivlahan, Daniel R.; Kranzler, Henry R.

2015-01-01

Background Over the past 60 years the view that “alcoholism” is a disease for which the only acceptable goal of treatment is abstinence has given way to the recognition that alcohol use disorders (AUDs) occur on a continuum of severity, for which a variety of treatment options are appropriate. However, because the available treatments for AUDs are not effective for everyone, more research is needed to develop novel and more efficacious treatments to address the range of AUD severity in diverse populations. Here we offer recommendations for the design and analysis of alcohol treatment trials, with a specific focus on the careful conduct of randomized clinical trials of medications and non-pharmacological interventions for AUDs. Methods Narrative review of the quality of published clinical trials and recommendations for the optimal design and analysis of treatment trials for AUDs. Results Despite considerable improvements in the design of alcohol clinical trials over the past two decades, many studies of AUD treatments have used faulty design features and statistical methods that are known to produce biased estimates of treatment efficacy. Conclusions The published statistical and methodological literatures provide clear guidance on methods to improve clinical trial design and analysis. Consistent use of state-of-the-art design features and analytic approaches will enhance the internal and external validity of treatment trials for AUDs across the spectrum of severity. The ultimate result of this attention to methodological rigor is that better treatment options will be identified for patients with an AUD. PMID:26250333

Clinical features and endocrine profile of Laron syndrome in Indian children

PubMed Central

Phanse-Gupte, Supriya R.; Khadilkar, Vaman V.; Khadilkar, Anuradha V.

2014-01-01

Introduction: Patients with growth hormone (GH) insensitivity (also known as Laron syndome) have been reported from the Mediterranean region and Southern Eucador, with few case reports from India. We present here the clinical and endocrine profile of 9 children with Laron syndrome from India. Material and Methods: Nine children diagnosed with Laron syndrome based on clinical features of GH deficiency and biochemical profile suggestive of GH resistance were studied over a period of 5 years from January 2008 to January 2013. Results and Discussion: Age of presentation was between 2.5-11.5 years. All children were considerably short on contemporary Indian charts with mean (SD) height Z score -5.2 (1.6). However, they were within ± 2 SD on Laron charts. No child was overweight [mean (SD) BMI Z score 0.92 (1.1)]. All children had characteristic facies of GH deficiency with an added feature of prominent eyes. Three boys had micropenis and 1 had unilateral undescended testis. All children had low IGF-1 (<5 percentile) and IGFP-3 (<0.1 percentile) with high basal and stimulated GH [Basal GH mean (SD) = 13.78 (12.75) ng/ml, 1-h stimulated GH mean (SD) = 46.29 (25.68) ng/ml]. All children showed poor response to IGF generation test. Conclusion: Laron syndrome should be suspected in children with clinical features of GH deficiency, high GH levels and low IGF-1/IGFBP-3. These children are in a state of GH resistance and need IGF-1 therapy. PMID:25364685

Clinical Features and Prognostic Factors of Hodgkin’s Lymphoma: A Single Center Experience

PubMed Central

Kılıçkap, Saadettin; Barışta, İbrahim; Ülger, Şükran; Çelik, İsmail; Selek, Uğur; Yıldız, Ferah; Kars, Ayşe; Özışık, Yavuz; Tekuzman, Gülten

2013-01-01

Background: Hodgkin’s lymphoma (HL) is a B cell lymphoma characterized by the presence of Reed-Sternberg cells. HL comprises 1% of all cancer cases and 14% of all lymphoma cases. Aims: We designed a retrospective study to investigate the clinical features and prognostic factors of HL patients diagnosed at an experienced oncology centre. Study Design: Retrospective study. Methods: Demographic characteristics, histopathological and clinical features, treatment modalities and response to treatment were obtained from hospital records. Dates of initial diagnosis, remission and relapse, last visit and death were recorded for survival analyses. Results: We analysed data of 391 HL patients (61% male, 39% female; mean age 35.7±15.1 years). The most common classical HL histological subtype was nodular sclerosing HL (NSHL) (42.7%). The most common stage was II 50.4%. The most common chemotherapy regimen was doxorubicin, bleomycin, vinblastine and dacarbazine (ABVD) (70.6%). Five and 10-year survival rates were 90% and 84%, respectively. Early-stage patients with good prognostic factors had better overall and relapse-free survival rates. The presence of “B” symptoms, albumin level, Eastern Cooperative Oncology Group (ECOG) performance score, and LDH were prognostic factors that affect the survival in both univariate and multivariate analyses. Conclusion: This is the first study that demonstrates the demographic, clinical and prognostic features of HL patients in Turkey, and provides a general picture of the HL patients in our country. PMID:25207097

Rectal bleeding, fecal incontinence, and high stool frequency after conformal radiotherapy for prostate cancer: Normal tissue complication probability modeling

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

Peeters, Stephanie; Hoogeman, Mischa S.; Heemsbergen, Wilma D.

2006-09-01

Purpose: To analyze whether inclusion of predisposing clinical features in the Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) model improves the estimation of late gastrointestinal toxicity. Methods and Materials: This study includes 468 prostate cancer patients participating in a randomized trial comparing 68 with 78 Gy. We fitted the probability of developing late toxicity within 3 years (rectal bleeding, high stool frequency, and fecal incontinence) with the original, and a modified LKB model, in which a clinical feature (e.g., history of abdominal surgery) was taken into account by fitting subset specific TD50s. The ratio of these TD50s is the dose-modifyingmore » factor for that clinical feature. Dose distributions of anorectal (bleeding and frequency) and anal wall (fecal incontinence) were used. Results: The modified LKB model gave significantly better fits than the original LKB model. Patients with a history of abdominal surgery had a lower tolerance to radiation than did patients without previous surgery, with a dose-modifying factor of 1.1 for bleeding and of 2.5 for fecal incontinence. The dose-response curve for bleeding was approximately two times steeper than that for frequency and three times steeper than that for fecal incontinence. Conclusions: Inclusion of predisposing clinical features significantly improved the estimation of the NTCP. For patients with a history of abdominal surgery, more severe dose constraints should therefore be used during treatment plan optimization.« less

Male Inmate Profiles and Their Biological Correlates

PubMed Central

Horn, Mathilde; Potvin, Stephane; Allaire, Jean-François; Côté, Gilles; Gobbi, Gabriella; Benkirane, Karim; Vachon, Jeanne; Dumais, Alexandre

2014-01-01

Objective: Borderline and antisocial personality disorders (PDs) share common clinical features (impulsivity, aggressiveness, substance use disorders [SUDs], and suicidal behaviours) that are greatly overrepresented in prison populations. These disorders have been associated biologically with testosterone and cortisol levels. However, the associations are ambiguous and the subject of controversy, perhaps because these heterogeneous disorders have been addressed as unitary constructs. A consideration of profiles of people, rather than of exclusive diagnoses, might yield clearer relationships. Methods: In our study, multiple correspondence analysis and cluster analysis were employed to identify subgroups among 545 newly convicted inmates. The groups were then compared in terms of clinical features and biological markers, including levels of cortisol, testosterone, estradiol, progesterone, and sulfoconjugated dehydroepiandrosterone (DHEA-S). Results: Four clusters with differing psychiatric, criminal, and biological profiles emerged. Clinically, one group had intermediate scores for each of the tested clinical features. Another group comprised people with little comorbidity. Two others displayed severe impulsivity, PD, and SUD. Biologically, cortisol levels were lowest in the last 2 groups and highest in the group with less comorbidity. In keeping with previous findings reported in the literature, testosterone was higher in a younger population with severe psychiatric symptoms. However, some apparently comparable behavioural outcomes were found to be related to distinct biological profiles. No differences were observed for estradiol, progesterone, or DHEA-S levels. Conclusions: The results not only confirm the importance of biological markers in the study of personality features but also demonstrate the need to consider the role of comorbidities and steroid coregulation. PMID:25161069

A clinically viable capsule endoscopy video analysis platform for automatic bleeding detection

NASA Astrophysics Data System (ADS)

Yi, Steven; Jiao, Heng; Xie, Jean; Mui, Peter; Leighton, Jonathan A.; Pasha, Shabana; Rentz, Lauri; Abedi, Mahmood

2013-02-01

In this paper, we present a novel and clinically valuable software platform for automatic bleeding detection on gastrointestinal (GI) tract from Capsule Endoscopy (CE) videos. Typical CE videos for GI tract run about 8 hours and are manually reviewed by physicians to locate diseases such as bleedings and polyps. As a result, the process is time consuming and is prone to disease miss-finding. While researchers have made efforts to automate this process, however, no clinically acceptable software is available on the marketplace today. Working with our collaborators, we have developed a clinically viable software platform called GISentinel for fully automated GI tract bleeding detection and classification. Major functional modules of the SW include: the innovative graph based NCut segmentation algorithm, the unique feature selection and validation method (e.g. illumination invariant features, color independent features, and symmetrical texture features), and the cascade SVM classification for handling various GI tract scenes (e.g. normal tissue, food particles, bubbles, fluid, and specular reflection). Initial evaluation results on the SW have shown zero bleeding instance miss-finding rate and 4.03% false alarm rate. This work is part of our innovative 2D/3D based GI tract disease detection software platform. While the overall SW framework is designed for intelligent finding and classification of major GI tract diseases such as bleeding, ulcer, and polyp from the CE videos, this paper will focus on the automatic bleeding detection functional module.

Clinical epidemiology.

PubMed

Martin, S W; Bonnett, B

1987-06-01

Rational clinical practice requires deductive particularization of diagnostic findings, prognoses, and therapeutic responses from groups of animals (herds) to the individual animal (herd) under consideration This process utilizes concepts, skills, and methods of epidemiology, as they relate to the study of the distribution and determinants of health and disease in populations, and casts them in a clinical perspective.We briefly outline diagnostic strategies and introduce a measure of agreement, called kappa, between clinical diagnoses. This statistic is useful not only as a measure of diagnostic accuracy, but also as a means of quantifying and understanding disagreement between diagnosticians. It is disconcerting to many, clinicians included, that given a general deficit of data on sensitivity and specificity, the level of agreement between many clinical diagnoses is only moderate at best with kappa values of 0.3 to 0.6.Sensitivity, specificity, pretest odds, and posttest probability of disease are defined and related to the interpretation of clinical findings and ancillary diagnostic test results. An understanding of these features and how they relate to ruling-in or ruling-out a diagnosis, or minimizzing diagnostic errors will greatly enhance the diagnostic accuracy of the practitioner, and reduce the frequency of clinical disagreement. The approach of running multiple tests on every patient is not only wasteful and expensive, it is unlikely to improve the ability of the clinician to establish the correct diagnosis.We conclude with a discussion of how to decide on the best therapy, a discussion which centers on, and outlines the key features of, the well designed clinical trial. Like a diagnosis, the results from a clinical trial may not always be definitive, nonetheless it is the best available method of gleaning information about treatment efficacy.

Clinical Features and Associated Likelihood of Primary Ciliary Dyskinesia in Children and Adolescents.

PubMed

Leigh, Margaret W; Ferkol, Thomas W; Davis, Stephanie D; Lee, Hye-Seung; Rosenfeld, Margaret; Dell, Sharon D; Sagel, Scott D; Milla, Carlos; Olivier, Kenneth N; Sullivan, Kelli M; Zariwala, Maimoona A; Pittman, Jessica E; Shapiro, Adam J; Carson, Johnny L; Krischer, Jeffrey; Hazucha, Milan J; Knowles, Michael R

2016-08-01

Primary ciliary dyskinesia (PCD), a genetically heterogeneous, recessive disorder of motile cilia, is associated with distinct clinical features. Diagnostic tests, including ultrastructural analysis of cilia, nasal nitric oxide measurements, and molecular testing for mutations in PCD genes, have inherent limitations. To define a statistically valid combination of systematically defined clinical features that strongly associates with PCD in children and adolescents. Investigators at seven North American sites in the Genetic Disorders of Mucociliary Clearance Consortium prospectively and systematically assessed individuals (aged 0-18 yr) referred due to high suspicion for PCD. The investigators defined specific clinical questions for the clinical report form based on expert opinion. Diagnostic testing was performed using standardized protocols and included nasal nitric oxide measurement, ciliary biopsy for ultrastructural analysis of cilia, and molecular genetic testing for PCD-associated genes. Final diagnoses were assigned as "definite PCD" (hallmark ultrastructural defects and/or two mutations in a PCD-associated gene), "probable/possible PCD" (no ultrastructural defect or genetic diagnosis, but compatible clinical features and nasal nitric oxide level in PCD range), and "other diagnosis or undefined." Criteria were developed to define early childhood clinical features on the basis of responses to multiple specific queries. Each defined feature was tested by logistic regression. Sensitivity and specificity analyses were conducted to define the most robust set of clinical features associated with PCD. From 534 participants 18 years of age and younger, 205 were identified as having "definite PCD" (including 164 with two mutations in a PCD-associated gene), 187 were categorized as "other diagnosis or undefined," and 142 were defined as having "probable/possible PCD." Participants with "definite PCD" were compared with the "other diagnosis or undefined" group. Four criteria-defined clinical features were statistically predictive of PCD: laterality defect; unexplained neonatal respiratory distress; early-onset, year-round nasal congestion; and early-onset, year-round wet cough (adjusted odds ratios of 7.7, 6.6, 3.4, and 3.1, respectively). The sensitivity and specificity based on the number of criteria-defined clinical features were four features, 0.21 and 0.99, respectively; three features, 0.50 and 0.96, respectively; and two features, 0.80 and 0.72, respectively. Systematically defined early clinical features could help identify children, including infants, likely to have PCD. Clinical trial registered with ClinicalTrials.gov (NCT00323167).

A Tale of Two Methods: Chart and Interview Methods for Identifying Delirium

PubMed Central

Saczynski, Jane S.; Kosar, Cyrus M.; Xu, Guoquan; Puelle, Margaret R.; Schmitt, Eva; Jones, Richard N.; Marcantonio, Edward R.; Wong, Bonnie; Isaza, Ilean; Inouye, Sharon K.

2014-01-01

Background Interview and chart-based methods for identifying delirium have been validated. However, relative strengths and limitations of each method have not been described, nor has a combined approach (using both interviews and chart), been systematically examined. Objectives To compare chart and interview-based methods for identification of delirium. Design, Setting and Participants Participants were 300 patients aged 70+ undergoing major elective surgery (majority were orthopedic surgery) interviewed daily during hospitalization for delirium using the Confusion Assessment Method (CAM; interview-based method) and whose medical charts were reviewed for delirium using a validated chart-review method (chart-based method). We examined rate of agreement on the two methods and patient characteristics of those identified using each approach. Predictive validity for clinical outcomes (length of stay, postoperative complications, discharge disposition) was compared. In the absence of a gold-standard, predictive value could not be calculated. Results The cumulative incidence of delirium was 23% (n= 68) by the interview-based method, 12% (n=35) by the chart-based method and 27% (n=82) by the combined approach. Overall agreement was 80%; kappa was 0.30. The methods differed in detection of psychomotor features and time of onset. The chart-based method missed delirium in CAM-identified patients laacking features of psychomotor agitation or inappropriate behavior. The CAM-based method missed chart-identified cases occurring during the night shift. The combined method had high predictive validity for all clinical outcomes. Conclusions Interview and chart-based methods have specific strengths for identification of delirium. A combined approach captures the largest number and the broadest range of delirium cases. PMID:24512042

[The specific features of the clinical course of acute suppurative otitis media of viral and mixed viral-bacterial etiology in the children of the preschool age].

PubMed

Radtsig, E Yu; Bugaichuk, O V

The objective of the present study was to elucidate the spectrum of the pathogenic agents responsible for the development of acute suppurative otitis media in the children of the preschool age and to reveal the specific clinical features of this disease depending on its etiological factors. The study involved 138 patients (186 ears) of either sex at the age from 1 year to 84 months who presented with acute suppurative otitis media. The following methods were employed for the purpose of the study: analysis of the patients' complaints and the past medical histories, examination of the ENT organs, microbiological (bacteriological and virological) studies of secretion from the tympanic cavity, diagnostic endoscopy of the nasal cavity and nasopharynx, laboratory investigations. The study allowed to reveal the characteristic clinical manifestations of the pathology of interest depending on its etiology.

Neuron-specific enolase levels in drug-naïve young adults with major depressive disorder.

PubMed

Wiener, Carolina David; Molina, Mariane Lopez; Passos, Miguel; Moreira, Fernanda Pedrotti; Bittencourt, Guilherme; de Mattos Souza, Luciano Dias; da Silva, Ricardo Azevedo; Jansen, Karen; Oses, Jean Pierre

2016-05-04

The aim of this study is to assess neuron-specific enolase (NSE) levels and clinical features in subjects with major depressive disorder (MDD). This is a cross-sectional study with drug-naïve young adults with MDD (aged 18-29 years). Serum levels of NSE were assessed using the electrochemiluminescence method. MDD diagnosis, suicidal ideation, and time of disease were assessed using the Structured Clinical Interview for DSM-IV (SCID). The Hamilton Depression Rating Scale (HDRS) and Hamilton Anxiety Rating Scale (HARS) were used to assess depressive and anxiety symptoms. No relationship was observed between NSE levels and severity of depressive and anxiety symptoms, time of disease, and suicidal ideation. These results suggest that NSE serum levels were not associated with clinical features of MDD among drug-naïve young adults. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

PubMed Central

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.

2015-01-01

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. PMID:25563255

Miniscrews for orthodontic anchorage: a review of available systems.

PubMed

Alkadhimi, Aslam; Al-Awadhi, Ebrahim A

2018-03-15

In recent years, extensive research has introduced novel ways of reinforcing orthodontic anchorage using a variety of devices temporarily anchored in bone (miniscrews). Currently, there are numerous manufacturers with different miniscrew designs on the market. The aim of this paper is to discuss the key design features of different miniscrew systems on the market. Furthermore, to present clinical selection criteria of miniscrews in different settings taking into account the determinant factors. Review of the literature was carried out using the following search methods: MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL). The search was focused on studies published until January 2018. We studied each individual miniscrew from all the identified manufacturers in details. All the features were then summarised and presented as a clinical guideline for the selection of miniscrews. In this article, we reviewed the development of miniscrews and outlined the general design features of miniscrews as well as specific design features of the current miniscrews in the market. Extensive research of the current products was carried out to help clinicians better understand the difference between the various designs of miniscrews that can be used.

Tissue Feature-Based and Segmented Deformable Image Registration for Improved Modeling of the Shear Movement of the Lungs

PubMed Central

Xie, Yaoqin; Chao, Ming; Xing, Lei

2009-01-01

Purpose To report a tissue feature-based image registration strategy with explicit inclusion of the differential motions of thoracic structures. Methods and Materials The proposed technique started with auto-identification of a number of corresponding points with distinct tissue features. The tissue feature points were found by using the scale-invariant feature transform (SIFT) method. The control point pairs were then sorted into different “colors” according to the organs they reside and used to model the involved organs individually. A thin-plate spline (TPS) method was used to register a structure characterized by the control points with a given “color”. The proposed technique was applied to study a digital phantom case, three lung and three liver cancer patients. Results For the phantom case, a comparison with the conventional TPS method showed that the registration accuracy was markedly improved when the differential motions of the lung and chest wall were taken into account. On average, the registration error and the standard deviation (SD) of the 15 points against the known ground truth are reduced from 3.0 mm to 0.5 mm and from 1.5 mm to 0.2 mm, respectively, when the new method was used. Similar level of improvement was achieved for the clinical cases. Conclusions The segmented deformable approach provides a natural and logical solution to model the discontinuous organ motions and greatly improves the accuracy and robustness of deformable registration. PMID:19545792

A Methodology for Validating Safety Heuristics Using Clinical Simulations: Identifying and Preventing Possible Technology-Induced Errors Related to Using Health Information Systems

PubMed Central

Borycki, Elizabeth; Kushniruk, Andre; Carvalho, Christopher

2013-01-01

Internationally, health information systems (HIS) safety has emerged as a significant concern for governments. Recently, research has emerged that has documented the ability of HIS to be implicated in the harm and death of patients. Researchers have attempted to develop methods that can be used to prevent or reduce technology-induced errors. Some researchers are developing methods that can be employed prior to systems release. These methods include the development of safety heuristics and clinical simulations. In this paper, we outline our methodology for developing safety heuristics specific to identifying the features or functions of a HIS user interface design that may lead to technology-induced errors. We follow this with a description of a methodological approach to validate these heuristics using clinical simulations. PMID:23606902

Metabolomic biosignature differentiates melancholic depressive patients from healthy controls.

PubMed

Liu, Yashu; Yieh, Lynn; Yang, Tao; Drinkenburg, Wilhelmus; Peeters, Pieter; Steckler, Thomas; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

2016-08-23

Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are "melancholic depression" and "anxious depression". Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression. With the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress. We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics.

The comparison of grey-scale ultrasonic and clinical features of hepatoblastoma and hepatocellular carcinoma in children: a retrospective study for ten years

PubMed Central

2011-01-01

Background Hepatoblastoma (HBL) and hepatocellular carcinoma (HCC) are respectively the first and the second most common pediatric malignant liver tumors. The purpose of this study was to evaluate the combined use of the ultrasound examination and the assessment of the patients' clinical features for differentiating HBL from HCC in children. Methods Thirty cases of the confirmed HBL and 12 cases of the confirmed HCC in children under the age of 15 years were enrolled into our study. They were divided into the HBL group and the HCC group according to the histological types of the tumors. The ultrasonic features and the clinical manifestations of the two groups were retrospectively analyzed, with an emphasis on the following parameters: onset age, gender (male/female) ratio, positive epatitis-B-surface-antigen (HBV), alpha-fetoprotein increase, and echo features including septa, calcification and liquefaction within the tumors. Results Compared with the children with HCC, the children with HBL had a significantly younger onset age (8.2 years vs. 3.9 years, P < 0.001) and a significantly smaller frequency of positive HBV (66.7% vs. 13.3%, P < 0.001). The septa and liquefaction were more frequently found in HBL than in HCC (25/30, 83.3% vs. 2/12, 16.7%, P < 0.001; 17/30, 56.7% vs. 3/12, 25%, P = 0.02). When a combination of the liquefaction, septa, negative HBV and onset age smaller than 5 years was used in the evaluation, the sensitivity was raised to 90%, the accuracy was raised to 88%, and the negative predictive value was raised to 73%. Conclusion Ultrasonic features combined with clinical manifestations are valuable for differentiating HBL from HCC in children. PMID:21702993

Compensation for the signal processing characteristics of ultrasound B-mode scanners in adaptive speckle reduction.

PubMed

Crawford, D C; Bell, D S; Bamber, J C

1993-01-01

A systematic method to compensate for nonlinear amplification of individual ultrasound B-scanners has been investigated in order to optimise performance of an adaptive speckle reduction (ASR) filter for a wide range of clinical ultrasonic imaging equipment. Three potential methods have been investigated: (1) a method involving an appropriate selection of the speckle recognition feature was successful when the scanner signal processing executes simple logarithmic compressions; (2) an inverse transform (decompression) of the B-mode image was effective in correcting for the measured characteristics of image data compression when the algorithm was implemented in full floating point arithmetic; (3) characterising the behaviour of the statistical speckle recognition feature under conditions of speckle noise was found to be the method of choice for implementation of the adaptive speckle reduction algorithm in limited precision integer arithmetic. In this example, the statistical features of variance and mean were investigated. The third method may be implemented on commercially available fast image processing hardware and is also better suited for transfer into dedicated hardware to facilitate real-time adaptive speckle reduction. A systematic method is described for obtaining ASR calibration data from B-mode images of a speckle producing phantom.

[Discriminant analysis to predict the clinical diagnosis of primary immunodeficiencies: a preliminary report].

PubMed

Murata, Chiharu; Ramírez, Ana Belén; Ramírez, Guadalupe; Cruz, Alonso; Morales, José Luis; Lugo-Reyes, Saul Oswaldo

2015-01-01

The features in a clinical history from a patient with suspected primary immunodeficiency (PID) direct the differential diagnosis through pattern recognition. PIDs are a heterogeneous group of more than 250 congenital diseases with increased susceptibility to infection, inflammation, autoimmunity, allergy and malignancy. Linear discriminant analysis (LDA) is a multivariate supervised classification method to sort objects of study into groups by finding linear combinations of a number of variables. To identify the features that best explain membership of pediatric PID patients to a group of defect or disease. An analytic cross-sectional study was done with a pre-existing database with clinical and laboratory records from 168 patients with PID, followed at the National Institute of Pediatrics during 1991-2012, it was used to build linear discriminant models that would explain membership of each patient to the different group defects and to the most prevalent PIDs in our registry. After a preliminary run only 30 features were included (4 demographic, 10 clinical, 10 laboratory, 6 germs), with which the training models were developed through a stepwise regression algorithm. We compared the automatic feature selection with a selection made by a human expert, and then assessed the diagnostic usefulness of the resulting models (sensitivity, specificity, prediction accuracy and kappa coefficient), with 95% confidence intervals. The models incorporated 6 to 14 features to explain membership of PID patients to the five most abundant defect groups (combined, antibody, well-defined, dysregulation and phagocytosis), and to the four most prevalent PID diseases (X-linked agammaglobulinemia, chronic granulomatous disease, common variable immunodeficiency and ataxiatelangiectasia). In practically all cases of feature selection the machine outperformed the human expert. Diagnosis prediction using the equations created had a global accuracy of 83 to 94%, with sensitivity of 60 to 100%, specificity of 83 to 95% and kappa coefficient of 0.37 to 0.76. In general, the selection of features has clinical plausibility, and the practical advantage of utilizing only clinical attributes, infecting germs and routine lab results (blood cell counts and serum immunoglobulins). The performance of the model as a diagnostic tool was acceptable. The study's main limitations are a limited sample size and a lack of cross validation. This is only the first step in the construction of a machine learning system, with a wider approach that includes a larger database and different methodologies, to assist the clinical diagnosis of primary immunodeficiencies.

Obstructive Sleep Apnea in Women: Study of Speech and Craniofacial Characteristics.

PubMed

Tyan, Marina; Espinoza-Cuadros, Fernando; Fernández Pozo, Rubén; Toledano, Doroteo; Lopez Gonzalo, Eduardo; Alcazar Ramirez, Jose Daniel; Hernandez Gomez, Luis Alfonso

2017-11-06

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by frequent cessation of breathing lasting 10 seconds or longer. The diagnosis of OSA is performed through an expensive procedure, which requires an overnight stay at the hospital. This has led to several proposals based on the analysis of patients' facial images and speech recordings as an attempt to develop simpler and cheaper methods to diagnose OSA. The objective of this study was to analyze possible relationships between OSA and speech and facial features on a female population and whether these possible connections may be affected by the specific clinical characteristics in OSA population and, more specifically, to explore how the connection between OSA and speech and facial features can be affected by gender. All the subjects are Spanish subjects suspected to suffer from OSA and referred to a sleep disorders unit. Voice recordings and photographs were collected in a supervised but not highly controlled way, trying to test a scenario close to a realistic clinical practice scenario where OSA is assessed using an app running on a mobile device. Furthermore, clinical variables such as weight, height, age, and cervical perimeter, which are usually reported as predictors of OSA, were also gathered. Acoustic analysis is centered in sustained vowels. Facial analysis consists of a set of local craniofacial features related to OSA, which were extracted from images after detecting facial landmarks by using the active appearance models. To study the probable OSA connection with speech and craniofacial features, correlations among apnea-hypopnea index (AHI), clinical variables, and acoustic and facial measurements were analyzed. The results obtained for female population indicate mainly weak correlations (r values between .20 and .39). Correlations between AHI, clinical variables, and speech features show the prevalence of formant frequencies over bandwidths, with F2/i/ being the most appropriate formant frequency for OSA prediction in women. Results obtained for male population indicate mainly very weak correlations (r values between .01 and .19). In this case, bandwidths prevail over formant frequencies. Correlations between AHI, clinical variables, and craniofacial measurements are very weak. In accordance with previous studies, some clinical variables are found to be good predictors of OSA. Besides, strong correlations are found between AHI and some clinical variables with speech and facial features. Regarding speech feature, the results show the prevalence of formant frequency F2/i/ over the rest of features for the female population as OSA predictive feature. Although the correlation reported is weak, this study aims to find some traces that could explain the possible connection between OSA and speech in women. In the case of craniofacial measurements, results evidence that some features that can be used for predicting OSA in male patients are not suitable for testing female population. ©Marina Tyan, Fernando Espinoza-Cuadros, Rubén Fernández Pozo, Doroteo Toledano, Eduardo Lopez Gonzalo, Jose Daniel Alcazar Ramirez, Luis Alfonso Hernandez Gomez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.11.2017.

[Various methods of overcoming secondary resistance to treatment developing in relation to adaptation to psychotropic drugs during long-term treatment (clinico-experimental study)].

PubMed

Avrutskiĭ, G Ia; Allikmets, L Kh; Neduva, A A; Zharkovskiĭ, A M; Beliakov, A V

1984-01-01

Clinical and experimental studies into the phenomenon of adaptation to neuroleptic agents and into the methods of its overcoming were carried out. An experimental study of the long-term administrations of haloperidol revealed the formation of adaptation to the drug which can be overcome by a zigzag-like sharp elevation of the dosage followed by rapid reduction to the baseline level. The trial of this method under clinical conditions showed that it was expedient to use on a large scale the experimental findings on the specific features of the formation and prevention of the secondary therapeutic resistance.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

PubMed

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is powerful, intuitive and very efficiently provides a high-level overview of a massive data space. In our application it exposes both expected relationships and relationships very rarely considered worth investigating by clinical researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative Ultrasound Using Texture Analysis of Myofascial Pain Syndrome in the Trapezius.

PubMed

Kumbhare, Dinesh A; Ahmed, Sara; Behr, Michael G; Noseworthy, Michael D

2018-01-01

Objective-The objective of this study is to assess the discriminative ability of textural analyses to assist in the differentiation of the myofascial trigger point (MTrP) region from normal regions of skeletal muscle. Also, to measure the ability to reliably differentiate between three clinically relevant groups: healthy asymptomatic, latent MTrPs, and active MTrP. Methods-18 and 19 patients were identified with having active and latent MTrPs in the trapezius muscle, respectively. We included 24 healthy volunteers. Images were obtained by research personnel, who were blinded with respect to the clinical status of the study participant. Histograms provided first-order parameters associated with image grayscale. Haralick, Galloway, and histogram-related features were used in texture analysis. Blob analysis was conducted on the regions of interest (ROIs). Principal component analysis (PCA) was performed followed by multivariate analysis of variance (MANOVA) to determine the statistical significance of the features. Results-92 texture features were analyzed for factorability using Bartlett's test of sphericity, which was significant. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.94. PCA demonstrated rotated eigenvalues of the first eight components (each comprised of multiple texture features) explained 94.92% of the cumulative variance in the ultrasound image characteristics. The 24 features identified by PCA were included in the MANOVA as dependent variables, and the presence of a latent or active MTrP or healthy muscle were independent variables. Conclusion-Texture analysis techniques can discriminate between the three clinically relevant groups.

The variable presentations and broadening geographic distribution of hepatic fascioliasis.

PubMed

Rowan, Sarah E; Levi, Marilyn E; Youngwerth, Jean M; Brauer, Brian; Everson, Gregory T; Johnson, Steven C

2012-06-01

We report 2 unrelated cases of hepatic fascioliasis in travelers returning to the United States from Africa and the Middle East. The first case presented with acute infection. Prominent clinical features included abdominal pain, elevated liver transaminases, serpiginous hepatic lesions, pericapsular hematoma, and marked peripheral eosinophilia. The second case was diagnosed in the chronic stage of infection and presented with right upper quadrant abdominal pain, cystic hepatic lesions, and an adult fluke in the common bile duct. We review the life cycle of Fasciola species, the corresponding clinical features during the stages of human infection, diagnostic methods, and the evolving understanding of the epidemiology of human fascioliasis, particularly emphasizing fascioliasis in African countries. Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.

Fourier domain image fusion for differential X-ray phase-contrast breast imaging.

PubMed

Coello, Eduardo; Sperl, Jonathan I; Bequé, Dirk; Benz, Tobias; Scherer, Kai; Herzen, Julia; Sztrókay-Gaul, Anikó; Hellerhoff, Karin; Pfeiffer, Franz; Cozzini, Cristina; Grandl, Susanne

2017-04-01

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well. Copyright © 2017 Elsevier B.V. All rights reserved.

MO-AB-BRA-10: Cancer Therapy Outcome Prediction Based On Dempster-Shafer Theory and PET Imaging

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

Lian, C; University of Rouen, QuantIF - EA 4108 LITIS, 76000 Rouen; Li, H

2015-06-15

Purpose: In cancer therapy, utilizing FDG-18 PET image-based features for accurate outcome prediction is challenging because of 1) limited discriminative information within a small number of PET image sets, and 2) fluctuant feature characteristics caused by the inferior spatial resolution and system noise of PET imaging. In this study, we proposed a new Dempster-Shafer theory (DST) based approach, evidential low-dimensional transformation with feature selection (ELT-FS), to accurately predict cancer therapy outcome with both PET imaging features and clinical characteristics. Methods: First, a specific loss function with sparse penalty was developed to learn an adaptive low-rank distance metric for representing themore » dissimilarity between different patients’ feature vectors. By minimizing this loss function, a linear low-dimensional transformation of input features was achieved. Also, imprecise features were excluded simultaneously by applying a l2,1-norm regularization of the learnt dissimilarity metric in the loss function. Finally, the learnt dissimilarity metric was applied in an evidential K-nearest-neighbor (EK- NN) classifier to predict treatment outcome. Results: Twenty-five patients with stage II–III non-small-cell lung cancer and thirty-six patients with esophageal squamous cell carcinomas treated with chemo-radiotherapy were collected. For the two groups of patients, 52 and 29 features, respectively, were utilized. The leave-one-out cross-validation (LOOCV) protocol was used for evaluation. Compared to three existing linear transformation methods (PCA, LDA, NCA), the proposed ELT-FS leads to higher prediction accuracy for the training and testing sets both for lung-cancer patients (100+/−0.0, 88.0+/−33.17) and for esophageal-cancer patients (97.46+/−1.64, 83.33+/−37.8). The ELT-FS also provides superior class separation in both test data sets. Conclusion: A novel DST- based approach has been proposed to predict cancer treatment outcome using PET image features and clinical characteristics. A specific loss function has been designed for robust accommodation of feature set incertitude and imprecision, facilitating adaptive learning of the dissimilarity metric for the EK-NN classifier.« less

Assessment of Canine Mast Cell Tumor Mortality Risk Based on Clinical, Histologic, Immunohistochemical, and Molecular Features.

PubMed

Horta, Rodrigo S; Lavalle, Gleidice E; Monteiro, Lidianne N; Souza, Mayara C C; Cassali, Geovanni D; Araújo, Roberto B

2018-03-01

Mast cell tumor (MCT) is a frequent cutaneous neoplasm in dogs that is heterogeneous in clinical presentation and biological behavior, with a variable potential for recurrence and metastasis. Accurate prediction of clinical outcomes has been challenging. The study objective was to develop a system for classification of canine MCT according to the mortality risk based on individual assessment of clinical, histologic, immunohistochemical, and molecular features. The study included 149 dogs with a histologic diagnosis of cutaneous or subcutaneous MCT. By univariate analysis, MCT metastasis and related death was significantly associated with clinical stage ( P < .0001, r P = -0.610), history of tumor recurrence ( P < .0001, r P = -0.550), Patnaik ( P < .0001, r P = -0.380) and Kiupel grades ( P < .0001, r P = -0.500), predominant organization of neoplastic cells ( P < .0001, r P = -0.452), mitotic count ( P < .0001, r P = -0.325), Ki-67 labeling index ( P < .0001, r P = -0.414), KITr pattern ( P = .02, r P = 0.207), and c-KIT mutational status ( P < .0001, r P = -0.356). By multivariate analysis with Cox proportional hazard model, only 2 features were independent predictors of overall survival: an amendment of the World Health Organization clinical staging system (hazard ratio [95% CI]: 1.824 [1.210-4.481]; P = .01) and a history of tumor recurrence (hazard ratio [95% CI]: 9.250 [2.158-23.268]; P < .001]. From these results, we propose an amendment of the WHO staging system, a method of risk analysis, and a suggested approach to clinical and laboratory evaluation of dogs with cutaneous MCT.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization

PubMed Central

Cruz-Roa, Angel; Díaz, Gloria; Romero, Eduardo; González, Fabio A.

2011-01-01

Histopathological images are an important resource for clinical diagnosis and biomedical research. From an image understanding point of view, the automatic annotation of these images is a challenging problem. This paper presents a new method for automatic histopathological image annotation based on three complementary strategies, first, a part-based image representation, called the bag of features, which takes advantage of the natural redundancy of histopathological images for capturing the fundamental patterns of biological structures, second, a latent topic model, based on non-negative matrix factorization, which captures the high-level visual patterns hidden in the image, and, third, a probabilistic annotation model that links visual appearance of morphological and architectural features associated to 10 histopathological image annotations. The method was evaluated using 1,604 annotated images of skin tissues, which included normal and pathological architectural and morphological features, obtaining a recall of 74% and a precision of 50%, which improved a baseline annotation method based on support vector machines in a 64% and 24%, respectively. PMID:22811960

Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.

PubMed

Hassanpour, Saeed; Bay, Graham; Langlotz, Curtis P

2017-06-01

We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.

Method to integrate clinical guidelines into the electronic health record (EHR) by applying the archetypes approach.

PubMed

Garcia, Diego; Moro, Claudia Maria Cabral; Cicogna, Paulo Eduardo; Carvalho, Deborah Ribeiro

2013-01-01

Clinical guidelines are documents that assist healthcare professionals, facilitating and standardizing diagnosis, management, and treatment in specific areas. Computerized guidelines as decision support systems (DSS) attempt to increase the performance of tasks and facilitate the use of guidelines. Most DSS are not integrated into the electronic health record (EHR), ordering some degree of rework especially related to data collection. This study's objective was to present a method for integrating clinical guidelines into the EHR. The study developed first a way to identify data and rules contained in the guidelines, and then incorporate rules into an archetype-based EHR. The proposed method tested was anemia treatment in the Chronic Kidney Disease Guideline. The phases of the method are: data and rules identification; archetypes elaboration; rules definition and inclusion in inference engine; and DSS-EHR integration and validation. The main feature of the proposed method is that it is generic and can be applied toany type of guideline.

Iris features-based heart disease diagnosis by computer vision

NASA Astrophysics Data System (ADS)

Nguchu, Benedictor A.; Li, Li

2017-07-01

The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.

SU-E-I-01: Iterative CBCT Reconstruction with a Feature-Preserving Penalty

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

Lyu, Q; Li, B; Southern Medical University, Guangzhou

2015-06-15

Purpose: Low-dose CBCT is desired in various clinical applications. Iterative image reconstruction algorithms have shown advantages in suppressing noise in low-dose CBCT. However, due to the smoothness constraint enforced during the reconstruction process, edges may be blurred and image features may lose in the reconstructed image. In this work, we proposed a new penalty design to preserve image features in the image reconstructed by iterative algorithms. Methods: Low-dose CBCT is reconstructed by minimizing the penalized weighted least-squares (PWLS) objective function. Binary Robust Independent Elementary Features (BRIEF) of the image were integrated into the penalty of PWLS. BRIEF is a generalmore » purpose point descriptor that can be used to identify important features of an image. In this work, BRIEF distance of two neighboring pixels was used to weigh the smoothing parameter in PWLS. For pixels of large BRIEF distance, weaker smooth constraint will be enforced. Image features will be better preserved through such a design. The performance of the PWLS algorithm with BRIEF penalty was evaluated by a CatPhan 600 phantom. Results: The image quality reconstructed by the proposed PWLS-BRIEF algorithm is superior to that by the conventional PWLS method and the standard FDK method. At matched noise level, edges in PWLS-BRIEF reconstructed image are better preserved. Conclusion: This study demonstrated that the proposed PWLS-BRIEF algorithm has great potential on preserving image features in low-dose CBCT.« less

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

PubMed

Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

2007-11-29

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

[Clinical and anatomical features of congenital microphthalmia and anophthalmia in children and conservative methods of rehabilitation].

PubMed

Sudovskaya, T V; Filatova, I A; Kiseleva, T N; Bobrovskaya, Yu A; Kokoeva, N Sh

2016-01-01

To develop a comprehensive classification system of distinctive clinical and anatomical features of congenital microphthalmia and anophthalmia in children and to specify indications, contraindications, and optimal timing of the primary and subsequent prosthetic treatment. A total of 70 patients with congenital micro- or anophthalmia aged from 1 month to 12 years were examined. Besides the routine ophthalmic examination, all patients underwent eye and orbit ultrasound (axial length measurement and B-scan), computed tomography of the orbits and skull, and immunological tests for infectious diseases (enzyme-linked immunosorbent assays). Basing on the examination we have determined the common types of congenital micro- and anophthalmia in children. We have also developed a stepwise prosthetic treatment aimed at better cosmetic rehabilitation. Indications and contraindications for the use of ocular prostheses in children with congenital micro- and anophthalmia have been identified. The proposed method of stepwise prosthetics is the principal option for conservative rehabilitation of children with congenital micro- or anophthalmia.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

PubMed

Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

2015-03-01

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

Extracorporeal photopheresis: Review of technical aspects.

PubMed

Arora, Satyam; Setia, Rasika

2017-01-01

Extracorporeal photochemotherapy (ECP) is considered as an immune modulating therapy majorly targeting the T cells of the Immune system. ECP induces an anti-inflammatory condition with tolerogenic responses without inducing a global immunosuppression state which is a typical feature of other therapeutic options such as steroids. Clinical indication of ECP has grown over time since its initial applications. Our review discusses the technical aspects of the concept of photopheresis with the available methods for its clinical applications.

A New Feature-Enhanced Speckle Reduction Method Based on Multiscale Analysis for Ultrasound B-Mode Imaging.

PubMed

Kang, Jinbum; Lee, Jae Young; Yoo, Yangmo

2016-06-01

Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p<0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.

Metal artifact reduction using a patch-based reconstruction for digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Borges, Lucas R.; Bakic, Predrag R.; Maidment, Andrew D. A.; Vieira, Marcelo A. C.

2017-03-01

Digital breast tomosynthesis (DBT) is rapidly emerging as the main clinical tool for breast cancer screening. Although several reconstruction methods for DBT are described by the literature, one common issue is the interplane artifacts caused by out-of-focus features. For breasts containing highly attenuating features, such as surgical clips and large calcifications, the artifacts are even more apparent and can limit the detection and characterization of lesions by the radiologist. In this work, we propose a novel method of combining backprojected data into tomographic slices using a patch-based approach, commonly used in denoising. Preliminary tests were performed on a geometry phantom and on an anthropomorphic phantom containing metal inserts. The reconstructed images were compared to a commercial reconstruction solution. Qualitative assessment of the reconstructed images provides evidence that the proposed method reduces artifacts while maintaining low noise levels. Objective assessment supports the visual findings. The artifact spread function shows that the proposed method is capable of suppressing artifacts generated by highly attenuating features. The signal difference to noise ratio shows that the noise levels of the proposed and commercial methods are comparable, even though the commercial method applies post-processing filtering steps, which were not implemented on the proposed method. Thus, the proposed method can produce tomosynthesis reconstructions with reduced artifacts and low noise levels.

User-guided segmentation for volumetric retinal optical coherence tomography images

PubMed Central

Yin, Xin; Chao, Jennifer R.; Wang, Ruikang K.

2014-01-01

Abstract. Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method. PMID:25147962

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

User-guided segmentation for volumetric retinal optical coherence tomography images.

PubMed

Yin, Xin; Chao, Jennifer R; Wang, Ruikang K

2014-08-01

Despite the existence of automatic segmentation techniques, trained graders still rely on manual segmentation to provide retinal layers and features from clinical optical coherence tomography (OCT) images for accurate measurements. To bridge the gap between this time-consuming need of manual segmentation and currently available automatic segmentation techniques, this paper proposes a user-guided segmentation method to perform the segmentation of retinal layers and features in OCT images. With this method, by interactively navigating three-dimensional (3-D) OCT images, the user first manually defines user-defined (or sketched) lines at regions where the retinal layers appear very irregular for which the automatic segmentation method often fails to provide satisfactory results. The algorithm is then guided by these sketched lines to trace the entire 3-D retinal layer and anatomical features by the use of novel layer and edge detectors that are based on robust likelihood estimation. The layer and edge boundaries are finally obtained to achieve segmentation. Segmentation of retinal layers in mouse and human OCT images demonstrates the reliability and efficiency of the proposed user-guided segmentation method.

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

Standardized Computer-based Organized Reporting of EEG: SCORE

PubMed Central

Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C; Fuglsang-Frederiksen, Anders; Martins-da-Silva, António; Trinka, Eugen; Visser, Gerhard; Rubboli, Guido; Hjalgrim, Helle; Stefan, Hermann; Rosén, Ingmar; Zarubova, Jana; Dobesberger, Judith; Alving, Jørgen; Andersen, Kjeld V; Fabricius, Martin; Atkins, Mary D; Neufeld, Miri; Plouin, Perrine; Marusic, Petr; Pressler, Ronit; Mameniskiene, Ruta; Hopfengärtner, Rüdiger; Emde Boas, Walter; Wolf, Peter

2013-01-01

The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists. PMID:23506075

Feature tracking CMR reveals abnormal strain in preclinical arrhythmogenic right ventricular dysplasia/ cardiomyopathy: a multisoftware feasibility and clinical implementation study.

PubMed

Bourfiss, Mimount; Vigneault, Davis M; Aliyari Ghasebeh, Mounes; Murray, Brittney; James, Cynthia A; Tichnell, Crystal; Mohamed Hoesein, Firdaus A; Zimmerman, Stefan L; Kamel, Ihab R; Calkins, Hugh; Tandri, Harikrishna; Velthuis, Birgitta K; Bluemke, David A; Te Riele, Anneline S J M

2017-09-01

Regional right ventricular (RV) dysfunction is the hallmark of Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C), but is currently only qualitatively evaluated in the clinical setting. Feature Tracking Cardiovascular Magnetic Resonance (FT-CMR) is a novel quantitative method that uses cine CMR to calculate strain values. However, most prior FT-CMR studies in ARVD/C have focused on global RV strain using different software methods, complicating implementation of FT-CMR in clinical practice. We aimed to assess the clinical value of global and regional strain using FT-CMR in ARVD/C and to determine differences between commercially available FT-CMR software packages. We analyzed cine CMR images of 110 subjects (39 overt ARVD/C [mutation+/phenotype+], 40 preclinical ARVD/C [mutation+/phenotype-] and 31 control) for global and regional (subtricuspid, anterior, apical) RV strain in the horizontal longitudinal axis using four FT-CMR software methods (Multimodality Tissue Tracking, TomTec, Medis and Circle Cardiovascular Imaging). Intersoftware agreement was assessed using Bland Altman plots. For global strain, all methods showed reduced strain in overt ARVD/C patients compared to control subjects (p < 0.041), whereas none distinguished preclinical from control subjects (p > 0.275). For regional strain, overt ARVD/C patients showed reduced strain compared to control subjects in all segments which reached statistical significance in the subtricuspid region for all software methods (p < 0.037), in the anterior wall for two methods (p < 0.005) and in the apex for one method (p = 0.012). Preclinical subjects showed abnormal subtricuspid strain compared to control subjects using one of the software methods (p = 0.009). Agreement between software methods for absolute strain values was low (Intraclass Correlation Coefficient = 0.373). Despite large intersoftware variability of FT-CMR derived strain values, all four software methods distinguished overt ARVD/C patients from control subjects by both global and subtricuspid strain values. In the subtricuspid region, one software package distinguished preclinical from control subjects, suggesting the potential to identify early ARVD/C prior to overt disease expression.

TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

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

Zhang, H; Chen, W; Kligerman, S

2014-06-15

Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had {sup 18}F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associatedmore » changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including quantitative PET/CT features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer. This work was supported in part by National Cancer Institute Grant R21 CA131979 and R01 CA172638. Shan Tan was supported in part by the National Natural Science Foundation of China 60971112 and 61375018, and by Fundamental Research Funds for the Central Universities 2012QN086.« less

A similarity measure method combining location feature for mammogram retrieval.

PubMed

Wang, Zhiqiong; Xin, Junchang; Huang, Yukun; Li, Chen; Xu, Ling; Li, Yang; Zhang, Hao; Gu, Huizi; Qian, Wei

2018-05-28

Breast cancer, the most common malignancy among women, has a high mortality rate in clinical practice. Early detection, diagnosis and treatment can reduce the mortalities of breast cancer greatly. The method of mammogram retrieval can help doctors to find the early breast lesions effectively and determine a reasonable feature set for image similarity measure. This will improve the accuracy effectively for mammogram retrieval. This paper proposes a similarity measure method combining location feature for mammogram retrieval. Firstly, the images are pre-processed, the regions of interest are detected and the lesions are segmented in order to get the center point and radius of the lesions. Then, the method, namely Coherent Point Drift, is used for image registration with the pre-defined standard image. The center point and radius of the lesions after registration are obtained and the standard location feature of the image is constructed. This standard location feature can help figure out the location similarity between the image pair from the query image to each dataset image in the database. Next, the content feature of the image is extracted, including the Histogram of Oriented Gradients, the Edge Direction Histogram, the Local Binary Pattern and the Gray Level Histogram, and the image pair content similarity can be calculated using the Earth Mover's Distance. Finally, the location similarity and content similarity are fused to form the image fusion similarity, and the specified number of the most similar images can be returned according to it. In the experiment, 440 mammograms, which are from Chinese women in Northeast China, are used as the database. When fusing 40% lesion location feature similarity and 60% content feature similarity, the results have obvious advantages. At this time, precision is 0.83, recall is 0.76, comprehensive indicator is 0.79, satisfaction is 96.0%, mean is 4.2 and variance is 17.7. The results show that the precision and recall of this method have obvious advantage, compared with the content-based image retrieval.

Differentiating cognitive impairment due to corticobasal degeneration and Alzheimer disease

PubMed Central

Day, Gregory S.; Lim, Tae Sung; Hassenstab, Jason; Goate, Alison M.; Grant, Elizabeth A.; Roe, Catherine M.; Cairns, Nigel J.

2017-01-01

Objective: To identify clinical features that reliably differentiate individuals with cognitive impairment due to corticobasal degeneration (CBD) and Alzheimer disease (AD). Methods: Clinical features were compared between individuals with autopsy-proven CBD (n = 17) and AD (n = 16). All individuals presented with prominent cognitive complaints and were evaluated annually with semistructured interviews, detailed neurologic examinations, and neuropsychological testing. Results: Substantial overlap was observed between individuals with dementia due to CBD and AD concerning presenting complaints, median (range) duration of symptoms before assessment (CBD = 3.0 [0–5.0] years, AD = 2.5 [0–8.0] years; p = 0.96), and median (range) baseline dementia severity (Clinical Dementia Rating Sum of Boxes: CBD = 3.5 [0–12.0], AD = 4.25 [0.5–9.0], p = 0.49). Subsequent emergence of asymmetric motor/sensory signs, hyperreflexia, gait abnormalities, parkinsonism, falls, urinary incontinence, and extraocular movement abnormalities identified individuals with CBD, with ≥3 discriminating features detected in 80% of individuals within 3.1 years (95% confidence interval 2.9–3.3) of the initial assessment. Individuals with CBD exhibited accelerated worsening of illness severity and declines in episodic memory, executive functioning, and letter fluency. Semiquantitative pathologic assessment revealed prominent tau pathology within the frontal and parietal lobes of CBD cases. Comorbid AD neuropathologic change was present in 59% (10 of 17) of CBD cases but did not associate with the clinical phenotype, rate of dementia progression, or dementia duration. Conclusions: CBD may mimic AD dementia early in its disease course. Interval screening for discriminating clinical features may improve antemortem diagnosis in individuals with CBD and prominent cognitive symptoms. PMID:28235814

Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer

NASA Astrophysics Data System (ADS)

Cheng, Hu; Fan, Ming; Zhang, Peng; Liu, Bin; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer is a highly heterogeneous disease both biologically and clinically, and certain pathologic parameters, i.e., Ki67 expression, are useful in predicting the prognosis of patients. The aim of the study is to identify intratumor heterogeneity of breast cancer for predicting Ki-67 proliferation status in estrogen receptor (ER)-positive breast cancer patients. A dataset of 77 patients was collected who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Of these patients, 51 were high-Ki-67 expression and 26 were low-Ki-67 expression. We partitioned the breast tumor into subregions using two methods based on the values of time to peak (TTP) and peak enhancement rate (PER). Within each tumor subregion, image features were extracted including statistical and morphological features from DCE-MRI. The classification models were applied on each region separately to assess whether the classifiers based on features extracted from various subregions features could have different performance for prediction. An area under a receiver operating characteristic curve (AUC) was computed using leave-one-out cross-validation (LOOCV) method. The classifier using features related with moderate time to peak achieved best performance with AUC of 0.826 than that based on the other regions. While using multi-classifier fusion method, the AUC value was significantly (P=0.03) increased to 0.858+/-0.032 compare to classifier with AUC of 0.778 using features from the entire tumor. The results demonstrated that features reflect heterogeneity in intratumoral subregions can improve the classifier performance to predict the Ki-67 proliferation status than the classifier using features from entire tumor alone.

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features.

PubMed

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M

2015-01-01

Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e - 3) on all calculi from 1 to 433 mm(3) in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

Characterizing mammographic images by using generic texture features

PubMed Central

2012-01-01

Introduction Although mammographic density is an established risk factor for breast cancer, its use is limited in clinical practice because of a lack of automated and standardized measurement methods. The aims of this study were to evaluate a variety of automated texture features in mammograms as risk factors for breast cancer and to compare them with the percentage mammographic density (PMD) by using a case-control study design. Methods A case-control study including 864 cases and 418 controls was analyzed automatically. Four hundred seventy features were explored as possible risk factors for breast cancer. These included statistical features, moment-based features, spectral-energy features, and form-based features. An elaborate variable selection process using logistic regression analyses was performed to identify those features that were associated with case-control status. In addition, PMD was assessed and included in the regression model. Results Of the 470 image-analysis features explored, 46 remained in the final logistic regression model. An area under the curve of 0.79, with an odds ratio per standard deviation change of 2.88 (95% CI, 2.28 to 3.65), was obtained with validation data. Adding the PMD did not improve the final model. Conclusions Using texture features to predict the risk of breast cancer appears feasible. PMD did not show any additional value in this study. With regard to the features assessed, most of the analysis tools appeared to reflect mammographic density, although some features did not correlate with PMD. It remains to be investigated in larger case-control studies whether these features can contribute to increased prediction accuracy. PMID:22490545

Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis.

PubMed

Bennett, Robert M; Russell, Jon; Cappelleri, Joseph C; Bushmakin, Andrew G; Zlateva, Gergana; Sadosky, Alesia

2010-06-28

The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters. Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships. Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions). Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.

Pediatric Eosinophilic Esophagitis Symptom Scores (PEESS® v2.0) identify histologic and molecular correlates of the key clinical features of disease

PubMed Central

Martin, Lisa J.; Franciosi, James P.; Collins, Margaret H.; Abonia, J. Pablo; Lee, James J.; Hommel, Kevin A.; Varni, James W.; Grotjan, J. Tommie; Eby, Michael; He, Hua; Marsolo, Keith; Putnam, Philip E.; Garza, Jose M.; Kaul, Ajay; Wen, Ting; Rothenberg, Marc E.

2015-01-01

Background The Pediatric Eosinophilic Esophagitis Symptom Score (PEESS® v2.0) measures patient-relevant outcomes. However, whether patient-identified domains (dysphagia, gastrointestinal reflux disease (GERD), nausea/vomiting, and pain) align with clinical symptomology and histopathologic and molecular features of eosinophilic esophagitis (EoE) is unclear. Objective The purpose of this study was to determine if clinical features of EoE, measured through the PEESS® v2.0, associate with histopathologic and molecular features of EoE. This represents a novel approach for analysis of allergic diseases, given the availability of allergic tissue biopsy specimens. Methods We systematically recruited treated and untreated, pediatric patients with EoE (aged 2–18 years) and examined parent proxy–reported symptoms using the PEESS® v2.0. Clinical symptomology was collected by questionnaire. Esophageal biopsy samples were quantified for levels of eosinophils, eosinophil peroxidase (EPX) immunohistochemical staining, and mast cells. Molecular features were assessed by the EoE Diagnostic Panel (94 EoE-related gene transcripts). Associations between domain scores and clinical symptoms and biologic features were analyzed using Wilcoxon Rank Sum and Spearman correlation. Results The PEESS® v2.0 domains correlated to specific parent-reported symptoms: dysphagia (p = 0.0012), GERD (p = 0.0001), and nausea/vomiting (p < 0.0001). Pain correlated with multiple symptoms (p < 0.0005). Dysphagia correlated most strongly with overall histopathology, particularly in the proximal esophagus (p ≤ 0.0049). Markers of esophageal activity (EPX) were significantly associated with dysphagia (strongest r = .37; p = 0.02). Eosinophil levels were more associated with pain (r = 0.27; p=0.06) than for dysphagia (r = 0.24; p = 0.13). The dysphagia domain correlated the most with esophageal gene transcript levels, predominantly with mast cell–specific genes. Conclusion We have 1) established a validated, parent proxy–report measure for pediatric EoE — the PEESS® v2.0; 2) verified that parent-proxy effectively captures symptoms; 3) determined that the dysphagia domain most closely aligns with symptoms and tissue-based molecular biomarkers; 4) established that symptoms correlate EPX staining; and 5) observed association between mast cells and dysphagia. PMID:26051952

Molecular Diagnosis and Biomarker Identification on SELDI proteomics data by ADTBoost method.

PubMed

Wang, Lu-Yong; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

Clinical proteomics is an emerging field that will have great impact on molecular diagnosis, identification of disease biomarkers, drug discovery and clinical trials in the post-genomic era. Protein profiling in tissues and fluids in disease and pathological control and other proteomics techniques will play an important role in molecular diagnosis with therapeutics and personalized healthcare. We introduced a new robust diagnostic method based on ADTboost algorithm, a novel algorithm in proteomics data analysis to improve classification accuracy. It generates classification rules, which are often smaller and easier to interpret. This method often gives most discriminative features, which can be utilized as biomarkers for diagnostic purpose. Also, it has a nice feature of providing a measure of prediction confidence. We carried out this method in amyotrophic lateral sclerosis (ALS) disease data acquired by surface enhanced laser-desorption/ionization-time-of-flight mass spectrometry (SELDI-TOF MS) experiments. Our method is shown to have outstanding prediction capacity through the cross-validation, ROC analysis results and comparative study. Our molecular diagnosis method provides an efficient way to distinguish ALS disease from neurological controls. The results are expressed in a simple and straightforward alternating decision tree format or conditional format. We identified most discriminative peaks in proteomic data, which can be utilized as biomarkers for diagnosis. It will have broad application in molecular diagnosis through proteomics data analysis and personalized medicine in this post-genomic era.

Clinical features of symptomatic patellofemoral joint osteoarthritis

PubMed Central

2012-01-01

Introduction Patellofemoral joint osteoarthritis (OA) is common and leads to pain and disability. However, current classification criteria do not distinguish between patellofemoral and tibiofemoral joint OA. The objective of this study was to provide empirical evidence of the clinical features of patellofemoral joint OA (PFJOA) and to explore the potential for making a confident clinical diagnosis in the community setting. Methods This was a population-based cross-sectional study of 745 adults aged ≥50 years with knee pain. Information on risk factors and clinical signs and symptoms was gathered by a self-complete questionnaire, and standardised clinical interview and examination. Three radiographic views of the knee were obtained (weight-bearing semi-flexed posteroanterior, supine skyline and lateral) and individuals were classified into four subsets (no radiographic OA, isolated PFJOA, isolated tibiofemoral joint OA, combined patellofemoral/tibiofemoral joint OA) according to two different cut-offs: 'any OA' and 'moderate to severe OA'. A series of binary logistic and multinomial regression functions were performed to compare the clinical features of each subset and their ability in combination to discriminate PFJOA from other subsets. Results Distinctive clinical features of moderate to severe isolated PFJOA included a history of dramatic swelling, valgus deformity, markedly reduced quadriceps strength, and pain on patellofemoral joint compression. Mild isolated PFJOA was barely distinguished from no radiographic OA (AUC 0.71, 95% CI 0.66, 0.76) with only difficulty descending stairs and coarse crepitus marginally informative over age, sex and body mass index. Other cardinal signs of knee OA - the presence of effusion, bony enlargement, reduced flexion range of movement, mediolateral instability and varus deformity - were indicators of tibiofemoral joint OA. Conclusions Early isolated PFJOA is clinically manifest in symptoms and self-reported functional limitation but has fewer clear clinical signs. More advanced disease is indicated by a small number of simple-to-assess signs and the relative absence of classic signs of knee OA, which are predominantly manifestations of tibiofemoral joint OA. Confident diagnosis of even more advanced PFJOA may be limited in the community setting. PMID:22417687

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Registration of 3D spectral OCT volumes using 3D SIFT feature point matching

NASA Astrophysics Data System (ADS)

Niemeijer, Meindert; Garvin, Mona K.; Lee, Kyungmoo; van Ginneken, Bram; Abràmoff, Michael D.; Sonka, Milan

2009-02-01

The recent introduction of next generation spectral OCT scanners has enabled routine acquisition of high resolution, 3D cross-sectional volumetric images of the retina. 3D OCT is used in the detection and management of serious eye diseases such as glaucoma and age-related macular degeneration. For follow-up studies, image registration is a vital tool to enable more precise, quantitative comparison of disease states. This work presents a registration method based on a recently introduced extension of the 2D Scale-Invariant Feature Transform (SIFT) framework1 to 3D.2 The SIFT feature extractor locates minima and maxima in the difference of Gaussian scale space to find salient feature points. It then uses histograms of the local gradient directions around each found extremum in 3D to characterize them in a 4096 element feature vector. Matching points are found by comparing the distance between feature vectors. We apply this method to the rigid registration of optic nerve head- (ONH) and macula-centered 3D OCT scans of the same patient that have only limited overlap. Three OCT data set pairs with known deformation were used for quantitative assessment of the method's robustness and accuracy when deformations of rotation and scaling were considered. Three-dimensional registration accuracy of 2.0+/-3.3 voxels was observed. The accuracy was assessed as average voxel distance error in N=1572 matched locations. The registration method was applied to 12 3D OCT scans (200 x 200 x 1024 voxels) of 6 normal eyes imaged in vivo to demonstrate the clinical utility and robustness of the method in a real-world environment.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

Propriospinal myoclonus

PubMed Central

van der Salm, Sandra M.A.; Erro, Roberto; Cordivari, Carla; Edwards, Mark J.; Koelman, Johannes H.T.M.; van den Ende, Tom; Bhatia, Kailash P.; van Rootselaar, Anne-Fleur; Brown, Peter

2014-01-01

Objective: Propriospinal myoclonus (PSM) is a rare disorder with repetitive, usually flexor arrhythmic brief jerks of the trunk, hips, and knees in a fixed pattern. It has a presumed generation in the spinal cord and diagnosis depends on characteristic features at polymyography. Recently, a historical paradigm shift took place as PSM has been reported to be a functional (or psychogenic) movement disorder (FMD) in most patients. This review aims to characterize the clinical features, etiology, electrophysiologic features, and treatment outcomes of PSM. Methods: Re-evaluation of all published PSM cases and systematic scoring of clinical and electrophysiologic characteristics in all published cases since 1991. Results: Of the 179 identified patients with PSM (55% male), the mean age at onset was 43 years (range 6–88 years). FMD was diagnosed in 104 (58%) cases. In 12 cases (26% of reported secondary cases, 7% of total cases), a structural spinal cord lesion was found. Clonazepam and botulinum toxin may be effective in reducing jerks. Conclusions: FMD is more frequent than previously assumed. Structural lesions reported to underlie PSM are scarce. Based on our clinical experience and the reviewed literature, we recommend polymyography to assess recruitment variability combined with a Bereitschaftspotential recording in all cases. PMID:25305154

The measurement of retardation in depression.

PubMed

Dantchev, N; Widlöcher, D J

1998-01-01

The description of clinical features helps to distinguish between depressive illness and nondepressive psychic pain and enables the clinician to decide whether prescription of an antidepressant is beneficial. Psychomotor retardation is probably a central feature of depression, and this review discusses the methods available for measuring it. The Salpêtrière Retardation Rating Scale (SRRS) specifically measures psychomotor retardation; the scale and applications are described. Means of measuring motor and speech activity and an experimental approach for understanding the process underlying psychomotor retardation are reviewed. Comparison of the SRRS and other rating scale scores demonstrates that retardation is related to depression severity and therapeutic change and is a good criterion for prediction of therapeutic effect. The SRRS has been used to show that selective antidepressants target specific clinical dimensions of depression depending on the patient subgroup treated. Measures of motor and speech activity are sensitive to therapeutic response. Choice Reaction Time and Simple Reaction Time tasks are particularly suited for examining psychomotor retardation because they test the decision process while avoiding motivation and attention interference. Psychomotor retardation is a constant and probably central feature of depression. Means available for measuring it can be used to assess the effects of antidepressants on specific clinical dimensions.

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

PubMed Central

Bakas, Spyridon; Akbari, Hamed; Sotiras, Aristeidis; Bilello, Michel; Rozycki, Martin; Kirby, Justin S.; Freymann, John B.; Farahani, Keyvan; Davatzikos, Christos

2017-01-01

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method. PMID:28872634

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

Stuck on Screens: Patterns of Computer and Gaming Station Use in Youth Seen in a Psychiatric Clinic

PubMed Central

Baer, Susan; Bogusz, Elliot; Green, David A.

2011-01-01

Objective: Computer and gaming-station use has become entrenched in the culture of our youth. Parents of children with psychiatric disorders report concerns about overuse, but research in this area is limited. The goal of this study is to evaluate computer/gaming-station use in adolescents in a psychiatric clinic population and to examine the relationship between use and functional impairment. Method: 102 adolescents, ages 11–17, from out-patient psychiatric clinics participated. Amount of computer/gaming-station use, type of use (gaming or non-gaming), and presence of addictive features were ascertained along with emotional/functional impairment. Multivariate linear regression was used to examine correlations between patterns of use and impairment. Results: Mean screen time was 6.7±4.2 hrs/day. Presence of addictive features was positively correlated with emotional/functional impairment. Time spent on computer/gaming-station use was not correlated overall with impairment after controlling for addictive features, but non-gaming time was positively correlated with risky behavior in boys. Conclusions: Youth with psychiatric disorders are spending much of their leisure time on the computer/gaming-station and a substantial subset show addictive features of use which is associated with impairment. Further research to develop measures and to evaluate risk is needed to identify the impact of this problem. PMID:21541096

Cataract formation associated with ocular toxocariasis.

PubMed

Ahn, Seong Joon; Woo, Se Joon; Hyon, Joon Young; Park, Kyu Hyung

2013-06-01

To report the clinical features of cataracts in eyes with ocular toxocariasis. Department of Ophthalmology, Seoul National University Bundang Hosptal, Seongnam, South Korea. Retrospective observational case series. The clinical diagnosis of ocular toxocariasis was based on the following characteristic features: retinal granuloma with or without ocular inflammation and positive results in serum antibody enzyme-linked immunosorbent assay. Patients younger than 60 years who presented with a unilateral cataract and were diagnosed with ocular toxocariasis between January 2009 and January 2012 were included. Demographic and ocular examination data for all patients showing atypical cataract features were collected. All cataracts were documented with anterior segment photography. Seven of 83 patients (8.4%) presented with an atypical cataract in the eye with ocular toxocariasis only. The mean patient age was 49.7 years ± 8.3 (SD) (range 38 to 59 years). All patients had small, round, white lens opacities resembling retinal granulomas. The granuloma-like opacities were located primarily in the lens midperiphery and in the subcapsular level. The lens opacity migrated in 1 patient. Ocular toxocariasis can cause a cataract with distinctive clinical features. These cataracts show a granuloma-like opacity primarily in the posterior subcapsular level; the opacity can migrate. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2013 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Impairments in the Face-Processing Network in Developmental Prosopagnosia and Semantic Dementia

PubMed Central

Mendez, Mario F.; Ringman, John M.; Shapira, Jill S.

2015-01-01

Background Developmental prosopagnosia (DP) and semantic dementia (SD) may be the two most common neurologic disorders of face processing, but their main clinical and pathophysiologic differences have not been established. To identify those features, we compared patients with DP and SD. Methods Five patients with DP, five with right temporal-predominant SD, and ten normal controls underwent cognitive, visual perceptual, and face-processing tasks. Results Although the patients with SD were more cognitively impaired than those with DP, the two groups did not differ statistically on the visual perceptual tests. On the face-processing tasks, the DP group had difficulty with configural analysis and they reported relying on serial, feature-by-feature analysis or awareness of salient features to recognize faces. By contrast, the SD group had problems with person knowledge and made semantically related errors. The SD group had better face familiarity scores, suggesting a potentially useful clinical test for distinguishing SD from DP. Conclusions These two disorders of face processing represent clinically distinguishable disturbances along a right hemisphere face-processing network: DP, characterized by early configural agnosia for faces, and SD, characterized primarily by a multimodal person knowledge disorder. We discuss these preliminary findings in the context of the current literature on the face-processing network; recent studies suggest an additional right anterior temporal, unimodal face familiarity-memory deficit consistent with an “associative prosopagnosia.” PMID:26705265

P09.62 Towards individualized survival prediction in glioblastoma patients using machine learning methods

PubMed Central

Vera, L.; Pérez-Beteta, J.; Molina, D.; Borrás, J. M.; Benavides, M.; Barcia, J. A.; Velásquez, C.; Albillo, D.; Lara, P.; Pérez-García, V. M.

2017-01-01

Abstract Introduction: Machine learning methods are integrated in clinical research studies due to their strong capability to discover parameters having a high information content and their predictive combined potential. Several studies have been developed using glioblastoma patient’s imaging data. Many of them have focused on including large numbers of variables, mostly two-dimensional textural features and/or genomic data, regardless of their meaning or potential clinical relevance. Materials and methods: 193 glioblastoma patients were included in the study. Preoperative 3D magnetic resonance images were collected and semi-automatically segmented using an in-house software. After segmentation, a database of 90 parameters including geometrical and textural image-based measures together with patients’ clinical data (including age, survival, type of treatment, etc.) was constructed. The criterion for including variables in the study was that they had either shown individual impact on survival in single or multivariate analyses or have a precise clinical or geometrical meaning. These variables were used to perform several machine learning experiments. In a first set of computational cross-validation experiments based on regression trees, those attributes showing the highest information measures were extracted. In the second phase, more sophisticated learning methods were employed in order to validate the potential of the previous variables predicting survival. Concretely support vector machines, neural networks and sparse grid methods were used. Results: Variables showing high information measure in the first phase provided the best prediction results in the second phase. Specifically, patient age, Stupp regimen and a geometrical measure related with the irregularity of contrast-enhancing areas were the variables showing the highest information measure in the first stage. For the second phase, the combinations of patient age and Stupp regimen together with one tumor geometrical measure and one tumor heterogeneity feature reached the best quality prediction. Conclusions: Advanced machine learning methods identified the parameters with the highest information measure and survival predictive potential. The uninformed machine learning methods identified a novel feature measure with direct impact on survival. Used in combination with other previously known variables multi-indexes can be defined that can help in tumor characterization and prognosis prediction. Recent advances on the definition of those multi-indexes will be reported in the conference. Funding: James S. Mc. Donnell Foundation (USA) 21st Century Science Initiative in Mathematical and Complex Systems Approaches for Brain Cancer [Collaborative award 220020450 and planning grant 220020420], MINECO/FEDER [MTM2015-71200-R], JCCM [PEII-2014-031-P].

Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion

NASA Astrophysics Data System (ADS)

Ma, Ling; Guo, Rongrong; Zhang, Guoyi; Tade, Funmilayo; Schuster, David M.; Nieh, Peter; Master, Viraj; Fei, Baowei

2017-02-01

Automatic segmentation of the prostate on CT images has many applications in prostate cancer diagnosis and therapy. However, prostate CT image segmentation is challenging because of the low contrast of soft tissue on CT images. In this paper, we propose an automatic segmentation method by combining a deep learning method and multi-atlas refinement. First, instead of segmenting the whole image, we extract the region of interesting (ROI) to delete irrelevant regions. Then, we use the convolutional neural networks (CNN) to learn the deep features for distinguishing the prostate pixels from the non-prostate pixels in order to obtain the preliminary segmentation results. CNN can automatically learn the deep features adapting to the data, which are different from some handcrafted features. Finally, we select some similar atlases to refine the initial segmentation results. The proposed method has been evaluated on a dataset of 92 prostate CT images. Experimental results show that our method achieved a Dice similarity coefficient of 86.80% as compared to the manual segmentation. The deep learning based method can provide a useful tool for automatic segmentation of the prostate on CT images and thus can have a variety of clinical applications.

Problems and Processes in Medical Encounters: The CASES method of dialogue analysis

PubMed Central

Laws, M. Barton; Taubin, Tatiana; Bezreh, Tanya; Lee, Yoojin; Beach, Mary Catherine; Wilson, Ira B.

2013-01-01

Objective To develop methods to reliably capture structural and dynamic temporal features of clinical interactions. Methods Observational study of 50 audio-recorded routine outpatient visits to HIV specialty clinics, using innovative analytic methods. The Comprehensive Analysis of the Structure of Encounters System (CASES) uses transcripts coded for speech acts, then imposes larger-scale structural elements: threads – the problems or issues addressed; and processes within threads –basic tasks of clinical care labeled Presentation, Information, Resolution (decision making) and Engagement (interpersonal exchange). Threads are also coded for the nature of resolution. Results 61% of utterances are in presentation processes. Provider verbal dominance is greatest in information and resolution processes, which also contain a high proportion of provider directives. About half of threads result in no action or decision. Information flows predominantly from patient to provider in presentation processes, and from provider to patient in information processes. Engagement is rare. Conclusions In this data, resolution is provider centered; more time for patient participation in resolution, or interpersonal engagement, would have to come from presentation. Practice Implications Awareness of the use of time in clinical encounters, and the interaction processes associated with various tasks, may help make clinical communication more efficient and effective. PMID:23391684

Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process.

PubMed

Liu, Ding-Yun; Gan, Tao; Rao, Ni-Ni; Xing, Yao-Wen; Zheng, Jie; Li, Sang; Luo, Cheng-Si; Zhou, Zhong-Jun; Wan, Yong-Li

2016-08-01

The gastrointestinal endoscopy in this study refers to conventional gastroscopy and wireless capsule endoscopy (WCE). Both of these techniques produce a large number of images in each diagnosis. The lesion detection done by hand from the images above is time consuming and inaccurate. This study designed a new computer-aided method to detect lesion images. We initially designed an algorithm named joint diagonalisation principal component analysis (JDPCA), in which there are no approximation, iteration or inverting procedures. Thus, JDPCA has a low computational complexity and is suitable for dimension reduction of the gastrointestinal endoscopic images. Then, a novel image feature extraction method was established through combining the algorithm of machine learning based on JDPCA and conventional feature extraction algorithm without learning. Finally, a new computer-aided method is proposed to identify the gastrointestinal endoscopic images containing lesions. The clinical data of gastroscopic images and WCE images containing the lesions of early upper digestive tract cancer and small intestinal bleeding, which consist of 1330 images from 291 patients totally, were used to confirm the validation of the proposed method. The experimental results shows that, for the detection of early oesophageal cancer images, early gastric cancer images and small intestinal bleeding images, the mean values of accuracy of the proposed method were 90.75%, 90.75% and 94.34%, with the standard deviations (SDs) of 0.0426, 0.0334 and 0.0235, respectively. The areas under the curves (AUCs) were 0.9471, 0.9532 and 0.9776, with the SDs of 0.0296, 0.0285 and 0.0172, respectively. Compared with the traditional related methods, our method showed a better performance. It may therefore provide worthwhile guidance for improving the efficiency and accuracy of gastrointestinal disease diagnosis and is a good prospect for clinical application. Copyright © 2016 Elsevier B.V. All rights reserved.

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

The effects of TIS and MI on the texture features in ultrasonic fatty liver images

NASA Astrophysics Data System (ADS)

Zhao, Yuan; Cheng, Xinyao; Ding, Mingyue

2017-03-01

Nonalcoholic fatty liver disease (NAFLD) is prevalent and has a worldwide distribution now. Although ultrasound imaging technology has been deemed as the common method to diagnose fatty liver, it is not able to detect NAFLD in its early stage and limited by the diagnostic instruments and some other factors. B-scan image feature extraction of fatty liver can assist doctor to analyze the patient's situation and enhance the efficiency and accuracy of clinical diagnoses. However, some uncertain factors in ultrasonic diagnoses are often been ignored during feature extraction. In this study, the nonalcoholic fatty liver rabbit model was made and its liver ultrasound images were collected by setting different Thermal index of soft tissue (TIS) and mechanical index (MI). Then, texture features were calculated based on gray level co-occurrence matrix (GLCM) and the impacts of TIS and MI on these features were analyzed and discussed. Furthermore, the receiver operating characteristic (ROC) curve was used to evaluate whether each feature was effective or not when TIS and MI were given. The results showed that TIS and MI do affect the features extracted from the healthy liver, while the texture features of fatty liver are relatively stable. In addition, TIS set to 0.3 and MI equal to 0.9 might be a better choice when using a computer aided diagnosis (CAD) method for fatty liver recognition.

In vivo signatures of nonfluent/agrammatic primary progressive aphasia caused by FTLD pathology

PubMed Central

Caso, Francesca; Mandelli, Maria Luisa; Henry, Maya; Gesierich, Benno; Bettcher, Brianne M.; Ogar, Jennifer; Filippi, Massimo; Comi, Giancarlo; Magnani, Giuseppe; Sidhu, Manu; Trojanowski, John Q.; Huang, Eric J.; Grinberg, Lea T.; Miller, Bruce L.; Dronkers, Nina; Seeley, William W.

2014-01-01

Objective: To identify early cognitive and neuroimaging features of sporadic nonfluent/agrammatic variant of primary progressive aphasia (nfvPPA) caused by frontotemporal lobar degeneration (FTLD) subtypes. Methods: We prospectively collected clinical, neuroimaging, and neuropathologic data in 11 patients with sporadic nfvPPA with FTLD-tau (nfvPPA-tau, n = 9) or FTLD–transactive response DNA binding protein pathology of 43 kD type A (nfvPPA-TDP, n = 2). We analyzed patterns of cognitive and gray matter (GM) and white matter (WM) atrophy at presentation in the whole group and in each pathologic subtype separately. We also considered longitudinal clinical data. Results: At first evaluation, regardless of pathologic FTLD subtype, apraxia of speech (AOS) was the most common cognitive feature and atrophy involved the left posterior frontal lobe. Each pathologic subtype showed few distinctive features. At presentation, patients with nfvPPA-tau presented with mild to moderate AOS, mixed dysarthria with prominent hypokinetic features, clear agrammatism, and atrophy in the GM of the left posterior frontal regions and in left frontal WM. While speech and language deficits were prominent early, within 3 years of symptom onset, all patients with nfvPPA-tau developed significant extrapyramidal motor signs. At presentation, patients with nfvPPA-TDP had severe AOS, dysarthria with spastic features, mild agrammatism, and atrophy in left posterior frontal GM only. Selective mutism occurred early, when general neurologic examination only showed mild decrease in finger dexterity in the right hand. Conclusions: Clinical features in sporadic nfvPPA caused by FTLD subtypes relate to neurodegeneration of GM and WM in frontal motor speech and language networks. We propose that early WM atrophy in nfvPPA is suggestive of FTLD-tau pathology while early selective GM loss might be indicative of FTLD-TDP. PMID:24353332

Cytomorphological Spectrum of Thyroiditis: A Review of 110 Cases

PubMed Central

Nair, Rahul; Gambhir, Anushree; Kaur, Supreet; Pandey, Aditi; Shetty, Abhinav; Naragude, Piyusha

2018-01-01

Introduction Different types of thyroiditis may share some parallel clinical and biochemical features. Timely intervention can significantly reduce morbidity and mortality. Aim Aim of this study is to find the frequency of various thyroiditis, study the cytomorphological features and correlate with clinical findings including radiological findings, thyroid function test, and anti-thyroid peroxidase antibodies (Anti-TPO antibodies). Materials and Methods The study included consecutive 110 cases of thyroiditis. Detailed cytomorphological features were studied and correlated with ultrasonography findings, thyroid function test, anti-thyroid peroxidase antibodies (anti-TPO) and histopathological features where thyroidectomy specimens were received for histopathological examination. Results The majority were Hashimoto's thyroiditis (n = 100) and females (n = 103). Other forms of thyroiditis were Hashimoto's thyroiditis with colloid goiter (n = 5), De Quervain's thyroiditis (n = 3), and one case each of postpartum thyroiditis and Hashimoto's thyroiditis with associated malignancy. The majority of patients were in the age group of 21–40 (n = 70) and the majority (n = 73) had diffuse enlargement of thyroid. The majority of patients were hypothyroid (n = 52). The serum anti-TPO antibodies were elevated in 47 patients out of 71 patients. In the 48 patients who underwent ultrasonography, 38 were diagnosed as having thyroiditis. The most consistent cytomorphological features seen in fine-needle aspiration smears of Hashimoto's thyroiditis were increased background lymphocytes, lymphocytic infiltration of thyroid follicular cell clusters, and Hurthle cells. Conclusion The diagnostic cytological features in Hashimoto's thyroiditis are increased background lymphocytes, lymphocytic infiltration of thyroid follicular cell clusters, and Hurthle cells. FNAC remains the “Gold Standard” for diagnosing Hashimoto's thyroiditis. Clinical history, thyroid function, and biochemical parameters are the key for diagnosis of other forms of thyroiditis. PMID:29686830

Incidence and clinical variables associated with streptococcal throat infections: a prospective diagnostic cohort study

PubMed Central

Little, Paul; Hobbs, FD Richard; Mant, David; McNulty, Cliodna AM; Mullee, Mark

2012-01-01

Background Management of pharyngitis is commonly based on features which are thought to be associated with Lancefield group A beta-haemolytic streptococci (GABHS) but it is debatable which features best predict GABHS. Non-group A strains share major virulence factors with group A, but it is unclear how commonly they present and whether their presentation differs. Aim To assess the incidence and clinical variables associated with streptococcal infections. Design and setting Prospective diagnostic cohort study in UK primary care. Method The presence of pathogenic streptococci from throat swabs was assessed among patients aged ≥5 years presenting with acute sore throat. Results Pathogenic streptococci were found in 204/597 patients (34%, 95% CI = 31 to 38%): 33% (68/204) were non-group A streptococci, mostly C (n = 29), G (n = 18) and B (n = 17); rarely D (n = 3) and Streptococcus pneumoniae (n = 1). Patients presented with similar features whether the streptococci were group A or non-group A. The features best predicting A, C or G beta-haemolytic streptococci were patient’s assessment of severity (odds ratio [OR] for a bad sore throat 3.31, 95% CI = 1.24 to 8.83); doctors’ assessment of severity (severely inflamed tonsils OR 2.28, 95% CI = 1.39 to 3.74); absence of a bad cough (OR 2.73, 95% CI = 1.56 to 4.76), absence of a coryza (OR 1.54, 95% CI = 0.99 to 2.41); and moderately bad or worse muscle aches (OR 2.20, 95% CI = 1.41 to 3.42). Conclusion Non-group A strains commonly cause streptococcal sore throats, and present with similar symptomatic clinical features to group A streptococci. The best features to predict streptococcal sore throat presenting in primary care deserve revisiting. PMID:23211183

Learning Optimal Individualized Treatment Rules from Electronic Health Record Data

PubMed Central

Wang, Yuanjia; Wu, Peng; Liu, Ying; Weng, Chunhua; Zeng, Donglin

2016-01-01

Medical research is experiencing a paradigm shift from “one-size-fits-all” strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients. PMID:28503676

[The specific microbiological and clinical features of acute otitis media].

PubMed

Gurov, A V; Levina, Yu V; Guseva, A L; Elchueva, Z G; Efimova, S P; Gordienko, M V

The objective of the present study was to elucidate the specific features of the clinical course of acute otitis media as well as the peculiarities of the vestibular function and the microbial paysage associated with this pathological condition under the present-day conditions. The study included 135 patients presenting with acute otitis media (AOM) at different stages of the disease. The discharge obtained from the tympanic cavity of all the patients was examined with the use of polymerase chain reaction in real time, audiological and vestibulogical methods. The distinctive features of acute otitis medium associated with Streptococcus pneumoniae infection were found to be the intense pain syndrome with the symptoms of intoxication, well apparent inflammatory changes in the tympanic membrane as revealed by otoscopy, the increased frequency of sensorineural impairment of hearing, and the characteristic type B tympanometric curve. Typical of AOM associated with Haemophilus influenza infection are the mild pain syndrome, weak changes in the tympanic membrane as revealed by otoscopy, conductive hearing loss, and the type C tympanometric curve.

Irritability is associated with anxiety and greater severity, but not bipolar spectrum features, in major depressive disorder

PubMed Central

Perlis, Roy H.; Fava, Maurizio; Trivedi, Madhukar H.; Alpert, Jonathan; Luther, James F.; Wisniewski, Stephen R.; Rush, A. John

2009-01-01

Objective Irritability is common during major depressive episodes, but its clinical significance and overlap with symptoms of anxiety or bipolar disorder remains unclear. We examined clinical correlates of irritability in a confirmatory cohort of Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study participants with major depressive disorder (MDD). Method Logistic regression was used to identify features associated with presence of irritability on the clinician-rated Inventory of Depressive Symptomatology. Results Of 2,307 study participants, 1067(46%) reported irritability at least half the time during the preceding week; they were more likely to be female, to be younger, to experience greater depression severity and anxiety, and to report poorer quality of life, prior suicide attempts, and suicidal ideation. Bipolar spectrum features were not more common among those with irritability. Conclusion Irritable depression is not a distinct subtype of MDD, but irritability is associated with greater overall severity, anxiety comorbidity, and suicidality. PMID:19207123

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

Latent feature decompositions for integrative analysis of multi-platform genomic data

PubMed Central

Gregory, Karl B.; Momin, Amin A.; Coombes, Kevin R.; Baladandayuthapani, Veerabhadran

2015-01-01

Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to, a glioblastoma multiforme dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features. PMID:26146492

Some challenges with statistical inference in adaptive designs.

PubMed

Hung, H M James; Wang, Sue-Jane; Yang, Peiling

2014-01-01

Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.

Native T1 mapping of the heart - a pictorial review.

PubMed

Germain, Philippe; El Ghannudi, Soraya; Jeung, Mi-Young; Ohlmann, Patrick; Epailly, Eric; Roy, Catherine; Gangi, Afshin

2014-01-01

T1 mapping is now a clinically feasible method, providing pixel-wise quantification of the cardiac structure's T1 values. Beyond focal lesions, well depicted by late gadolinium enhancement sequences, it has become possible to discriminate diffuse myocardial alterations, previously not assessable by noninvasive means. The strength of this method includes the high reproducibility and immediate clinical applicability, even without the use of contrast media injection (native or pre-contrast T1). The two most important determinants of native T1 augmentation are (1) edema related to tissue water increase (recent infarction or inflammation) and (2) interstitial space increase related to fibrosis (infarction scar, cardiomyopathy) or to amyloidosis. Conversely, lipid (Anderson-Fabry) or iron overload diseases are responsible for T1 reduction. In this pictorial review, the main features provided by native T1 mapping are discussed and illustrated, with a special focus on the awaited clinical purpose of this unique, promising new method.

Spatial modeling and classification of corneal shape.

PubMed

Marsolo, Keith; Twa, Michael; Bullimore, Mark A; Parthasarathy, Srinivasan

2007-03-01

One of the most promising applications of data mining is in biomedical data used in patient diagnosis. Any method of data analysis intended to support the clinical decision-making process should meet several criteria: it should capture clinically relevant features, be computationally feasible, and provide easily interpretable results. In an initial study, we examined the feasibility of using Zernike polynomials to represent biomedical instrument data in conjunction with a decision tree classifier to distinguish between the diseased and non-diseased eyes. Here, we provide a comprehensive follow-up to that work, examining a second representation, pseudo-Zernike polynomials, to determine whether they provide any increase in classification accuracy. We compare the fidelity of both methods using residual root-mean-square (rms) error and evaluate accuracy using several classifiers: neural networks, C4.5 decision trees, Voting Feature Intervals, and Naïve Bayes. We also examine the effect of several meta-learning strategies: boosting, bagging, and Random Forests (RFs). We present results comparing accuracy as it relates to dataset and transformation resolution over a larger, more challenging, multi-class dataset. They show that classification accuracy is similar for both data transformations, but differs by classifier. We find that the Zernike polynomials provide better feature representation than the pseudo-Zernikes and that the decision trees yield the best balance of classification accuracy and interpretability.

An Innovative Approach to Automatically Detect and Interpret Salient Spatiotemporal Features of a Numeric Field: A Case Study in Electrocardiographic Imaging

NASA Astrophysics Data System (ADS)

Ironi, Liliana; Tentoni, Stefania

2009-08-01

The last decade has witnessed major advancements in the direct application of functional imaging techniques to several clinical contexts. Unfortunately, this is not the case of Electrocardiology. As a matter of fact, epicardial maps, which can hit electrical conduction pathologies that routine surface ECG's analysis may miss, can be obtained non invasively from body surface data through mathematical model-based reconstruction methods. But, their interpretation still requires highly specialized skills that belong to few experts. The automated detection of salient patterns in the map, grounded on the existing interpretation rationale, would therefore represent a major contribution towards the clinical use of such valuable tools, whose diagnostic potential is still largely unexploited. We focus on epicardial activation isochronal maps, which convey information about the heart electric function in terms of the depolarization wavefront kinematics. An approach grounded on the integration of a Spatial Aggregation (SA) method with concepts borrowed from Computational Geometry provides a computational framework to extract, from the given activation data, a few basic features that characterize the wavefront propagation, as well as a more specific set of features that identify an important class of heart rhythm pathologies, namely reentry arrhythmias due to block of conduction.

Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease.

PubMed

Iakovakis, Dimitrios; Hadjidimitriou, Stelios; Charisis, Vasileios; Bostantzopoulou, Sevasti; Katsarou, Zoe; Hadjileontiadis, Leontios J

2018-05-16

Parkinson's disease (PD) is a degenerative movement disorder causing progressive disability that severely affects patients' quality of life. While early treatment can produce significant benefits for patients, the mildness of many early signs combined with the lack of accessible high-frequency monitoring tools may delay clinical diagnosis. To meet this need, user interaction data from consumer technologies have recently been exploited towards unsupervised screening for PD symptoms in daily life. Similarly, this work proposes a method for detecting fine motor skills decline in early PD patients via analysis of patterns emerging from finger interaction with touchscreen smartphones during natural typing. Our approach relies on low-/higher-order statistical features of keystrokes timing and pressure variables, computed from short typing sessions. Features are fed into a two-stage multi-model classification pipeline that reaches a decision on the subject's status (PD patient/control) by gradually fusing prediction probabilities obtained for individual typing sessions and keystroke variables. This method achieved an AUC = 0.92 and 0.82/0.81 sensitivity/specificity (matched groups of 18 early PD patients/15 controls) with discriminant features plausibly correlating with clinical scores of relevant PD motor symptoms. These findings suggest an improvement over similar approaches, thereby constituting a further step towards unobtrusive early PD detection from routine activities.

The development of agoraphobia is associated with the symptoms and location of a patient's first panic attack

PubMed Central

2012-01-01

Background The place where a patient experiences his/her first panic attack (FPA) may be related to their agoraphobia later in life. However, no investigations have been done into the clinical features according to the place where the FPA was experienced. In particular, there is an absence of detailed research examining patients who experienced their FPA at home. In this study, patients were classified by the location of their FPA and the differences in their clinical features were explored (e.g., symptoms of FPA, frequency of agoraphobia, and severity of FPA). Methods The subjects comprised 830 panic disorder patients who were classified into 5 groups based on the place of their FPA (home, school/office, driving a car, in a public transportation vehicle, outside of home), The clinical features of these patients were investigated. Additionally, for panic disorder patients with agoraphobia at their initial clinic visit, the clinical features of patients who experienced their FPA at home were compared to those who experienced their attack elsewhere. Results In comparison of the FPAs of the 5 groups, significant differences were seen among the 7 descriptors (sex ratio, drinking status, smoking status, severity of the panic attack, depression score, ratio of agoraphobia, and degree of avoidance behavior) and 4 symptoms (sweating, chest pain, feeling dizzy, and fear of dying). The driving and public transportation group patients showed a higher incidence of co-morbid agoraphobia than did the other groups. Additionally, for panic disorder patients with co-morbid agoraphobia, the at-home group had a higher frequency of fear of dying compared to the patients in the outside-of-home group and felt more severe distress elicited by their FPA. Conclusion The results of this study suggest that the clinical features of panic disorder patients vary according to the place of their FPA. The at-home group patients experienced "fear of dying" more frequently and felt more distress during their FPA than did the subjects in the other groups. These results indicate that patients experiencing their FPA at home should be treated with a focus on the fear and distress elicited by the attack. PMID:22494552

Computer aided analysis of gait patterns in patients with acute anterior cruciate ligament injury.

PubMed

Christian, Josef; Kröll, Josef; Strutzenberger, Gerda; Alexander, Nathalie; Ofner, Michael; Schwameder, Hermann

2016-03-01

Gait analysis is a useful tool to evaluate the functional status of patients with anterior cruciate ligament injury. Pattern recognition methods can be used to automatically assess walking patterns and objectively support clinical decisions. This study aimed to test a pattern recognition system for analyzing kinematic gait patterns of recently anterior cruciate ligament injured patients and for evaluating the effects of a therapeutic treatment. Gait kinematics of seven male patients with an acute unilateral anterior cruciate ligament rupture and seven healthy males were recorded. A support vector machine was trained to distinguish the groups. Principal component analysis and recursive feature elimination were used to extract features from 3D marker trajectories. A Classifier Oriented Gait Score was defined as a measure of gait quality. Visualizations were used to allow functional interpretations of characteristic group differences. The injured group was evaluated by the system after a therapeutic treatment. The results were compared against a clinical rating of the patients' gait. Cross validation yielded 100% accuracy. After the treatment the score improved significantly (P<0.01) as well as the clinical rating (P<0.05). The visualizations revealed characteristic kinematic features, which differentiated between the groups. The results show that gait alterations in the early phase after anterior cruciate ligament injury can be detected automatically. The results of the automatic analysis are comparable with the clinical rating and support the validity of the system. The visualizations allow interpretations on discriminatory features and can facilitate the integration of the results into the diagnostic process. Copyright © 2016 Elsevier Ltd. All rights reserved.

Health System Features That Enhance Access to Comprehensive Primary Care for Women Living with HIV in High-Income Settings: A Systematic Mixed Studies Review.

PubMed

O'Brien, Nadia; Hong, Quan Nha; Law, Susan; Massoud, Sarah; Carter, Allison; Kaida, Angela; Loutfy, Mona; Cox, Joseph; Andersson, Neil; de Pokomandy, Alexandra

2018-04-01

Women living with HIV in high-income settings continue to experience modifiable barriers to care. We sought to determine the features of care that facilitate access to comprehensive primary care, inclusive of HIV, comorbidity, and sexual and reproductive healthcare. Using a systematic mixed studies review design, we reviewed qualitative, mixed methods, and quantitative studies identified in Ovid MEDLINE, EMBASE, and CINAHL databases (January 2000 to August 2017). Eligibility criteria included women living with HIV; high-income countries; primary care; and healthcare accessibility. We performed a thematic synthesis using NVivo. After screening 3466 records, we retained 44 articles and identified 13 themes. Drawing on a social-ecological framework on engagement in HIV care, we situated the themes across three levels of the healthcare system: care providers, clinical care environments, and social and institutional factors. At the care provider level, features enhancing access to comprehensive primary care included positive patient-provider relationships and availability of peer support, case managers, and/or nurse navigators. Within clinical care environments, facilitators to care were appointment reminder systems, nonidentifying clinic signs, women and family spaces, transportation services, and coordination of care to meet women's HIV, comorbidity, and sexual and reproductive healthcare needs. Finally, social and institutional factors included healthcare insurance, patient and physician education, and dispelling HIV-related stigma. This review highlights several features of care that are particularly relevant to the care-seeking experience of women living with HIV. Improving their health through comprehensive care requires a variety of strategies at the provider, clinic, and greater social and institutional levels.

Waardenburg syndrome type I: Dental phenotypes and genetic analysis of an extended family

PubMed Central

de Aquino, Sibele-Nascimento; Paranaíba, Lívia-Maris-R.; Gomes, Andreia; dos-Santos-Neto, Pedro; Coletta, Ricardo-D.; Cardoso, Aline-Francoise; Frota, Ana-Cláudia; Martelli-Júnior, Hercílio

2016-01-01

Background The aim of this study was to describe the pattern of inheritance and the clinical features in a large family with Waardenburg syndrome type I (WS1), detailing the dental abnormalities and screening for PAX3 mutations. Material and Methods To characterize the pattern of inheritance and clinical features, 29 family members were evaluated by dermatologic, ophthalmologic, otorhinolaryngologic and orofacial examination. Molecular analysis of the PAX3 gene was performed. Results The pedigree of the family,including the last four generations, was constructed and revealed non-consanguineous marriages. Out of 29 descendants, 16 family members showed features of WS1, with 9 members showing two major criteria indicative of WS1. Five patients showed white forelock and iris hypopigmentation, and four showed dystopia canthorum and iris hypopigmentation. Two patients had hearing loss. Dental abnormalities were identified in three family members, including dental agenesis, conical teeth and taurodontism. Sequencing analysis failed to identify mutations in the PAX3 gene. Conclusions These results confirm that WS1 was transmitted in this family in an autosomal dominant pattern with variable expressivity and high penetrance. The presence of dental manifestations, especially tooth agenesis and conical teeth which resulted in considerable aesthetic impact on affected individuals was a major clinical feature. Clinical relevance: This article reveals the presence of well-defined dental changes associated with WS1 and tries to establish a possible association between these two entities showing a new spectrum of WS1. Key words:Waardenburg syndrome, hearing loss, oral manifestations, mutation. PMID:27031059

Altered spontaneous brain activity pattern in patients with late monocular blindness in middle-age using amplitude of low-frequency fluctuation: a resting-state functional MRI study

PubMed Central

Li, Qing; Huang, Xin; Ye, Lei; Wei, Rong; Zhang, Ying; Zhong, Yu-Lin; Jiang, Nan; Shao, Yi

2016-01-01

Objective Previous reports have demonstrated significant brain activity changes in bilateral blindness, whereas brain activity changes in late monocular blindness (MB) at rest are not well studied. Our study aimed to investigate spontaneous brain activity in patients with late middle-aged MB using the amplitude of low-frequency fluctuation (ALFF) method and their relationship with clinical features. Methods A total of 32 patients with MB (25 males and 7 females) and 32 healthy control (HC) subjects (25 males and 7 females), similar in age, sex, and education, were recruited for the study. All subjects were performed with resting-state functional magnetic resonance imaging scanning. The ALFF method was applied to evaluate spontaneous brain activity. The relationships between the ALFF signal values in different brain regions and clinical features in MB patients were investigated using correlation analysis. Results Compared with HCs, the MB patients had marked lower ALFF values in the left cerebellum anterior lobe, right parahippocampal gyrus, right cuneus, left precentral gyrus, and left paracentral lobule, but higher ALFF values in the right middle frontal gyrus, left middle frontal gyrus, and left supramarginal gyrus. However, there was no linear correlation between the mean ALFF signal values in brain regions and clinical manifestations in MB patients. Conclusion There were abnormal spontaneous activities in many brain regions including vision and vision-related regions, which might indicate the neuropathologic mechanisms of vision loss in the MB patients. Meanwhile, these brain activity changes might be used as a useful clinical indicator for MB. PMID:27980398

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

Catatonia in Psychotic Patients: Clinical Features and Treatment Response

PubMed Central

England, Mary L.; Öngür, Dost; Konopaske, Glenn T.; Karmacharya, Rakesh

2012-01-01

We report clinical features and treatment response in 25 patients with catatonia admitted to an inpatient psychiatric unit specializing in psychotic disorders. ECT, benzodiazepines, and clozapine had beneficial effects on catatonic features, while typical antipsychotics resulted in clinical worsening. PMID:21677256

Clinical Features and Extraintestinal Manifestations of Crohn Disease in Children

PubMed Central

Lee, Young Ah; Chun, Peter; Hwang, Eun Ha; Mun, Sang Wook; Lee, Yeoun Joo

2016-01-01

Purpose The aim of this study was to investigate the clinical features and extraintestinal manifestations (EIMs) of Crohn disease (CD) in Korean pediatric patients. Methods The medical records of 73 children diagnosed with CD were retrospectively reviewed. Data regarding baseline demographic and clinical characteristics, including CD phenotype at diagnosis based on the Montreal classification, and clinical features and course of EIMs were investigated. Results Fifty-two (71.2%) of the patients were males. The mean age of the patients was 12.5 years. The mean follow-up period was 3.4 years. The disease location was ileal in 3 (4.1%) of the patients, colonic in 13 (17.8%), ileocolonic in 56 (76.7%). The clinical behavior was inflammatory in 62 (84.9%) of the patients, stricturing in 8 (11.0%), and penetrating in 3 (4.1%). Perianal abscesses or fistulas were found in 37 (50.7%) of the patients. EIMs observed during the study period were anal skin tag in 25 patients (34.2%), hypertransaminasemia in 20 (27.4%), peripheral arthritis in 2 (2.7%), erythema nodosum in 2 (2.7%), vulvitis in 1 (1.4%), uveitis in 1 (1.4%), and pulmonary thromboembolism in 1 (1.4%). Conclusion Perianal diseases and manifestations were present in more than half of Korean pediatric CD patients at diagnosis. Inspection of the anus should be mandatory in Korean children with suspicious CD, as perianal fistulas, abscesses, and anal skin tags may be the first clue to the diagnosis of CD. PMID:28090468

A CTSA Agenda to Advance Methods for Comparative Effectiveness Research

PubMed Central

Helfand, Mark; Tunis, Sean; Whitlock, Evelyn P.; Pauker, Stephen G.; Basu, Anirban; Chilingerian, Jon; Harrell Jr., Frank E.; Meltzer, David O.; Montori, Victor M.; Shepard, Donald S.; Kent, David M.

2011-01-01

Abstract Clinical research needs to be more useful to patients, clinicians, and other decision makers. To meet this need, more research should focus on patient‐centered outcomes, compare viable alternatives, and be responsive to individual patients’ preferences, needs, pathobiology, settings, and values. These features, which make comparative effectiveness research (CER) fundamentally patient‐centered, challenge researchers to adopt or develop methods that improve the timeliness, relevance, and practical application of clinical studies. In this paper, we describe 10 priority areas that address 3 critical needs for research on patient‐centered outcomes (PCOR): (1) developing and testing trustworthy methods to identify and prioritize important questions for research; (2) improving the design, conduct, and analysis of clinical research studies; and (3) linking the process and outcomes of actual practice to priorities for research on patient‐centered outcomes. We argue that the National Institutes of Health, through its clinical and translational research program, should accelerate the development and refinement of methods for CER by linking a program of methods research to the broader portfolio of large, prospective clinical and health system studies it supports. Insights generated by this work should be of enormous value to PCORI and to the broad range of organizations that will be funding and implementing CER. Clin Trans Sci 2011; Volume 4: 188–198 PMID:21707950

A Combination of Molecular Markers and Clinical Features Improve the Classification of Pancreatic Cysts

PubMed Central

Springer, Simeon; Wang, Yuxuan; Molin, Marco Dal; Masica, David L.; Jiao, Yuchen; Kinde, Isaac; Blackford, Amanda; Raman, Siva P.; Wolfgang, Christopher L.; Tomita, Tyler; Niknafs, Noushin; Douville, Christopher; Ptak, Janine; Dobbyn, Lisa; Allen, Peter J.; Klimstra, David S.; Schattner, Mark A.; Schmidt, C. Max; Yip-Schneider, Michele; Cummings, Oscar W.; Brand, Randall E.; Zeh, Herbert J.; Singhi, Aatur D.; Scarpa, Aldo; Salvia, Roberto; Malleo, Giuseppe; Zamboni, Giuseppe; Falconi, Massimo; Jang, Jin-Young; Kim, Sun-Whe; Kwon, Wooil; Hong, Seung-Mo; Song, Ki-Byung; Kim, Song Cheol; Swan, Niall; Murphy, Jean; Geoghegan, Justin; Brugge, William; Fernandez-Del Castillo, Carlos; Mino-Kenudson, Mari; Schulick, Richard; Edil, Barish H.; Adsay, Volkan; Paulino, Jorge; van Hooft, Jeanin; Yachida, Shinichi; Nara, Satoshi; Hiraoka, Nobuyoshi; Yamao, Kenji; Hijioka, Susuma; van der Merwe, Schalk; Goggins, Michael; Canto, Marcia Irene; Ahuja, Nita; Hirose, Kenzo; Makary, Martin; Weiss, Matthew J.; Cameron, John; Pittman, Meredith; Eshleman, James R.; Diaz, Luis A.; Papadopoulos, Nickolas; Kinzler, Kenneth W.; Karchin, Rachel; Hruban, Ralph H.; Vogelstein, Bert; Lennon, Anne Marie

2016-01-01

Background & Aims The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients. Methods We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid-pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53 and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers were compared with that of clinical markers, and a combination of molecular and clinical markers. Results We identified molecular markers and clinical features that classified cyst type with 90%–100% sensitivity and 92%–98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery, and could therefore reduce the number of unnecessary operations by 91%. Conclusions We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery. PMID:26253305

Hepatitis Diagnosis Using Facial Color Image

NASA Astrophysics Data System (ADS)

Liu, Mingjia; Guo, Zhenhua

Facial color diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experi-ence-based nature, traditional facial color diagnosis has a very limited application in clinical medicine. To circumvent the subjective and qualitative problems of facial color diagnosis of Traditional Chinese Medicine, in this paper, we present a novel computer aided facial color diagnosis method (CAFCDM). The method has three parts: face Image Database, Image Preprocessing Module and Diagnosis Engine. Face Image Database is carried out on a group of 116 patients affected by 2 kinds of liver diseases and 29 healthy volunteers. The quantitative color feature is extracted from facial images by using popular digital image processing techni-ques. Then, KNN classifier is employed to model the relationship between the quantitative color feature and diseases. The results show that the method can properly identify three groups: healthy, severe hepatitis with jaundice and severe hepatitis without jaundice with accuracy higher than 73%.

WE-EF-210-08: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in Ultrasound Images Using Patch-Based Anatomical Feature

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

Yang, X; Rossi, P; Jani, A

Purpose: Transrectal ultrasound (TRUS) is the standard imaging modality for the image-guided prostate-cancer interventions (e.g., biopsy and brachytherapy) due to its versatility and real-time capability. Accurate segmentation of the prostate plays a key role in biopsy needle placement, treatment planning, and motion monitoring. As ultrasound images have a relatively low signal-to-noise ratio (SNR), automatic segmentation of the prostate is difficult. However, manual segmentation during biopsy or radiation therapy can be time consuming. We are developing an automated method to address this technical challenge. Methods: The proposed segmentation method consists of two major stages: the training stage and the segmentation stage.more » During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, because these training images have been mapped to the new patient’ images, and the more informative anatomical features are selected to train the kernel support vector machine (KSVM). During the segmentation stage, the selected anatomical features are extracted from newly acquired image as the input of the well-trained KSVM and the output of this trained KSVM is the segmented prostate of this patient. Results: This segmentation technique was validated with a clinical study of 10 patients. The accuracy of our approach was assessed using the manual segmentation. The mean volume Dice Overlap Coefficient was 89.7±2.3%, and the average surface distance was 1.52 ± 0.57 mm between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D ultrasound-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning framework, demonstrated its clinical feasibility, and validated its accuracy with manual segmentation (gold standard). This segmentation technique could be a useful tool for image-guided interventions in prostate-cancer diagnosis and treatment. This research is supported in part by DOD PCRP Award W81XWH-13-1-0269, and National Cancer Institute (NCI) Grant CA114313.« less

A practical approach to Sasang constitutional diagnosis using vocal features

PubMed Central

2013-01-01

Background Sasang constitutional medicine (SCM) is a type of tailored medicine that divides human beings into four Sasang constitutional (SC) types. Diagnosis of SC types is crucial to proper treatment in SCM. Voice characteristics have been used as an essential clue for diagnosing SC types. In the past, many studies tried to extract quantitative vocal features to make diagnosis models; however, these studies were flawed by limited data collected from one or a few sites, long recording time, and low accuracy. We propose a practical diagnosis model having only a few variables, which decreases model complexity. This in turn, makes our model appropriate for clinical applications. Methods A total of 2,341 participants’ voice recordings were used in making a SC classification model and to test the generalization ability of the model. Although the voice data consisted of five vowels and two repeated sentences per participant, we used only the sentence part for our study. A total of 21 features were extracted, and an advanced feature selection method—the least absolute shrinkage and selection operator (LASSO)—was applied to reduce the number of variables for classifier learning. A SC classification model was developed using multinomial logistic regression via LASSO. Results We compared the proposed classification model to the previous study, which used both sentences and five vowels from the same patient’s group. The classification accuracies for the test set were 47.9% and 40.4% for male and female, respectively. Our result showed that the proposed method was superior to the previous study in that it required shorter voice recordings, is more applicable to practical use, and had better generalization performance. Conclusions We proposed a practical SC classification method and showed that our model having fewer variables outperformed the model having many variables in the generalization test. We attempted to reduce the number of variables in two ways: 1) the initial number of candidate features was decreased by considering shorter voice recording, and 2) LASSO was introduced for reducing model complexity. The proposed method is suitable for an actual clinical environment. Moreover, we expect it to yield more stable results because of the model’s simplicity. PMID:24200041

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

NASA Astrophysics Data System (ADS)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

Comparative study of protoporphyrin IX fluorescence image enhancement methods to improve an optical imaging system for oral cancer detection

NASA Astrophysics Data System (ADS)

Jiang, Ching-Fen; Wang, Chih-Yu; Chiang, Chun-Ping

2011-07-01

Optoelectronics techniques to induce protoporphyrin IX fluorescence with topically applied 5-aminolevulinic acid on the oral mucosa have been developed to noninvasively detect oral cancer. Fluorescence imaging enables wide-area screening for oral premalignancy, but the lack of an adequate fluorescence enhancement method restricts the clinical imaging application of these techniques. This study aimed to develop a reliable fluorescence enhancement method to improve PpIX fluorescence imaging systems for oral cancer detection. Three contrast features, red-green-blue reflectance difference, R/B ratio, and R/G ratio, were developed first based on the optical properties of the fluorescence images. A comparative study was then carried out with one negative control and four biopsy confirmed clinical cases to validate the optimal image processing method for the detection of the distribution of malignancy. The results showed the superiority of the R/G ratio in terms of yielding a better contrast between normal and neoplastic tissue, and this method was less prone to errors in detection. Quantitative comparison with the clinical diagnoses in the four neoplastic cases showed that the regions of premalignancy obtained using the proposed method accorded with the expert's determination, suggesting the potential clinical application of this method for the detection of oral cancer.

A generic minimization random allocation and blinding system on web.

PubMed

Cai, Hongwei; Xia, Jielai; Xu, Dezhong; Gao, Donghuai; Yan, Yongping

2006-12-01

Minimization is a dynamic randomization method for clinical trials. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the validity of conventional analyses and its complexity in implementation. However, both the statistical and clinical validity of minimization were demonstrated in recent studies. Minimization random allocation system integrated with blinding function that could facilitate the implementation of this method in general clinical trials has not been reported. SYSTEM OVERVIEW: The system is a web-based random allocation system using Pocock and Simon minimization method. It also supports multiple treatment arms within a trial, multiple simultaneous trials, and blinding without further programming. This system was constructed with generic database schema design method, Pocock and Simon minimization method and blinding method. It was coded with Microsoft Visual Basic and Active Server Pages (ASP) programming languages. And all dataset were managed with a Microsoft SQL Server database. Some critical programming codes were also provided. SIMULATIONS AND RESULTS: Two clinical trials were simulated simultaneously to test the system's applicability. Not only balanced groups but also blinded allocation results were achieved in both trials. Practical considerations for minimization method, the benefits, general applicability and drawbacks of the technique implemented in this system are discussed. Promising features of the proposed system are also summarized.

Attention-Deficit/hyperactivity Disorder and Comorbidities in 18 Paisa Colombian Multigenerational Families.

ERIC Educational Resources Information Center

Palacio, Juan D.; Castellanos, F. Xavier; Pineda, David A.; Lopera, Francisco; Arcos-Burgos, Mauricio; Quiroz, Yakeel T.; Henao, Gloria C.; Puerta, Isabel C.; Ramirez, Dora L.; Rapoport, Judith L.; Bailey-Wilson, Joan; Berg, Kate; Muenke, Maximilian

2004-01-01

Objective: Eighteen extended multigenerational families were recruited from the genetically isolated Paisa community in Colombia to conduct genetic studies of attention-deficit/hyperactivity disorder (ADHD). This report describes the inclusion strategy and clinical features of participants to facilitate comparisons with other data sets. Method:…

Jervell and Lange-Nielsen Syndrome (Long QT Syndrome).

ERIC Educational Resources Information Center

Hulbert, T. P.

1994-01-01

Clinical features, pathogenetic hypotheses, and symptoms of the cardio-auditory or surdo-cardiac disorder first reported by Jervell and Lange-Nielsen are described, and methods of diagnosis and treatment are presented, to alert teachers and other professionals to potentially life-threatening symptoms they may observe when working with deaf and…

Heterogeneity in Learning Style in Asperger Syndrome and High-Functioning Autism

ERIC Educational Resources Information Center

Tsatsanis, Katharine D.

2004-01-01

Although children and adolescents with high-functioning autism and Asperger syndrome present with some similar clinical features and challenges, heterogeneity of learning style coupled with the predominance of specific "packages" of materials and methods tends to understate the need for individualization when designing an educational and/or a…

[The comparison of clinical features and laboratory indexes between flat descending hearing loss and total hearing loss].

PubMed

Wang, R L; Zhang, D M

2017-12-20

Objective: To discuss similarities and differences in clinical features and laboratory indexes between patients with flat descending type sudden hearing loss and those with total hearing loss. Method: The clinical data of 123 patients with full frequencies hearing loss were retrospectively analyzed. The differences in clinical features and laboratory tests(platelet, coagulation series, D-dimer, blood lipids, hemorheology) between patients with flat descending hearing loss and those with total hearing loss were analyzed by gender, age and ear side, treatment time, concomitant symptom (tinnitus, dizziness), original underlying diseases (hypertension, diabetes), etc. Result: In the clinical features,among 51 flat descending cases, the ratio of male and female was 2.401:1; among 72 total hearing loss cases, the ratio of men and women ratio was 1.058:1 ( P <0.05). Among two groups of patients,the majority received treatment within 7 days, among whom 66.7% were flat descending population, and 83.3% were total hearing loss population ( P <0.05). Flat descending population with dizziness only accounted for 35.3% while this figure was up to 70.8% when it came to total hearing loss patients ( P <0.01). Two groups showed no differences in age, ear side, tinnitus, the original underlying diseases (hypertension, diabetes). In the laboratory tests, the total hearing loss population overtopped the plat descending population in PLT and PCT ( P <0.05), while falling below the plat descending population in APTT ( P <0.01). Two groups showed no differences in other indicators of platelet and coagulation series and laboratory data of D-dimer, blood lipids, hemorheology. Conclusion: Compared with flat descending sudden hearing loss, sudden total hearing loss more frequently happened to females who also were accompanied by dizziness. The treatment rate within 7 days was high and the patients with hypercoagulable state accounted for a higher proportion. Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.

Survey on multisensory feedback virtual reality dental training systems.

PubMed

Wang, D; Li, T; Zhang, Y; Hou, J

2016-11-01

Compared with traditional dental training methods, virtual reality training systems integrated with multisensory feedback possess potentials advantages. However, there exist many technical challenges in developing a satisfactory simulator. In this manuscript, we systematically survey several current dental training systems to identify the gaps between the capabilities of these systems and the clinical training requirements. After briefly summarising the components, functions and unique features of each system, we discuss the technical challenges behind these systems including the software, hardware and user evaluation methods. Finally, the clinical requirements of an ideal dental training system are proposed. Future research/development areas are identified based on an analysis of the gaps between current systems and clinical training requirements. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Optical ensemble analysis of intraocular lens performance through a simulated clinical trial with ZEMAX.

PubMed

Zhao, Huawei

2009-01-01

A ZEMAX model was constructed to simulate a clinical trial of intraocular lenses (IOLs) based on a clinically oriented Monte Carlo ensemble analysis using postoperative ocular parameters. The purpose of this model is to test the feasibility of streamlining and optimizing both the design process and the clinical testing of IOLs. This optical ensemble analysis (OEA) is also validated. Simulated pseudophakic eyes were generated by using the tolerancing and programming features of ZEMAX optical design software. OEA methodology was verified by demonstrating that the results of clinical performance simulations were consistent with previously published clinical performance data using the same types of IOLs. From these results we conclude that the OEA method can objectively simulate the potential clinical trial performance of IOLs.

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

PubMed Central

Qian, Xiaohua; Tan, Hua; Zhang, Jian; Zhao, Weilin; Chan, Michael D.; Zhou, Xiaobo

2016-01-01

Purpose: Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose. Methods: To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system. Results: The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance. Conclusions: The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP. PMID:27806598

Progressive biparietal atrophy: an atypical presentation of Alzheimer's disease.

PubMed Central

Ross, S J; Graham, N; Stuart-Green, L; Prins, M; Xuereb, J; Patterson, K; Hodges, J R

1996-01-01

OBJECTIVES: To define the clinical, neuropsychological, and radiological features of bilateral parietal lobe atrophy. METHODS: Four patients underwent a comprehensive longitudinal neuropsychological assessment, as well as MRI and HMPAO-SPECT. RESULTS: The consistent findings in the patients were early visuospatial problems, agraphia of a predominantly peripheral (or apraxic) type, and difficulty with bimanual tasks, all of which outweighted deficits in memory and language until later in the course of the illness. As the disease progressed, impairments in the phonological aspects of language and in auditory-verbal short term memory were often striking, perhaps reflecting spread from the parietal lobe to perisylvian language areas. Three patients went on to develop a global dementia and fulfilled the criteria for a clinical diagnosis of probable Alzheimer's disease; the fourth patient has only recently been identified. Neuroimaging disclosed bilateral parietal lobe atrophy (MRI) and hypoperfusion (SPECT), which was out of proportion to that seen elsewhere in the brain. One patient has died and had pathologically confirmed Alzheimer's disease with particular concentration in both superior parietal lobes. CONCLUSIONS: Bilateral biparietal atrophy is a recognisable clinical syndrome which can be the presenting feature of Alzheimer's disease. Although the label "posterior cortical atrophy" has been applied to such cases, review of the medical literature suggests that this broad rubric actually consists of two main clinical syndromes with features reflecting involvement of the occipitotemporal (ventral) and biparietal (dorsal) cortical areas respectively. Images PMID:8890778

Trends in Diagnosis and Clinical Features of Melanoma in situ in U.S. Men and Women: a prospective, observational study

PubMed Central

Wei, Erin X.; Qureshi, Abrar A.; Han, Jiali; Li, Tricia Y.; Cho, Eunyoung; Lin, Jennifer Y.; Li, Wen-Qing

2016-01-01

Background The incidence of melanoma in situ is rising, but little is known about its characteristics. Objective To determine trends in diagnosis and clinical features of melanoma in situ. Methods Incident cases of melanoma were collected prospectively from the Nurses’ Health Study from 1976–2010 and Health Professionals Follow-up Study from 1986–2010. Results MIS incidence increased from 2 to 42 per 100,000-person-year (100KPY) among women, and from 11 to 73 per 100KPY among men, exceeding the rate of increase of invasive melanomas. Melanoma mortality initially increased during the follow-up period then plateaued. Men were more likely than women to develop in situ melanomas on upper half of the body (p<0.001). Invasive melanomas were diagnosed at a younger age than melanoma in situ (p<0.001), and were more likely to be found on the lower extremities than in situ melanomas (p<0.001). Limitations This is a strictly descriptive study without examination into mechanisms. Conclusion We found epidemiologic and clinical differences in in situ and invasive melanomas, which support further examination into the variations in etiologic pathways. The lack of improvement in mortality despite increase in detection of in situ relative to invasive lesions further highlight the need to improve invasive melanoma-specific clinical screening features. PMID:27436155

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

PubMed Central

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study.

PubMed

Del Fiol, Guilherme; Michelson, Matthew; Iorio, Alfonso; Cotoi, Chris; Haynes, R Brian

2018-06-25

A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001). Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis. ©Guilherme Del Fiol, Matthew Michelson, Alfonso Iorio, Chris Cotoi, R Brian Haynes. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.06.2018.

A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.

PubMed

Novas, Romulo Bourget; Fazan, Valeria Paula Sassoli; Felipe, Joaquim Cezar

2016-02-01

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

ClinicalTrials.gov Turns 10! | NIH MedlinePlus the Magazine

MedlinePlus

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Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Software for MR image overlay guided needle insertions: the clinical translation process

NASA Astrophysics Data System (ADS)

Ungi, Tamas; U-Thainual, Paweena; Fritz, Jan; Iordachita, Iulian I.; Flammang, Aaron J.; Carrino, John A.; Fichtinger, Gabor

2013-03-01

PURPOSE: Needle guidance software using augmented reality image overlay was translated from the experimental phase to support preclinical and clinical studies. Major functional and structural changes were needed to meet clinical requirements. We present the process applied to fulfill these requirements, and selected features that may be applied in the translational phase of other image-guided surgical navigation systems. METHODS: We used an agile software development process for rapid adaptation to unforeseen clinical requests. The process is based on iterations of operating room test sessions, feedback discussions, and software development sprints. The open-source application framework of 3D Slicer and the NA-MIC kit provided sufficient flexibility and stable software foundations for this work. RESULTS: All requirements were addressed in a process with 19 operating room test iterations. Most features developed in this phase were related to workflow simplification and operator feedback. CONCLUSION: Efficient and affordable modifications were facilitated by an open source application framework and frequent clinical feedback sessions. Results of cadaver experiments show that software requirements were successfully solved after a limited number of operating room tests.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

PubMed

Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

2018-01-19

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments

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

Chang, Yongjun; Paul, Anjan Kumar; Kim, Namkug, E-mail: namkugkim@gmail.com

Purpose: To develop a semiautomated computer-aided diagnosis (CAD) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. Methods: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid CAD software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrencemore » matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of CAD with visual inspection by expert radiologists based on established gold standards. Results: Most univariate features for this proposed CAD system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed CAD system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, “axial ratio” and “max probability” in axial images were most frequently included in the optimal feature sets for the authors’ proposed CAD system, while “shape” and “calcification” in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed CAD system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. Conclusions: The use of thyroid CAD to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid CAD might be considered a viable way to generate a second opinion for radiologists in clinical practice.« less

Usability study of clinical exome analysis software: top lessons learned and recommendations.

PubMed

Shyr, Casper; Kushniruk, Andre; Wasserman, Wyeth W

2014-10-01

New DNA sequencing technologies have revolutionized the search for genetic disruptions. Targeted sequencing of all protein coding regions of the genome, called exome analysis, is actively used in research-oriented genetics clinics, with the transition to exomes as a standard procedure underway. This transition is challenging; identification of potentially causal mutation(s) amongst ∼10(6) variants requires specialized computation in combination with expert assessment. This study analyzes the usability of user interfaces for clinical exome analysis software. There are two study objectives: (1) To ascertain the key features of successful user interfaces for clinical exome analysis software based on the perspective of expert clinical geneticists, (2) To assess user-system interactions in order to reveal strengths and weaknesses of existing software, inform future design, and accelerate the clinical uptake of exome analysis. Surveys, interviews, and cognitive task analysis were performed for the assessment of two next-generation exome sequence analysis software packages. The subjects included ten clinical geneticists who interacted with the software packages using the "think aloud" method. Subjects' interactions with the software were recorded in their clinical office within an urban research and teaching hospital. All major user interface events (from the user interactions with the packages) were time-stamped and annotated with coding categories to identify usability issues in order to characterize desired features and deficiencies in the user experience. We detected 193 usability issues, the majority of which concern interface layout and navigation, and the resolution of reports. Our study highlights gaps in specific software features typical within exome analysis. The clinicians perform best when the flow of the system is structured into well-defined yet customizable layers for incorporation within the clinical workflow. The results highlight opportunities to dramatically accelerate clinician analysis and interpretation of patient genomic data. We present the first application of usability methods to evaluate software interfaces in the context of exome analysis. Our results highlight how the study of user responses can lead to identification of usability issues and challenges and reveal software reengineering opportunities for improving clinical next-generation sequencing analysis. While the evaluation focused on two distinctive software tools, the results are general and should inform active and future software development for genome analysis software. As large-scale genome analysis becomes increasingly common in healthcare, it is critical that efficient and effective software interfaces are provided to accelerate clinical adoption of the technology. Implications for improved design of such applications are discussed. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Similarity-Based Recommendation of New Concepts to a Terminology

PubMed Central

Chandar, Praveen; Yaman, Anil; Hoxha, Julia; He, Zhe; Weng, Chunhua

2015-01-01

Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation. PMID:26958170

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

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients andmore » compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm{sup 3} in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.« less

Exploring barriers and facilitators to the clinical use of virtual reality for post-stroke unilateral spatial neglect assessment.

PubMed

Ogourtsova, Tatiana; Archambault, Philippe S; Lamontagne, Anouk

2017-11-07

Hemineglect, defined as a failure to attend to the contralesional side of space, is a prevalent and disabling post-stroke deficit. Conventional hemineglect assessments lack sensitivity as they contain mainly non-functional tasks performed in near-extrapersonal space, using static, two-dimensional methods. This is of concern given that hemineglect is a strong predictor for functional deterioration, limited post-stroke recovery, and difficulty in community reintegration. With the emerging field of virtual reality, several virtual tools have been proposed and have reported better sensitivity in neglect-related deficits detection than conventional methods. However, these and future virtual reality-based tools are yet to be implemented in clinical practice. The present study aimed to explore the barriers/facilitators perceived by clinicians in the use of virtual reality for hemineglect assessment; and to identify features of an optimal virtual assessment. A qualitative descriptive process, in the form of focus groups, self-administered questionnaire and individual interviews was used. Two focus groups (n = 11 clinicians) were conducted and experts in the field (n = 3) were individually interviewed. Several barriers and facilitators, including personal, institutional, client suitability, and equipment factors, were identified. Clinicians and experts in the field reported numerous features for the virtual tool optimization. Factors identified through this study lay the foundation for the development of a knowledge translation initiative towards an implementation of a virtual assessment for hemineglect. Addressing the identified barriers/facilitators during implementation and incorporating the optimal features in the design of the virtual assessment could assist and promote its eventual adoption in clinical settings. Implications for rehabilitation A multimodal and active knowledge translation intervention built on the presently identified modifiable factors is suggested to be implemented to support the clinical integration of a virtual reality-based assessment for post-stroke hemineglect. To amplify application and usefulness of a virtual-reality based tool in the assessment of post-stroke hemineglect, optimal features identified in the present study should be incorporated in the design of such technology.

Uncommon opportunistic fungal infections of oral cavity: A review

PubMed Central

Deepa, AG; Nair, Bindu J; Sivakumar, TT; Joseph, Anna P

2014-01-01

The majority of opportunistic oral mucosal fungal infections are due to Candida albicans and Aspergillus fumigatus species. Mucor and Cryptococcus also have a major role in causing oral infections, whereas Geotrichum, Fusarium, Rhodotorula, Saccharomyces and Penicillium marneffei are uncommon pathogens in the oral cavity. The broad spectrum of clinical presentation includes pseudo-membranes, abscesses, ulcers, pustules and extensive tissue necrosis involving bone. This review discusses various uncommon opportunistic fungal infections affecting the oral cavity including their morphology, clinical features and diagnostic methods. PMID:25328305

Clinical application of a light-pen computer system for quantitative angiography

NASA Technical Reports Server (NTRS)

Alderman, E. L.

1975-01-01

The important features in a clinical system for quantitative angiography were examined. The human interface for data input, whether an electrostatic pen, sonic pen, or light-pen must be engineered to optimize the quality of margin definition. The computer programs which the technician uses for data entry and computation of ventriculographic measurements must be convenient to use on a routine basis in a laboratory performing multiple studies per day. The method used for magnification correction must be continuously monitored.

[Hereditary motor and sensory neuropathy type 4A].

PubMed

2010-01-01

The first in the Russian Federation clinical cases of patients with autosomal-recessive type of hereditary motor and sensory neuropathy, type 4A, (HMSN 4A) are presented. In all cases, the diagnosis has been verified using molecular-genetic methods (DNA diagnostics). An analysis of features of clinical manifestations was performed in patients, aged from 5 to 34 years, with different disease duration (from 3-to 29 years). Criteria of selection of patients for DNA diagnostics for searching mutations in the GDAP1 gene are specified.

A clinical research analytics toolkit for cohort study.

PubMed

Yu, Yiqin; Zhu, Yu; Sun, Xingzhi; Tao, Ying; Zhang, Shuo; Xu, Linhao; Pan, Yue

2012-01-01

This paper presents a clinical informatics toolkit that can assist physicians to conduct cohort studies effectively and efficiently. The toolkit has three key features: 1) support of procedures defined in epidemiology, 2) recommendation of statistical methods in data analysis, and 3) automatic generation of research reports. On one hand, our system can help physicians control research quality by leveraging the integrated knowledge of epidemiology and medical statistics; on the other hand, it can improve productivity by reducing the complexities for physicians during their cohort studies.

A new approach for the quantitative evaluation of drawings in children with learning disabilities.

PubMed

Galli, Manuela; Vimercati, Sara Laura; Stella, Giacomo; Caiazzo, Giorgia; Norveti, Federica; Onnis, Francesca; Rigoldi, Chiara; Albertini, Giorgio

2011-01-01

A new method for a quantitative and objective description of drawing and for the quantification of drawing ability in children with learning disabilities (LD) is hereby presented. Twenty-four normally developing children (N) (age 10.6 ± 0.5) and 18 children with learning disabilities (LD) (age 10.3 ± 2.4) took part to the study. The drawing tasks were chosen among those already used in clinical daily experience (Denver Developmental Screening Test). Some parameters were defined in order to quantitatively describe the features of the children's drawings, introducing new objective measurements beside the subjective standard clinical evaluation. The experimental set-up revealed to be valid for clinical application with LD children. The parameters highlighted the presence of differences in the drawing features of N and LD children. This paper suggests the applicability of this protocol to other fields of motor and cognitive valuation, as well as the possibility to study the upper limbs position and muscle activation during drawing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease.

PubMed

Butt, A H; Rovini, E; Dolciotti, C; Bongioanni, P; De Petris, G; Cavallo, F

2017-07-01

The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved. A neurologist assessed the subjects during the task execution, assigning them a score according to the MDS/UPDRS-Section III items. Features of motor performance from both subject groups (patients and healthy controls) were extracted with dedicated algorithms. Furthermore, to find out the significance of such features from the clinical point of view, machine learning based methods were used. Overall, our findings showed the moderate potential of LMC to extract the motor performance of PwPD.

Preprocessing Structured Clinical Data for Predictive Modeling and Decision Support

PubMed Central

Oliveira, Mónica Duarte; Janela, Filipe; Martins, Henrique M. G.

2016-01-01

Summary Background EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls. Objectives This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them. Methods Along standard data processing stages – extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework –, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies. Results Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately. Conclusions This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data. PMID:27924347

SU-F-R-04: Radiomics for Survival Prediction in Glioblastoma (GBM)

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

Zhang, H; Molitoris, J; Bhooshan, N

Purpose: To develop a quantitative radiomics approach for survival prediction of glioblastoma (GBM) patients treated with chemoradiotherapy (CRT). Methods: 28 GBM patients who received CRT at our institution were retrospectively studied. 255 radiomic features were extracted from 3 gadolinium-enhanced T1 weighted MRIs for 2 regions of interest (ROIs) (the surgical cavity and its surrounding enhancement rim). The 3 MRIs were at pre-treatment, 1-month and 3-month post-CRT. The imaging features comprehensively quantified the intensity, spatial variation (texture), geometric property and their spatial-temporal changes for the 2 ROIs. 3 demographics features (age, race, gender) and 12 clinical parameters (KPS, extent of resection,more » whether concurrent temozolomide was adjusted/stopped and radiotherapy related information) were also included. 4 Machine learning models (logistic regression (LR), support vector machine (SVM), decision tree (DT), neural network (NN)) were applied to predict overall survival (OS) and progression-free survival (PFS). The number of cases and percentage of cases predicted correctly were collected and AUC (area under the receiver operating characteristic (ROC) curve) were determined after leave-one-out cross-validation. Results: From univariate analysis, 27 features (1 demographic, 1 clinical and 25 imaging) were statistically significant (p<0.05) for both OS and PFS. Two sets of features (each contained 24 features) were algorithmically selected from all features to predict OS and PFS. High prediction accuracy of OS was achieved by using NN (96%, 27 of 28 cases were correctly predicted, AUC = 0.99), LR (93%, 26 of 28 cases were correctly predicted, AUC = 0.95) and SVM (93%, 26 of 28 cases were correctly predicted, AUC = 0.90). When predicting PFS, NN obtained the highest prediction accuracy (89%, 25 of 28 cases were correctly predicted, AUC = 0.92). Conclusion: Radiomics approach combined with patients’ demographics and clinical parameters can accurately predict survival in GBM patients treated with CRT.« less

Automated detection of pulmonary nodules in CT images with support vector machines

NASA Astrophysics Data System (ADS)

Liu, Lu; Liu, Wanyu; Sun, Xiaoming

2008-10-01

Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma..sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100%, 92.75% and 90.23% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

[Clinical features of a Chinese pedigree with Waardenburg syndrome type 2].

PubMed

Yang, Shu-zhi; Yuan, Hui-jun; Bai, Lin-na; Cao, Ju-yang; Xu, Ye; Shen, Wei-dong; Ji, Fei; Yang, Wei-yan

2005-10-12

To investigate detailed clinical features of a Chinese pedigree with Waardenburg syndrome type 2. Members of this pedigree were interviewed to identify personal or family medical histories of hearing loss, the use of aminoglycosides, and other clinical abnormalities by filling questionnaire. The audiological and other clinical evaluations of the proband and other members of this family were conducted, including pure-tone audiometry, immittance and auditory brain-stem response and ophthalmological, dermatologic, hair, temporal bone CT examinations. This family is categorized as Waardenburg syndrome type 2 according to its clinical features. It's an autosomal dominant disorder with incomplete penetrance. The clinical features varied greatly among family members and characterized by sensorineural hearing loss, heterochromia irides, freckle on the face and premature gray hair. Hearing loss can be unilateral or bilateral, congenital or late onset in this family. This Chinese family has some unique clinical features comparing with the international diagnostic criteria for Waardenburg syndrome. This study may provide some evidences to amend the diagnostic criteria for Waardenburg syndrome in Chinese population.

SU-E-J-262: Variability in Texture Analysis of Gynecological Tumors in the Context of An 18F-FDG PET Adaptive Protocol

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

Nawrocki, J; Chino, J; Das, S

Purpose: This study examines the effect on texture analysis due to variable reconstruction of PET images in the context of an adaptive FDG PET protocol for node positive gynecologic cancer patients. By measuring variability in texture features from baseline and intra-treatment PET-CT, we can isolate unreliable texture features due to large variation. Methods: A subset of seven patients with node positive gynecological cancers visible on PET was selected for this study. Prescribed dose varied between 45–50.4Gy, with a 55–70Gy boost to the PET positive nodes. A baseline and intratreatment (between 30–36Gy) PET-CT were obtained on a Siemens Biograph mCT. Eachmore » clinical PET image set was reconstructed 6 times using a TrueX+TOF algorithm with varying iterations and Gaussian filter. Baseline and intra-treatment primary GTVs were segmented using PET Edge (MIM Software Inc., Cleveland, OH), a semi-automatic gradient-based algorithm, on the clinical PET and transferred to the other reconstructed sets. Using an in-house MATLAB program, four 3D texture matrices describing relationships between voxel intensities in the GTV were generated: co-occurrence, run length, size zone, and neighborhood difference. From these, 39 textural features characterizing texture were calculated in addition to SUV histogram features. The percent variability among parameters was first calculated. Each reconstructed texture feature from baseline and intra-treatment per patient was normalized to the clinical baseline scan and compared using the Wilcoxon signed-rank test in order to isolate variations due to reconstruction parameters. Results: For the baseline scans, 13 texture features showed a mean range greater than 10%. For the intra scans, 28 texture features showed a mean range greater than 10%. Comparing baseline to intra scans, 25 texture features showed p <0.05. Conclusion: Variability due to different reconstruction parameters increased with treatment, however, the majority of texture features showed significant changes during treatment independent of reconstruction effects.« less

Assessment of features for automatic CTG analysis based on expert annotation.

PubMed

Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

2011-01-01

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

[Molecular identification of human Diphyllobothrium nihonkaiense using mitochondrial cytochrome c oxidase subunit 1 (cox1) gene sequence].

PubMed

Ono, Sayaka; Morimoto, Norihito; Korenaga, Masataka; Kumazawa, Hideo; Komatsu, Yutaka; Kuge, Itsu; Higashidani, Yoshihumi; Ogura, Katsumi; Sugiura, Tetsuro

2010-11-01

Identification of Diphyllobothrium species has been carried out based on their morphology, especially sexual organs. In addition to these criteria, PCR-based identification methods have been developed recently. A 20 year-old Japanese living in Kochi Prefecture passed tapeworm. He was successfully treated with single dose of gastrografin. We examined the morphologic features of the proglottids and eggs using histology and scanning electron microscope. We also analyzed mitochondrial cytochrome c oxidase subunit 1 (cox1) gene of the proglottids. The causative tapeworm species was identified as D. nihonkaiense based on the results of morphologic features and genetic analysis. We discussed the advantage of PCR-based identification methods of Diphyllobothrium species using cox1 sequence in the clinical laboratory.

Real-time caries diagnostics by optical PNC method

NASA Astrophysics Data System (ADS)

Masychev, Victor I.; Alexandrov, Michail T.

2000-11-01

The results of hard tooth tissues research by the optical PNC- method in experimental and clinical conditions are presented. In the experiment under 90 test-sample of tooth slices with thickness about 1mm (enamel, dentine and cement) were researched. The results of the experiment were processed by the method of correlation analyze. Clinical researches were executed on teeth of 210 patients. The regions of tooth tissue diseases with initial, moderate and deep caries were investigated. Spectral characteristics of intact and pathologically changed tooth tissues are presented and their peculiar features are discussed. The results the optical PNC-method application while processing tooth carious cavities are presented in order to estimate efficiency of the mechanical and antiseptic processing of teeth. It is revealed that the PNC-method can be sued as for differential diagnostics of a degree dental carious stage, as for estimating of carefulness of tooth cavity processing before filling.

Express diagnostics of intact and pathological dental hard tissues by optical PNC method

NASA Astrophysics Data System (ADS)

Masychev, Victor I.; Alexandrov, Michail T.

2000-03-01

The results of hard tooth tissues research by the optical PNC- method in experimental and clinical conditions are presented. In the experiment under 90 test-sample of tooth slices with thickness about 1 mm (enamel, dentine and cement) were researched. The results of the experiment were processed by the method of correlation analyze. Clinical researches were executed on teeth of 210 patients. The regions of tooth tissue diseases with initial, moderate and deep caries were investigated. Spectral characteristics of intact and pathologically changed tooth tissues are presented and their peculiar features are discussed. The results the optical PNC- method application while processing tooth carious cavities are presented in order to estimate efficiency of the mechanical and antiseptic processing of teeth. It is revealed that the PNC-method can be used as for differential diagnostics of a degree dental carious stage, as for estimating of carefulness of tooth cavity processing before filling.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

PubMed Central

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-01-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

Big data and clinicians: a review on the state of the science.

PubMed

Wang, Weiqi; Krishnan, Eswar

2014-01-17

In the past few decades, medically related data collection saw a huge increase, referred to as big data. These huge datasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important role in identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving primary-care quality. The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss the challenges and limitations in this realm. We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We used different combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature of interest from the past 10 years. This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of this type of data. Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with our ability to collect and store data.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

NASA Astrophysics Data System (ADS)

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-05-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

3D brain tumor segmentation in multimodal MR images based on learning population- and patient-specific feature sets.

PubMed

Jiang, Jun; Wu, Yao; Huang, Meiyan; Yang, Wei; Chen, Wufan; Feng, Qianjin

2013-01-01

Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a method to construct a graph by learning the population- and patient-specific feature sets of multimodal magnetic resonance (MR) images and by utilizing the graph-cut to achieve a final segmentation. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through learning population- and patient-specific feature sets, respectively. The proposed method is evaluated using 23 glioma image sequences, and the segmentation results are compared with other approaches. The encouraging evaluation results obtained, i.e., DSC (84.5%), Jaccard (74.1%), sensitivity (87.2%), and specificity (83.1%), show that the proposed method can effectively make use of both population- and patient-specific information. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Detection of protruding lesion in wireless capsule endoscopy videos of small intestine

NASA Astrophysics Data System (ADS)

Wang, Chengliang; Luo, Zhuo; Liu, Xiaoqi; Bai, Jianying; Liao, Guobin

2018-02-01

Wireless capsule endoscopy (WCE) is a developed revolutionary technology with important clinical benefits. But the huge image data brings a heavy burden to the doctors for locating and diagnosing the lesion images. In this paper, a novel and efficient approach is proposed to help clinicians to detect protruding lesion images in small intestine. First, since there are many possible disturbances such as air bubbles and so on in WCE video frames, which add the difficulty of efficient feature extraction, the color-saliency region detection (CSD) method is developed for extracting the potentially saliency region of interest (SROI). Second, a novel color channels modelling of local binary pattern operator (CCLBP) is proposed to describe WCE images, which combines grayscale and color angle. The CCLBP feature is more robust to variation of illumination and more discriminative for classification. Moreover, support vector machine (SVM) classifier with CCLBP feature is utilized to detect protruding lesion images. Experimental results on real WCE images demonstrate that proposed method has higher accuracy on protruding lesion detection than some art-of-state methods.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B

2018-01-01

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity. Copyright © 2017 Elsevier B.V. All rights reserved.

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

EHR based Genetic Testing Knowledge Base (iGTKB) Development.

PubMed

Zhu, Qian; Liu, Hongfang; Chute, Christopher G; Ferber, Matthew

2015-01-01

The gap between a large growing number of genetic tests and a suboptimal clinical workflow of incorporating these tests into regular clinical practice poses barriers to effective reliance on advanced genetic technologies to improve quality of healthcare. A promising solution to fill this gap is to develop an intelligent genetic test recommendation system that not only can provide a comprehensive view of genetic tests as education resources, but also can recommend the most appropriate genetic tests to patients based on clinical evidence. In this study, we developed an EHR based Genetic Testing Knowledge Base for Individualized Medicine (iGTKB). We extracted genetic testing information and patient medical records from EHR systems at Mayo Clinic. Clinical features have been semi-automatically annotated from the clinical notes by applying a Natural Language Processing (NLP) tool, MedTagger suite. To prioritize clinical features for each genetic test, we compared odds ratio across four population groups. Genetic tests, genetic disorders and clinical features with their odds ratios have been applied to establish iGTKB, which is to be integrated into the Genetic Testing Ontology (GTO). Overall, there are five genetic tests operated with sample size greater than 100 in 2013 at Mayo Clinic. A total of 1,450 patients who was tested by one of the five genetic tests have been selected. We assembled 243 clinical features from the Human Phenotype Ontology (HPO) for these five genetic tests. There are 60 clinical features with at least one mention in clinical notes of patients taking the test. Twenty-eight clinical features with high odds ratio (greater than 1) have been selected as dominant features and deposited into iGTKB with their associated information about genetic tests and genetic disorders. In this study, we developed an EHR based genetic testing knowledge base, iGTKB. iGTKB will be integrated into the GTO by providing relevant clinical evidence, and ultimately to support development of genetic testing recommendation system, iGenetics.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Multi-spectral fiber spectroscopy in 0,4-16μm range for biomedical applications(Conference Presentation)

NASA Astrophysics Data System (ADS)

Artyushenko, Viacheslav

2017-02-01

Various biomedical applications of fiber optics in a broad spectral range 0,4-16μm span from endoscopic imaging and Photo Dynamic Diagnostics (PDD) to laser power delivery for minimal invasive laser surgery, tissue coagulation and welding, Photo Dynamic Therapy (PDT), etc. Present review will highlight the latest results in advanced fiber solutions for a precise tissue diagnostics and control of some therapy methods - for so called "theranostic". Spectral fiber sensing for label free analysis of tissue composition helps to differentiate malignant and normal tissue to secure minimal invasive, but complete tumor removal or treatment. All key methods of Raman, fluorescence, diffuse reflection & MIR-absorption spectroscopy will be compared when used for the same spot of tissue - to select the most specific, sensitive and accurate method or to combine them for the synergy enhanced effect. The most informative spectral features for distinct organs/ tumor can be used to design special fiber sensors to be developed for portable and low cost applications with modern IT-features. Examples of multi-spectral tissue diagnostics promising for the future clinical applications will be presented to enable reduced mortality from cancer in the future. Translation of described methods into clinical practice will be discussed in comparison with the other method of optical diagnostics which should enhance modern medicine by less invasive, more precise and more effective methods of therapy to be fused with in-vivo diagnostics sensors & systems.

SU-G-BRC-13: Model Based Classification for Optimal Position Selection for Left-Sided Breast Radiotherapy: Free Breathing, DIBH, Or Prone

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

Lin, H; Liu, T; Xu, X

Purpose: There are clinical decision challenges to select optimal treatment positions for left-sided breast cancer patients—supine free breathing (FB), supine Deep Inspiration Breath Hold (DIBH) and prone free breathing (prone). Physicians often make the decision based on experiences and trials, which might not always result optimal OAR doses. We herein propose a mathematical model to predict the lowest OAR doses among these three positions, providing a quantitative tool for corresponding clinical decision. Methods: Patients were scanned in FB, DIBH, and prone positions under an IRB approved protocol. Tangential beam plans were generated for each position, and OAR doses were calculated.more » The position with least OAR doses is defined as the optimal position. The following features were extracted from each scan to build the model: heart, ipsilateral lung, breast volume, in-field heart, ipsilateral lung volume, distance between heart and target, laterality of heart, and dose to heart and ipsilateral lung. Principal Components Analysis (PCA) was applied to remove the co-linearity of the input data and also to lower the data dimensionality. Feature selection, another method to reduce dimensionality, was applied as a comparison. Support Vector Machine (SVM) was then used for classification. Thirtyseven patient data were acquired; up to now, five patient plans were available. K-fold cross validation was used to validate the accuracy of the classifier model with small training size. Results: The classification results and K-fold cross validation demonstrated the model is capable of predicting the optimal position for patients. The accuracy of K-fold cross validations has reached 80%. Compared to PCA, feature selection allows causal features of dose to be determined. This provides more clinical insights. Conclusion: The proposed classification system appeared to be feasible. We are generating plans for the rest of the 37 patient images, and more statistically significant results are to be presented.« less

Hypohidrotic and hidrotic ectodermal dysplasia: a report of two cases.

PubMed

Vasconcelos Carvalho, Marianne; Romero Souto de Sousa, José; Paiva Correa de Melo, Filipe; Fonseca Faro, Tatiane; Nunes Santos, Ana Clara; Carvalho, Silvia; Veras Sobral, Ana Paula

2013-07-14

Ectodermal dysplasias are a large group of syndromes characterized by anomalies in the structures of ectodermal origin. There are 2 major types of this disorder, based on clinical findings: hypohidrotic ectodermal dysplasia and hidrotic ectodermal dysplasia. This clinical classification is very important because clinical professionals involved with this disease need first a clear and practical method of diagnosis. The main oral manifestation of ectodermal dysplasia may be expressed as hypodontia. Thus, dental professionals may be the first to diagnose ectodermal dysplasia. The present article reports one case of each of the main types (hypohidrotic and hidrotic) of ectodermal dysplasia and the authors review the literature regarding the pathogenesis, clinical features, and therapeutic management of this condition.

Automated Detection of Actinic Keratoses in Clinical Photographs

PubMed Central

Hames, Samuel C.; Sinnya, Sudipta; Tan, Jean-Marie; Morze, Conrad; Sahebian, Azadeh; Soyer, H. Peter; Prow, Tarl W.

2015-01-01

Background Clinical diagnosis of actinic keratosis is known to have intra- and inter-observer variability, and there is currently no non-invasive and objective measure to diagnose these lesions. Objective The aim of this pilot study was to determine if automatically detecting and circumscribing actinic keratoses in clinical photographs is feasible. Methods Photographs of the face and dorsal forearms were acquired in 20 volunteers from two groups: the first with at least on actinic keratosis present on the face and each arm, the second with no actinic keratoses. The photographs were automatically analysed using colour space transforms and morphological features to detect erythema. The automated output was compared with a senior consultant dermatologist’s assessment of the photographs, including the intra-observer variability. Performance was assessed by the correlation between total lesions detected by automated method and dermatologist, and whether the individual lesions detected were in the same location as the dermatologist identified lesions. Additionally, the ability to limit false positives was assessed by automatic assessment of the photographs from the no actinic keratosis group in comparison to the high actinic keratosis group. Results The correlation between the automatic and dermatologist counts was 0.62 on the face and 0.51 on the arms, compared to the dermatologist’s intra-observer variation of 0.83 and 0.93 for the same. Sensitivity of automatic detection was 39.5% on the face, 53.1% on the arms. Positive predictive values were 13.9% on the face and 39.8% on the arms. Significantly more lesions (p<0.0001) were detected in the high actinic keratosis group compared to the no actinic keratosis group. Conclusions The proposed method was inferior to assessment by the dermatologist in terms of sensitivity and positive predictive value. However, this pilot study used only a single simple feature and was still able to achieve sensitivity of detection of 53.1% on the arms.This suggests that image analysis is a feasible avenue of investigation for overcoming variability in clinical assessment. Future studies should focus on more sophisticated features to improve sensitivity for actinic keratoses without erythema and limit false positives associated with the anatomical structures on the face. PMID:25615930

A Naturalistic Study of Referred Children and Adolescents with Obsessive-Compulsive Disorder.

ERIC Educational Resources Information Center

Masi, Gabriele; Millepiedi, Stefania; Mucci, Maria; Bertini, Nicoletta; Milantoni, Luca; Arcangeli, Francesca

2005-01-01

Objective: To report on clinical features, comorbidity, and response to pharmacotherapy in children and adolescents with obsessive-compulsive disorder (OCD) naturalistically followed and treated with serotonin reuptake inhibitors (SRIs). Method: A consecutive series of 94 patients (65 males, 29 females, age 13.6 [+ or -] 2.8 years), referred in…

Conversion Disorder in Australian Pediatric Practice

ERIC Educational Resources Information Center

Kozlowska, Kasia; Nunn, Kenneth P.; Rose, Donna; Morris, Anne; Ouvrier, Robert A.; Varghese, John

2007-01-01

Objectives: To describe the incidence and clinical features of children presenting to Australian child health specialists with conversion disorder. Method: Active, national surveillance of conversion disorder in children younger than 16 years of age during 2002 and 2003. Results: A total of 194 children were reported on. The average age was 11.8…

PATTERN RECOGNITION APPROACH TO MEDICAL DIAGNOSIS,

DTIC Science & Technology

A sequential method of pattern recognition was used to recognize hyperthyroidism in a sample of 2219 patients being treated at the Straub Clinic in...the most prominent class features are selected. Thus, the symptoms which best distinguish hyperthyroidism are extracted at every step and the number of tests required to reach a diagnosis is reduced. (Author)

Turban pin aspiration: new fashion, new syndrome.

PubMed

Ilan, Ophir; Eliashar, Ron; Hirshoren, Nir; Hamdan, Kasem; Gross, Menachem

2012-04-01

Turban pin aspiration syndrome is a new clinical entity afflicting young Islamic girls wearing a turban.The goal of this study was to present our experience in diagnosis and treatment of this new entity, define its clinical and epidemiologic features, and shed a new light on the role of fashion in the increased incidence. A retrospective study in a tertiary university hospital. Review of clinical parameters and epidemiologic features of 26 patients diagnosed with turban pin aspiration syndrome admitted to the Hadassah-Hebrew University Hospitals in Jerusalem from 1990 to 2010. All patients were Muslim females with an average age of 16 years. In all cases, the history was positive for accidental aspiration. Most of the pins were located in the trachea (42%). In 20 cases, the pins were extracted by rigid bronchoscopy without major complications. Fluoroscopy-assisted rigid bronchoscopy was used successfully in three cases. In one case, the object was self-ejected by coughing before the bronchoscopy, and two patients were referred to the chest unit for thoracotomy. Clinicians should be aware of this distinct form of foreign body aspiration, its method of diagnosis, and extraction techniques. A cultural investigation showed a difference in the turban-fastening technique of young girls as compared with their mothers. Removal by rigid bronchoscopy is a safe method with a high success rate and should be considered as the preferred extraction method of choice. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Perineural Infiltration of Cutaneous Squamous Cell Carcinoma and Basal Cell Carcinoma Without Clinical Features

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

Lin, Charles, E-mail: Charles_Lin@health.qld.gov.au; Tripcony, Lee; Keller, Jacqui

2012-01-01

Purpose: To review the factors that influence outcome and patterns of relapse in patients with cutaneous squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) with perineural infiltration (PNI) without clinical or radiologic features, treated with surgery and radiotherapy. Methods and Materials: Between 1991 and 2004, 222 patients with SCC or BCC with PNI on pathologic examination but without clinical or radiologic PNI features were identified. Charts were reviewed retrospectively and relevant data collected. All patients were treated with curative intent; all had radiotherapy, and most had surgery. The primary endpoint was 5-year relapse-free survival from the time of diagnosis.more » Results: Patients with SCC did significantly worse than those with BCC (5-year relapse-free survival, 78% vs. 91%; p < 0.01). Squamous cell carcinoma with PNI at recurrence did significantly worse than de novo in terms of 5-year local failure (40% vs. 19%; p < 0.01) and regional relapse (29% vs. 5%; p < 0.01). Depth of invasion was also a significant factor. Of the PNI-specific factors for SCC, focal PNI did significantly better than more-extensive PNI, but involved nerve diameter or presence of PNI at the periphery of the tumor were not significant factors. Conclusions: Radiotherapy in conjunction with surgery offers an acceptable outcome for cutaneous SCC and BCC with PNI. This study suggests that focal PNI is not an adverse feature.« less

Dendritic cell and histiocytic neoplasms: biology, diagnosis, and treatment.

PubMed

Dalia, Samir; Shao, Haipeng; Sagatys, Elizabeth; Cualing, Hernani; Sokol, Lubomir

2014-10-01

Dendritic and histiocytic cell neoplasms are rare malignancies that make up less than 1% of all neoplasms arising in lymph nodes or soft tissues. These disorders have distinctive disease biology, clinical presentations, pathology, and unique treatment options. Morphology and immunohistochemistry evaluation by a hematopathologist remains key for differentiating between these neoplasms. In this review, we describe tumor biology, clinical features, pathology, and treatment of follicular dendritic cell sarcoma, interdigitating dendritic cell sarcoma, indeterminate dendritic cell sarcoma, histiocytic sarcoma, fibroblastic reticular cell tumors, and disseminated juvenile xanthogranuloma. A literature search for articles published between 1990 and 2013 was undertaken. Articles are reviewed and salient findings are systematically described. Patients with dendritic cell and histiocytic neoplasms have distinct but variable clinical presentations; however, because many tumors have recently been recognized, their true incidence is uncertain. Although the clinical features can present in many organs, most occur in the lymph nodes or skin. Most cases are unifocal and solitary presentations have good prognoses with surgical resection. The role of adjuvant therapy in these disorders remains unclear. In cases with disseminated disease, prognosis is poor and data on treatment options are limited, although chemotherapy and referral to a tertiary care center should be considered. Excisional biopsy is the preferred method of specimen collection for tissue diagnosis, and immunohistochemistry is the most important diagnostic method for differentiating these disorders from other entities. Dendritic cell and histiocytic cell neoplasms are rare hematological disorders with variable clinical presentations and prognoses. Immunohistochemistry remains important for diagnosis. Larger pooled analyses or clinical trials are needed to better understand optimal treatment options in these rare disorders. Whenever possible, patients should be referred to a tertiary care center for disease management.

PROGNOSTIC SIGNIFICANCE OF CLINICAL, HISTOPATHOLOGICAL, AND MOLECULAR CHARACTERISTICS OF MEDULLOBLASTOMAS IN THE PROSPECTIVE HIT2000 MULTICENTER CLINICAL TRIAL COHORT

PubMed Central

Pietsch, Torsten; Schmidt, Rene; Remke, Marc; Korshunov, Andrey; Hovestadt, Volker; Jones, David TW; Felsberg, Jörg; Kaulich, Kerstin; Goschzik, Tobias; Kool, Marcel; Northcott, Paul A.; von Hoff, Katja; von Bueren, André O.; Friedrich, Carsten; Skladny, Heyko; Fleischhack, Gudrun; Taylor, Michael D.; Cremer, Friedrich; Lichter, Peter; Faldum, Andreas; Reifenberger, Guido; Rutkowski, Stefan; Pfister, Stefan M.

2014-01-01

BACKGROUND: This study aimed to prospectively evaluate clinical, histopathological and molecular variables for outcome prediction in medulloblastoma patients. METHODS: Patients from the HIT2000 cooperative clinical trial were prospectively enrolled based on the availability of sufficient tumor material and complete clinical information. This revealed a cohort of 184 patients (median age 7.6 years), which was randomly split at a 2:1 ratio into a training (n = 127), and a validation (n = 57) dataset. All samples were subjected to thorough histopathological investigation, CTNNB1 mutation analysis, quantitative PCR, MLPA and FISH analyses for cytogenetic variables, and methylome analysis. RESULTS: By univariable analysis, clinical factors (M-stage), histopathological variables (large cell component, endothelial proliferation, synaptophysin pattern), and molecular features (chromosome 6q status, MYC amplification, TOP2A copy-number, subgrouping) were found to be prognostic. Molecular consensus subgrouping (WNT, SHH, Group 3, Group 4) was validated as an independent feature to stratify patients into different risk groups. When comparing methods for the identification of WNT-driven medulloblastoma, this study identified CTNNB1 sequencing and methylation profiling to most reliably identify these patients. After removing patients with particularly favorable (CTNNB1 mutation, extensive nodularity) or unfavorable (MYC amplification) markers, a risk score for the remaining “intermediate molecular risk” population dependent on age, M-stage, pattern of synaptophysin expression, and MYCN copy-number status was identified and validated, with speckled synaptophysin expression indicating worse outcome. CONCLUSIONS: Methylation subgrouping and CTNNB1 mutation status represent robust tools for the risk-stratification of medulloblastoma. A simple clinico-pathological risk score for “intermediate molecular risk” patients was identified, which deserves further validation. SECONDARY CATEGORY: Pediatrics.

Circulating D-dimer level correlates with disease characteristics in hepatoblastoma patients

PubMed Central

Zhang, BinBin; Liu, GongBao; Liu, XiangQi; Zheng, Shan; Dong, Kuiran; Dong, Rui

2017-01-01

Abstract Objectives: Hepatoblastoma (HB) is the most common pediatric liver malignancy, typically affecting children within the first 4 years of life. However, only a few validated blood biomarkers are used in clinical evaluation. The current study explored the clinical application and significance of D-dimer levels in patients with HB. Method: Forty-four patients with HB were included in this retrospective study. D-dimer and plasma fibrinogen levels were examined, and their correlation with other clinical features was analyzed. D-dimer and plasma fibrinogen levels were examined between HB and other benign hepatic tumors. Results: D-dimer levels were significantly associated with high-risk HB features, such as advanced stage and high α-fetoprotein (AFP) levels. Higher D-dimer levels were observed in stage 4 patients compared with stage 1/2/3 patients (P = .028). Higher D-dimer levels were also observed in patients with higher AFP levels before chemotherapy compared with patients with lower AFP levels after chemotherapy (P< 0.001). In addition, higher D-dimer levels were observed in HB compared with other benign hepatic tumors such as hepatic hemangioma and hepatocellular adenoma (P = .012). No significant difference in D-dimer levels was found between sex (P = .503) and age (≥12 vs <12 months, P = .424). There was no significant difference in plasma fibrinogen levels between sex or age and high-risk HB features, such as advanced stage and high AFP levels. Conclusions: Elevated D-dimer levels could be a useful determinant biomarker for high-risk features in patients with HB. This finding also supports the clinical application of D-dimer in HB. PMID:29381980

Lesion with morphologic feature of organizing pneumonia (OP) in CT-guided lung biopsy samples for diagnosis of bronchiolitis obliterans organizing pneumonia (BOOP): a retrospective study of 134 cases in a single center

PubMed Central

Wang, Yongsheng; Li, Yan; Ding, Jingjing; Chen, Lulu; Dai, Jinghong; Cai, Hourong; Xiao, Yonglong; Cao, Min; Huang, Mei; Qiu, Yuying; Meng, Fanqing; Fan, Xiangshan; Zhang, Deping

2014-01-01

Background Small biopsy samples are generally considered inconclusive for bronchiolitis obliterans organizing pneumonia (BOOP) diagnosis despite their potential to reveal organizing pneumonia (OP) pathologically, necessitating risky invasive tissue biopsy during surgery for reliable confirmation. Objective OP by CT-guided lung biopsy was to evaluate the role in the diagnosis of BOOP. Methods A retrospective review of 134 cases with the OP feature in the CT-guided lung biopsy samples between 2004 and 2011 at a single center was conducted. Diagnostic accuracy of OP by CT-guided lung biopsy and clinical-radiographic data alone were compared. Results After exclusion of 11 cases due to pathology with others besides OP and 15 cases for loss to follow-up, 108 were included. Of these, 95 cases and 13 cases were classified as BOOP and non-BOOP group, respectively. Among BOOP group, only 30 were initially diagnosed as BOOP according to the typical clinical and radiographic features. The other 65 cases with atypical features were diagnosed as BOOP mainly based on OP by CT-guided lung biopsy. Among non-BOOP group, one was misdiagnosed as BOOP, and others were not BOOP according to clinical and radiographic findings. Thus, OP by CT-guided lung biopsy produced a diagnostic accuracy of 87.96% (95/108), much higher than 31.25% (30/96) observed using clinical and radiographic data alone. Combined, these techniques produced diagnostic accuracy of 98.96% (95/96). Conclusions OP by CT-guided lung biopsy can be effectively used as the pathological evidence for BOOP diagnosis and reducing unnecessary surgery. PMID:25276367

Localized scleroderma: a series of 52 patients.

PubMed

Toledano, C; Rabhi, S; Kettaneh, A; Fabre, B; Fardet, L; Tiev, K P; Cabane, J

2009-05-01

Localized scleroderma also called morphea is a skin disorder of undetermined cause. The widely recognized Mayo Clinic Classification identifies 5 main morphea types: plaque, generalized, bullous, linear and deep. Whether each of these distinct types has a particular clinical course or is associated with some patient-related features is still unclear. We report here a retrospective series of patients with localized scleroderma with an attempt to identify features related to the type of lesion involved. The medical records of all patients with a diagnosis of localized scleroderma were reviewed by skilled practitioners. Lesions were classified according to the Mayo Clinic Classification. The relationship between each lesion type and various clinical features was tested by non-parametrical methods. The sample of 52 patients included 43 females and 9 males. Median age at onset was 30 y (range 1-76). Frequencies of patients according to morphea types were: plaque morphea 41 (78.8%) (including morphea en plaque 30 (57.7%) and atrophoderma of Pasini-Pierini 11 (21.1%)), linear scleroderma 14 (26.9%). Nine patients (17.3%) had both types of localized scleroderma. Median age at onset was lower in patients with linear scleroderma (8 y (range 3-44)) than in others (36 y (range 1-77)) (p=0.0003). Head involvement was more common in patients with linear scleroderma (37.5%) than in other subtypes (11.1%) (p=0.05). Atrophoderma of Pasini-Pierini was never located at the head. Systemic symptoms, antinuclear antibodies and the rheumatic factor were not associated with localized scleroderma types or subtypes. These results suggest that morphea types, in adults are not associated with distinct patient features except for age at disease onset (lower) and the localization on the head (more frequent), in patients with lesions of the linear type.

Assessment of Correlation between Sweat Chloride Levels and Clinical Features of Cystic Fibrosis Patients.

PubMed

Raina, Manzoor A; Khan, Mosin S; Malik, Showkat A; Raina, Ab Hameed; Makhdoomi, Mudassir J; Bhat, Javed I; Mudassar, Syed

2016-12-01

Cystic Fibrosis (CF) is an autosomal recessive disorder and the incidence of this disease is undermined in Northern India. The distinguishable salty character of the sweat belonging to individuals suffering from CF makes sweat chloride estimation essential for diagnosis of CF disease. The aim of this prospective study was to elucidate the relationship of sweat chloride levels with clinical features and pattern of CF. A total of 182 patients, with clinical features of CF were included in this study for quantitative measurement of sweat chloride. Sweat stimulation and collection involved pilocarpine iontophoresis based on the Gibson and Cooks methodology. The quantitative estimation of chloride was done by Schales and Schales method with some modifications. Cystic Fibrosis Trans Membrane Conductance Regulator (CFTR) mutation status was recorded in case of patients with borderline sweat chloride levels to correlate the results and for follow-up. Out of 182 patients having clinical features consistent with CF, borderline and elevated sweat chloride levels were present in 9 (5%) and 41 (22.5%) subjects respectively. Elevated sweat chloride levels were significantly associated with wheeze, Failure To Thrive (FTT), history of CF in Siblings, product of Consanguineous Marriage (CM), digital clubbing and steatorrhoea on univariate analysis. On multivariate analysis only wheeze, FTT and steatorrhoea were found to be significantly associated with elevated sweat chloride levels (p<0.05). Among the nine borderline cases six cases were positive for at least two CFTR mutations and rest of the three cases were not having any mutation in CFTR gene. The diagnosis is often delayed and the disease is advanced in most patients at the time of diagnosis. Sweat testing is a gold standard for diagnosis of CF patients as genetic mutation profile being heterozygous and unlikely to become diagnostic test.

Gender differences in older adults with chronic migraine in Turkey.

PubMed

Özge, Aynur; Uluduz, Derya; Selekler, Macit; Öztürk, Musa; Baykan, Betül; Çınar, Nilgün; Domaç, Füsun M; Zarifoğlu, Mehmet; Inan, Levent E; Akyol, Ali; Bolay, Hayrunnisa; Uzuner, Gülnur T; Erdemoğlu, Ali K; Oksuz, Nevra; Temel, Gulhan O

2015-05-01

Chronic migraine is a growing and disabling subtype of migraine with different risk factors and clinical features, even in older adults. We sought to define and differentiate clinical features of chronic migraine in older adults. We also aimed to compare major clinical features of chronic migraine in older adults with those in younger people of both sexes. We used electronic dataset (Turkish Headache Database) from 13 tertiary headache centers in Turkey. Electronic dataset included detailed headache-defining features according to ICHD-II criteria based on face-to-face interviews and examination by a headache specialist. Using statistical methods, clinical variables of chronic migraine in older adults were compared with those of younger adults. We included 915 patients with chronic migraine (mean age 43.80 ± 13.95 years); 83.3% were females. In total, 301 patients (32.9%) with chronic migraine aged >50 years were compared with 614 patients aged <50 years. There was no significant change in men with increasing age. However, duration of headache history, severity of attacks, previous histories of motion sickness and positive family history of headaches were significantly different in women with increasing age. Further sex-related differences have been shown in parameters such as attack duration, quality and associated nausea. Chronic migraine is an infrequent type of migraine and shows age-related changes in some phenotypic characteristics, such as severity of attacks, especially in women aged older than 50 years. Furthermore, positive family history of headaches and history of motion sickness increase the likelihood of developing chronic migraine in older women, indicating involvement of some gender-related, but as-yet unknown, genetic factors. © 2014 Japan Geriatrics Society.

Oral lymphoepithelial cyst: A clinicopathological study of 26 cases and review of the literature

PubMed Central

Sykara, Maria; Ntovas, Panagiotis; Tosios, Konstantinos I.; Sklavounou, Alexandra

2017-01-01

Introduction Τo describe the clinicopathological features of 26 oral lymphoepithelial cysts (LECs) and review the literature. Material and Methods Twenty-six cases of oral LECs diagnosed during a 37-year period were retrospectively collected. The patients’ gender and age, as well as the main clinical features of the cysts were retrieved from the requisition forms. The main microscopic features were recorded after reevaluation of all cases. Pubmed and Google Scholar electronic databases were searched with the key word “oral LEC”. Inclusion criteria were the microscopic confirmation of LEC diagnosis and the report at least two of three main clinical features (gender, age and cyst’s location). Results The 26 oral LECs represented 0.08% of 31,564 biopsies accessioned during the study period. They affected 25 patients, 14 females and 11 males with a mean age of 33.04±9.81 years. They appeared as smooth (92%) nodules, with soft (24%) or firm (76%) consistency and normal (28%), yellow to normal (20%), yellow (32%) or white (20%) hue, in the tongue (69.23%) or the floor of mouth (30.77%). They were covered by parakeratinized squamous (92.31%) or non-keratinized (7.69%) epithelium and contained desquamated epithelial cells, amorphous eosinophilic material and/or inflammatory cells (100%). The lymphoid tissue surrounded the cystic cavity partially (34.62%) or completely (65.38%), often in a follicular pattern with prominent germinal centers (53.85%). Literature review yielded 316 cases of oral LECs derived from 25 case reports, 3 case studies/retrospective studies with detailed information for each case and 7 studies with summarized data. Conclusions Oral LEC is a pathologic entity with discrete clinical presentation that is, however, commonly misdiagnosed in clinical practice as other, mostly benign, entities. Its pathogenesis remains obscure, as its clinicopathologic features are consistent with both theories suggested up to date. Key words:Oral lymphoepithelial cyst; developmental cyst; non odontogenic cyst; lymphoid tissue; oral tonsil. PMID:28936296

Patient prognosis based on feature extraction, selection and classification of EEG periodic activity.

PubMed

Sánchez-González, Alain; García-Zapirain, Begoña; Maestro Saiz, Iratxe; Yurrebaso Santamaría, Izaskun

2015-01-01

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.

Imaging of gout: an overview.

PubMed

Dalbeth, Nicola; Doyle, Anthony J

2012-12-01

The diverse clinical states and sites of pathology in gout provide challenges when considering the features apparent on imaging. Ideally, an imaging modality should capture all aspects of disease including monosodium urate crystal deposition, acute inflammation, tophus, tissue remodelling and complications of disease. The modalities used in gout include conventional radiography, ultrasonography, magnetic resonance imaging, computed tomography and dual-energy computed tomography. This review discusses the role of each of these imaging modalities in gout, focussing on the imaging characteristics, role in gout diagnosis and role for disease monitoring. Ultrasonography and dual-energy computed tomography are particularly promising methods for both non-invasive diagnosis and monitoring of disease. The observation that ultrasonographic appearances of monosodium urate crystal deposition can be observed in patients with hyperuricaemia but no other clinical features of gout raises important questions about disease definitions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

PubMed

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

PubMed Central

Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

2016-01-01

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. PMID:27444059

An effective method for cirrhosis recognition based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Chen, Yameng; Sun, Gengxin; Lei, Yiming; Zhang, Jinpeng

2018-04-01

Liver disease is one of the main causes of human healthy problem. Cirrhosis, of course, is the critical phase during the development of liver lesion, especially the hepatoma. Many clinical cases are still influenced by the subjectivity of physicians in some degree, and some objective factors such as illumination, scale, edge blurring will affect the judgment of clinicians. Then the subjectivity will affect the accuracy of diagnosis and the treatment of patients. In order to solve the difficulty above and improve the recognition rate of liver cirrhosis, we propose a method of multi-feature fusion to obtain more robust representations of texture in ultrasound liver images, the texture features we extract include local binary pattern(LBP), gray level co-occurrence matrix(GLCM) and histogram of oriented gradient(HOG). In this paper, we firstly make a fusion of multi-feature to recognize cirrhosis and normal liver based on parallel combination concept, and the experimental results shows that the classifier is effective for cirrhosis recognition which is evaluated by the satisfying classification rate, sensitivity and specificity of receiver operating characteristic(ROC), and cost time. Through the method we proposed, it will be helpful to improve the accuracy of diagnosis of cirrhosis and prevent the development of liver lesion towards hepatoma.

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel).

PubMed

Douville, Christopher; Masica, David L; Stenson, Peter D; Cooper, David N; Gygax, Derek M; Kim, Rick; Ryan, Michael; Karchin, Rachel

2016-01-01

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features--DNA and protein sequence conservation, indel length, and occurrence in repeat regions--are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in-frame and frameshift indels (VEST-indel) as pathogenic or benign. We apply 24 features, including a new "PubMed" feature, to estimate a gene's importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false-positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta-predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta-predictor with improved performance over any individual method. © 2015 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

A Novel Feature-Tracking Echocardiographic Method for the Quantitation of Regional Myocardial Function

PubMed Central

Pirat, Bahar; Khoury, Dirar S.; Hartley, Craig J.; Tiller, Les; Rao, Liyun; Schulz, Daryl G.; Nagueh, Sherif F.; Zoghbi, William A.

2012-01-01

Objectives The aim of this study was to validate a novel, angle-independent, feature-tracking method for the echocardiographic quantitation of regional function. Background A new echocardiographic method, Velocity Vector Imaging (VVI) (syngo Velocity Vector Imaging technology, Siemens Medical Solutions, Ultrasound Division, Mountain View, California), has been introduced, based on feature tracking—incorporating speckle and endocardial border tracking, that allows the quantitation of endocardial strain, strain rate (SR), and velocity. Methods Seven dogs were studied during baseline, and various interventions causing alterations in regional function: dobutamine, 5-min coronary occlusion with reperfusion up to 1 h, followed by dobutamine and esmolol infusions. Echocardiographic images were acquired from short- and long-axis views of the left ventricle. Segment-length sonomicrometry crystals were used as the reference method. Results Changes in systolic strain in ischemic segments were tracked well with VVI during the different states of regional function. There was a good correlation between circumferential and longitudinal systolic strain by VVI and sonomicrometry (r = 0.88 and r = 0.83, respectively, p < 0.001). Strain measurements in the nonischemic basal segments also demonstrated a significant correlation between the 2 methods (r = 0.65, p < 0.001). Similarly, a significant relation was observed for circumferential and longitudinal SR between the 2 methods (r = 0.94, p < 0.001 and r = 0.90, p < 0.001, respectively). The endocardial velocity relation to changes in strain by sonomicrometry was weaker owing to significant cardiac translation. Conclusions Velocity Vector Imaging, a new feature-tracking method, can accurately assess regional myocardial function at the endocardial level and is a promising clinical tool for the simultaneous quantification of regional and global myocardial function. PMID:18261685

Pathological Gleason prediction through gland ring morphometry in immunofluorescent prostate cancer images

NASA Astrophysics Data System (ADS)

Scott, Richard; Khan, Faisal M.; Zeineh, Jack; Donovan, Michael; Fernandez, Gerardo

2016-03-01

The Gleason score is the most common architectural and morphological assessment of prostate cancer severity and prognosis. There have been numerous quantitative techniques developed to approximate and duplicate the Gleason scoring system. Most of these approaches have been developed in standard H and E brightfield microscopy. Immunofluorescence (IF) image analysis of tissue pathology has recently been proven to be extremely valuable and robust in developing prognostic assessments of disease, particularly in prostate cancer. There have been significant advances in the literature in quantitative biomarker expression as well as characterization of glandular architectures in discrete gland rings. In this work we leverage a new method of segmenting gland rings in IF images for predicting the pathological Gleason; both the clinical and the image specific grade, which may not necessarily be the same. We combine these measures with nuclear specific characteristics as assessed by the MST algorithm. Our individual features correlate well univariately with the Gleason grades, and in a multivariate setting have an accuracy of 85% in predicting the Gleason grade. Additionally, these features correlate strongly with clinical progression outcomes (CI of 0.89), significantly outperforming the clinical Gleason grades (CI of 0.78). This work presents the first assessment of morphological gland unit features from IF images for predicting the Gleason grade.

Pneumococcal pneumonia: clinical features, diagnosis and management in HIV-infected and HIV noninfected patients.

PubMed

Madeddu, Giordano; Fois, Alessandro Giuseppe; Pirina, Pietro; Mura, Maria Stella

2009-05-01

In this review, we focus on the clinical features, diagnosis and management of pneumococcal pneumonia in HIV-infected and noninfected patients, with particular attention to the most recent advances in this area. Classical clinical features are found in young adults, whereas atypical forms occur in immunocompromised patients including HIV-infected individuals. Bacteremic pneumococcal pneumonia is more frequently observed in HIV-infected and also in low-risk patients, according to the Pneumonia Severity Index (PSI). Pneumococcal pneumonia diagnostic process includes physical examination, radiologic findings and microbiologic diagnosis. However, etiologic diagnosis using traditional culture methods is difficult to obtain. In this setting, urinary antigen test, which recognizes Streptococcus pneumoniae cell wall C-polysaccharide, increases the probability of etiologic diagnosis. A correct management approach is crucial in reducing pneumococcal pneumonia mortality. The use of the PSI helps clinicians in deciding between inpatient and outpatient management in immunocompetent individuals, according to Infectious Diseases Society of America (IDSA)-American Thoracic Society (ATS) guidelines. Recent findings support PSI utility also in HIV-infected patients. Recently, efficacy of pneumococcal vaccine in reducing pneumococcal disease incidence has been evidenced in both HIV-infected and noninfected individuals. Rapid diagnosis and correct management together with implementation of preventive measures are crucial in order to reduce pneumococcal pneumonia related incidence and mortality in HIV-infected and noninfected patients.

A family with Wagner syndrome with uveitis and a new versican mutation

PubMed Central

Rothschild, Pierre-Raphaël; Brézin, Antoine P.; Nedelec, Brigitte; des Roziers, Cyril Burin; Ghiotti, Tiffany; Orhant, Lucie; Boimard, Mathieu

2013-01-01

Purpose To report the clinical and molecular findings of a kindred with Wagner syndrome (WS) revealed by intraocular inflammatory features. Methods Eight available family members underwent complete ophthalmologic examination, including laser flare cell meter measurements. Collagen, type II, alpha 1, versican (VCAN), frizzled family receptor 4, low density lipoprotein receptor-related protein 5, tetraspanin 12, and Norrie disease (pseudoglioma) genes were screened with direct sequencing. Results The index case was initially referred for unexplained severe and chronic postoperative bilateral uveitis following a standard cataract surgery procedure. Clinical examination of the proband revealed an optically empty vitreous with avascular vitreous strands and veils, features highly suggestive of WS. The systematic familial ophthalmologic examination identified three additional unsuspected affected family members who also presented with the WS phenotype, including uveitis for one of them. We identified a novel c.4004–6T>A nucleotide substitution at the acceptor splice site of intron 7 of the VCAN gene that segregated with the disease phenotype. Conclusions We present a family with WS with typical WS features and intraocular inflammatory manifestations associated with a novel splice site VCAN mutation. Beyond the structural role in the retinal-vitreous architecture, versican is also emerging as a pivotal mediator of the inflammatory response, supporting uveitis predisposition as a clinical manifestation of WS. PMID:24174867

Oropharyngeal Dysphagia in Dermatomyositis: Associations with Clinical and Laboratory Features Including Autoantibodies

PubMed Central

Mugii, Naoki; Hasegawa, Minoru; Matsushita, Takashi; Hamaguchi, Yasuhito; Oohata, Sacihe; Okita, Hirokazu; Yahata, Tetsutarou; Someya, Fujiko; Inoue, Katsumi; Murono, Shigeyuki; Fujimoto, Manabu; Takehara, Kazuhiko

2016-01-01

Objective Dysphagia develops with low frequency in patients with dermatomyositis. Our objective was to determine the clinical and laboratory features that can estimate the development of dysphagia in dermatomyositis. Methods This study included 92 Japanese patients with adult-onset dermatomyositis. The associations between dysphagia and clinical and laboratory features including disease-specific autoantibodies determined by immunoprecipitation assays were analyzed. Results Videofluoroscopy swallow study (VFSS) was performed for all patients with clinical dysphagia (n = 13, 14.1%) but not for patients without clinical dysphagia. Typical findings of dysphagia (pharyngeal pooling, n = 11 and/or nasal regurgitation, n = 4) was detected by VFSS in all patients with clinical dysphagia. Eleven patients with dysphagia (84.6%) had anti-transcription intermediary factor 1γ (TIF-1γ) antibody. By univariate analysis, the average age and the male to female ratio, internal malignancy, and anti-TIF-1γ antibody were significantly higher and the frequency of interstitial lung diseases and manual muscle testing (MMT) scores of sternomastoid and dertoid muscles were significantly lower in patients with dysphagia than in patients without dysphagia. Among patients with anti-TIF-1γ antibody, the mean age, the ratios of male to female and internal malignancy were significantly higher and mean MMT scores of sternomastoid muscle were significantly lower in patients with dysphagia compared with patients without dysphagia. By multivariable analysis, the risk of dysphagia was strongly associated with the existence of internal malignancy and ant-TIF-1γ antibody and was also associated with reduced scores of manual muscle test of sternomastoid muscle. Dysphagia was markedly improved after the treatment against myositis in all 13 patients. Conclusion These findings indicate that dysphagia can develop frequently in patients with internal malignancy, anti-TIF-1γ antibody, or severe muscle weakness of sternomastoid muscle. PMID:27167831

A feature refinement approach for statistical interior CT reconstruction

NASA Astrophysics Data System (ADS)

Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong

2016-07-01

Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)—minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

A feature refinement approach for statistical interior CT reconstruction.

PubMed

Hu, Zhanli; Zhang, Yunwan; Liu, Jianbo; Ma, Jianhua; Zheng, Hairong; Liang, Dong

2016-07-21

Interior tomography is clinically desired to reduce the radiation dose rendered to patients. In this work, a new statistical interior tomography approach for computed tomography is proposed. The developed design focuses on taking into account the statistical nature of local projection data and recovering fine structures which are lost in the conventional total-variation (TV)-minimization reconstruction. The proposed method falls within the compressed sensing framework of TV minimization, which only assumes that the interior ROI is piecewise constant or polynomial and does not need any additional prior knowledge. To integrate the statistical distribution property of projection data, the objective function is built under the criteria of penalized weighed least-square (PWLS-TV). In the implementation of the proposed method, the interior projection extrapolation based FBP reconstruction is first used as the initial guess to mitigate truncation artifacts and also provide an extended field-of-view. Moreover, an interior feature refinement step, as an important processing operation is performed after each iteration of PWLS-TV to recover the desired structure information which is lost during the TV minimization. Here, a feature descriptor is specifically designed and employed to distinguish structure from noise and noise-like artifacts. A modified steepest descent algorithm is adopted to minimize the associated objective function. The proposed method is applied to both digital phantom and in vivo Micro-CT datasets, and compared to FBP, ART-TV and PWLS-TV. The reconstruction results demonstrate that the proposed method performs better than other conventional methods in suppressing noise, reducing truncated and streak artifacts, and preserving features. The proposed approach demonstrates its potential usefulness for feature preservation of interior tomography under truncated projection measurements.

Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

PubMed Central

Weng, Chunhua

2013-01-01

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment. PMID:22733976

Photographic Reading Center of the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT): Methods and Baseline Results

PubMed Central

Fischer, William S.; Wall, Michael; McDermott, Michael P.; Kupersmith, Mark J.; Feldon, Steven E.

2015-01-01

Purpose. To describe the methods used by the Photographic Reading Center (PRC) of the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and to report baseline assessments of papilledema severity in participants. Methods. Stereoscopic digital images centered on the optic disc and the macula were collected using certified personnel and photographic equipment. Certification of the camera system included standardization and calibration using a model eye. Lay readers assessed disc photos of all eyes using the Frisén grade and performed quantitative measurements of papilledema. Frisén grades by PRC were compared with site investigator clinical grades. Spearman rank correlations were used to quantify associations among disc features and selected clinical variables. Results. Frisén grades according to the PRC and site investigator's grades, matched exactly in 48% of the study eyes and 42% of the fellow eyes and within one grade in 94% of the study eyes and 92% of the fellow eyes. Frisén grade was strongly correlated (r > 0.65, P < 0.0001) with quantitative measures of disc area. Cerebrospinal fluid pressure was weakly associated with Frisén grade and disc area determinations (r ≤ 0.31). Neither Frisén grade nor any fundus feature was associated with perimetric mean deviation. Conclusions. In a prospective clinical trial, lay readers agreed reasonably well with physicians in assessing Frisén grade. Standardization of camera systems enhanced consistency of photographic quality across study sites. Images were affected more by sensors with poor dynamic range than by poor resolution. Frisén grade is highly correlated with quantitative assessment of disc area. (ClinicalTrials.gov number, NCT01003639.) PMID:26024112

Fat embolism syndrome in long bone trauma following vehicular accidents: Experience from a tertiary care hospital in north India

PubMed Central

Koul, Parvaiz A.; Ahmad, Feroze; Gurcoo, Showkat A.; Khan, Umar H.; Naqash, Imtiyaz A.; Sidiq, Suhail; Jan, Rafi Ahmad; Koul, Ajaz N.; Ashraf, Mohammad; Bhat, Mubasher Ahmad

2013-01-01

Background: Fat embolism syndrome (FES) is a clinical problem arising mainly due to fractures particularly of long bones and pelvis. Not much literature is available about FES from the Indian subcontinent. Materials and Methods: Thirty-five patients referred/admitted prospectively over a 3-year period for suspected FES to a north Indian tertiary care center and satisfying the clinical criteria proposed by Gurd and Wilson, and Schonfeld were included in the study. Clinical features, risk factors, complications, response to treatment and any sequelae were recorded. Results: The patients (all male) presented with acute onset breathlessness, 36-120 hours following major bone trauma due to vehicular accidents. Associated features included features of cerebral dysfunction (n = 24, 69%), petechial rash (14%), tachycardia (94%) and fever (46%). Hypoxemia was demonstrable in 80% cases, thrombocytopenia in 91%, anemia in 94% and hypoalbuminemia in 59%. Bilateral alveolar infiltrates were seen on chest radiography in 28 patients and there was evidence of bilateral ground glass appearance in 5 patients on CT. Eleven patients required ventilatory assistance whereas others were treated with supportive management. Three patients expired due to associated sepsis and respiratory failure, whereas others recovered with a mean hospital stay of 9 days. No long term sequelae were observed. Conclusion: FES remains a clinical challenge and is a diagnosis of exclusion based only on clinical grounds because of the absence of any specific laboratory test. A high index of suspicion is required for diagnosis and initiating supportive management in patients with traumatic fractures, especially in those having undergone an invasive orthopedic procedure. PMID:23741088

Research training in integrative medicine: how can we make teaching and learning in research methods more sustainable and engaging?

PubMed

Witt, Claudia M; Withers, Shelly Rafferty

2013-01-01

The aim of this project was to identify strategies for increasing learner engagement and knowledge retention in clinical research training of complementary and integrative medicine (CIM) practitioners, and to offer a conceptual framework to address clinical research training for CIM practitioners. In a featured large-group discussion (15min presentation and 30min discussion), two questions (strategies that are recommended to overcome these barriers; relevant aspects for a framework for building sustainable knowledge) were put to the audience. The sample consisted of 43 participants at the International Congress of Educators in Complementary and Alternative Medicine, in Washington, DC, in October 2012. The featured discussion was moderated and detailed notes were taken. Notes were synthesized and discussed by both authors until consensus was reached. Based on the results from the featured discussion session and a focused literature search, a framework for building sustainable knowledge and skills in clinical research for CIM practitioners was developed. Participants' responses to the questions of engagement and sustainability included curricular structures, pedagogical strategies for instruction, the use of digital tools to extend the learning experience, the necessity to ground instruction firmly in the medical literature of the field, and the relevance of mentoring. Key considerations for building sustainable knowledge in clinical research for CIM practitioners are as follows: (1) prioritizing clinical research training, (2) issues of curriculum and pedagogy, (3) technology/digital tools, (4) administrative challenges, (5) supporting the formation of communities of practice, and (6) cultural perspectives of CIM practitioners. © 2013 Elsevier Inc. All rights reserved.

Clinical spectrum of 4H leukodystrophy caused by POLR3A and POLR3B mutations

PubMed Central

Vanderver, Adeline; van Spaendonk, Rosalina M.L.; Schiffmann, Raphael; Brais, Bernard; Bugiani, Marianna; Sistermans, Erik; Catsman-Berrevoets, Coriene; Kros, Johan M.; Pinto, Pedro Soares; Pohl, Daniela; Tirupathi, Sandya; Strømme, Petter; de Grauw, Ton; Fribourg, Sébastien; Demos, Michelle; Pizzino, Amy; Naidu, Sakkubai; Guerrero, Kether; van der Knaap, Marjo S.; Bernard, Geneviève

2014-01-01

Objective: To study the clinical and radiologic spectrum and genotype–phenotype correlation of 4H (hypomyelination, hypodontia, hypogonadotropic hypogonadism) leukodystrophy caused by mutations in POLR3A or POLR3B. Methods: We performed a multinational cross-sectional observational study of the clinical, radiologic, and molecular characteristics of 105 mutation-proven cases. Results: The majority of patients presented before 6 years with gross motor delay or regression. Ten percent had an onset beyond 10 years. The disease course was milder in patients with POLR3B than in patients with POLR3A mutations. Other than the typical neurologic, dental, and endocrine features, myopia was seen in almost all and short stature in 50%. Dental and hormonal findings were not invariably present. Mutations in POLR3A and POLR3B were distributed throughout the genes. Except for French Canadian patients, patients from European backgrounds were more likely to have POLR3B mutations than other populations. Most patients carried the common c.1568T>A POLR3B mutation on one allele, homozygosity for which causes a mild phenotype. Systematic MRI review revealed that the combination of hypomyelination with relative T2 hypointensity of the ventrolateral thalamus, optic radiation, globus pallidus, and dentate nucleus, cerebellar atrophy, and thinning of the corpus callosum suggests the diagnosis. Conclusions: 4H is a well-recognizable clinical entity if all features are present. Mutations in POLR3A are associated with a more severe clinical course. MRI characteristics are helpful in addressing the diagnosis, especially if patients lack the cardinal non-neurologic features. PMID:25339210

Virtual patients to explore and develop clinical case summary statement skills amongst Japanese resident physicians: a mixed methods study.

PubMed

Heist, Brian S; Kishida, Naoki; Deshpande, Gautam; Hamaguchi, Sugihiro; Kobayashi, Hiroyuki

2016-02-01

In Western clinical training, formulation of a summary statement (SS) is a core exercise for articulation, evaluation, and improvement of clinical reasoning (CR). In Japanese clinical training, structured guidance in developing CR, including opportunity for SS practice, is uncommon, and the present status of case summarization skills is unclear. We used Virtual Patients (VPs) to explore Japanese junior residents' SS styles and the effectiveness of VPs on improving SS quality. All first-year junior resident physicians at 4 residency programs (n = 54) were assigned randomized sequences of 5 VP modules, rolled out at 6 day intervals. During each module, participants free-texted a case summary and then reviewed a model summary. Thematic analysis was used to identify SS styles and each SS was categorized accordingly. Frequency of SS styles, and SS CR quality determined by 1) an internally developed Key Feature rubric and 2) demonstration of semantic qualification, were compared across modules. Four SS styles were identified: numbered features matched to differential diagnoses, differential diagnoses with supportive comments, feature listing, and narrative summarization. From module #1 to #5, significant increases in the narrative summarization SS style (p = 0.016), SS CR quality score (p = 0.021) and percentage of semantically driven SS (p = 0.003) were observed. Our study of Japanese junior residents identified distinct clinical case summary statement styles, and observed adoption of the narrative summarization style and improvement in the CR quality of summary statements during a series of VP cases.

Using GIS for spatial analysis of rectal lesions in the human body.

PubMed

Garb, Jane L; Ganai, Sabha; Skinner, Ric; Boyd, Christopher S; Wait, Richard B

2007-03-15

Geographic Information Systems (GIS) have been used in a wide variety of applications to integrate data and explore the spatial relationship of geographic features. Traditionally this has referred to features on the surface of the earth. However, it is possible to apply GIS in medicine, at the scale of the human body, to visualize and analyze anatomic and clinical features. In the present study we used GIS to examine the findings of transanal endoscopic microsurgery (TEM), a minimally-invasive procedure to locate and remove both benign and cancerous lesions of the rectum. Our purpose was to determine whether anatomic features of the human rectum and clinical findings at the time of surgery could be rendered in a GIS and spatially analyzed for their relationship to clinical outcomes. Maps of rectal topology were developed in two and three dimensions. These maps highlight anatomic features of the rectum and the location of lesions found on TEM. Spatial analysis demonstrated a significant relationship between anatomic location of the lesion and procedural failure. This study demonstrates the feasibility of rendering anatomical locations and clinical events in a GIS and its value in clinical research. This allows the visualization and spatial analysis of clinical and pathologic features, increasing our awareness of the relationship between anatomic features and clinical outcomes as well as enhancing our understanding and management of this disease process.

Using GIS for spatial analysis of rectal lesions in the human body

PubMed Central

Garb, Jane L; Ganai, Sabha; Skinner, Ric; Boyd, Christopher S; Wait, Richard B

2007-01-01

Background Geographic Information Systems (GIS) have been used in a wide variety of applications to integrate data and explore the spatial relationship of geographic features. Traditionally this has referred to features on the surface of the earth. However, it is possible to apply GIS in medicine, at the scale of the human body, to visualize and analyze anatomic and clinical features. In the present study we used GIS to examine the findings of transanal endoscopic microsurgery (TEM), a minimally-invasive procedure to locate and remove both benign and cancerous lesions of the rectum. Our purpose was to determine whether anatomic features of the human rectum and clinical findings at the time of surgery could be rendered in a GIS and spatially analyzed for their relationship to clinical outcomes. Results Maps of rectal topology were developed in two and three dimensions. These maps highlight anatomic features of the rectum and the location of lesions found on TEM. Spatial analysis demonstrated a significant relationship between anatomic location of the lesion and procedural failure. Conclusion This study demonstrates the feasibility of rendering anatomical locations and clinical events in a GIS and its value in clinical research. This allows the visualization and spatial analysis of clinical and pathologic features, increasing our awareness of the relationship between anatomic features and clinical outcomes as well as enhancing our understanding and management of this disease process. PMID:17362510

Lumbar spinal canal MRI diameter is smaller in herniated disc cauda equina syndrome patients

PubMed Central

Kruit, Mark C.; Peul, Wilco C.; Vleggeert-Lankamp, Carmen L. A.

2017-01-01

Introduction Correlation between magnetic resonance imaging (MRI) and clinical features in cauda equina syndrome (CES) is unknown; nor is known whether there are differences in MRI spinal canal size between lumbar herniated disc patients with CES versus lumbar herniated discs patients without CES, operated for sciatica. The aims of this study are 1) evaluating the association of MRI features with clinical presentation and outcome of CES and 2) comparing lumbar spinal canal diameters of lumbar herniated disc patients with CES versus lumbar herniated disc patients without CES, operated because of sciatica. Methods MRIs of CES patients were assessed for the following features: level of disc lesion, type (uni- or bilateral) and severity of caudal compression. Pre- and postoperative clinical features (micturition dysfunction, defecation dysfunction, altered sensation of the saddle area) were retrieved from the medical files. In addition, anteroposterior (AP) lumbar spinal canal diameters of CES patients were measured at MRI. AP diameters of lumbar herniated disc patients without CES, operated for sciatica, were measured for comparison. Results 48 CES patients were included. At MRI, bilateral compression was seen in 82%; complete caudal compression in 29%. MRI features were not associated with clinical presentation nor outcome. AP diameter was measured for 26 CES patients and for 31 lumbar herniated disc patients without CES, operated for sciatica. Comparison displayed a significant smaller AP diameter of the lumbar spinal canal in CES patients (largest p = 0.002). Compared to average diameters in literature, diameters of CES patients were significantly more often below average than that of the sciatica patients (largest p = 0.021). Conclusion This is the first study demonstrating differences in lumbar spinal canal size between lumbar herniated disc patients with CES and lumbar herniated disc patients without CES, operated for sciatica. This finding might imply that lumbar herniated disc patients with a relative small lumbar spinal canal might need to be approached differently in managing complaints of herniated disc. Since the number of studied patients is relatively small, further research should be conducted before clinical consequences are considered. PMID:29023556

Application of dermoscopy image analysis technique in diagnosing urethral condylomata acuminata.

PubMed

Zhang, Yunjie; Jiang, Shuang; Lin, Hui; Guo, Xiaojuan; Zou, Xianbiao

2018-01-01

In this study, cases with suspected urethral condylomata acuminata were examined by dermoscopy, in order to explore an effective method for clinical. To study the application of dermoscopy image analysis technique in clinical diagnosis of urethral condylomata acuminata. A total of 220 suspected urethral condylomata acuminata were clinically diagnosed first with the naked eyes, and then by using dermoscopy image analysis technique. Afterwards, a comparative analysis was made for the two diagnostic methods. Among the 220 suspected urethral condylomata acuminata, there was a higher positive rate by dermoscopy examination than visual observation. Dermoscopy examination technique is still restricted by its inapplicability in deep urethral orifice and skin wrinkles, and concordance between different clinicians may also vary. Dermoscopy image analysis technique features a high sensitivity, quick and accurate diagnosis and is non-invasive, and we recommend its use.

Magnetic resonance neurography and diffusion tensor imaging: origins, history, and clinical impact of the first 50,000 cases with an assessment of efficacy and utility in a prospective 5000-patient study group.

PubMed

Filler, Aaron

2009-10-01

Methods were invented that made it possible to image peripheral nerves in the body and to image neural tracts in the brain. The history, physical basis, and dyadic tensor concept underlying the methods are reviewed. Over a 15-year period, these techniques-magnetic resonance neurography (MRN) and diffusion tensor imaging-were deployed in the clinical and research community in more than 2500 published research reports and applied to approximately 50,000 patients. Within this group, approximately 5000 patients having MRN were carefully tracked on a prospective basis. A uniform Neurography imaging methodology was applied in the study group, and all images were reviewed and registered by referral source, clinical indication, efficacy of imaging, and quality. Various classes of image findings were identified and subjected to a variety of small targeted prospective outcome studies. Those findings demonstrated to be clinically significant were then tracked in the larger clinical volume data set. MRN demonstrates mechanical distortion of nerves, hyperintensity consistent with nerve irritation, nerve swelling, discontinuity, relations of nerves to masses, and image features revealing distortion of nerves at entrapment points. These findings are often clinically relevant and warrant full consideration in the diagnostic process. They result in specific pathological diagnoses that are comparable to electrodiagnostic testing in clinical efficacy. A review of clinical outcome studies with diffusion tensor imaging also shows convincing utility. MRN and diffusion tensor imaging neural tract imaging have been validated as indispensable clinical diagnostic methods that provide reliable anatomic pathological information. There is no alternative diagnostic method in many situations. With the elapsing of 15 years, tens of thousands of imaging studies, and thousands of publications, these methods should no longer be considered experimental.

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

Galavis, P; Friedman, K; Chandarana, H

Purpose: Radiomics involves the extraction of texture features from different imaging modalities with the purpose of developing models to predict patient treatment outcomes. The purpose of this study is to investigate texture feature reproducibility across [18F]FDG PET/CT and [18F]FDG PET/MR imaging in patients with primary malignancies. Methods: Twenty five prospective patients with solid tumors underwent clinical [18F]FDG PET/CT scan followed by [18F]FDG PET/MR scans. In all patients the lesions were identified using nuclear medicine reports. The images were co-registered and segmented using an in-house auto-segmentation method. Fifty features, based on the intensity histogram, second and high order matrices, were extractedmore » from the segmented regions from both image data sets. One-way random-effects ANOVA model of the intra-class correlation coefficient (ICC) was used to establish texture feature correlations between both data sets. Results: Fifty features were classified based on their ICC values, which were found in the range from 0.1 to 0.86, in three categories: high, intermediate, and low. Ten features extracted from second and high-order matrices showed large ICC ≥ 0.70. Seventeen features presented intermediate 0.5 ≤ ICC ≤ 0.65 and the remaining twenty three presented low ICC ≤ 0.45. Conclusion: Features with large ICC values could be reliable candidates for quantification as they lead to similar results from both imaging modalities. Features with small ICC indicates a lack of correlation. Therefore, the use of these features as a quantitative measure will lead to different assessments of the same lesion depending on the imaging modality from where they are extracted. This study shows the importance of the need for further investigation and standardization of features across multiple imaging modalities.« less

Targeted exome sequencing and chromosomal microarray for the molecular diagnosis of nevoid basal cell carcinoma syndrome.

PubMed

Matsudate, Yoshihiro; Naruto, Takuya; Hayashi, Yumiko; Minami, Mitsuyoshi; Tohyama, Mikiko; Yokota, Kenji; Yamada, Daisuke; Imoto, Issei; Kubo, Yoshiaki

2017-06-01

Nevoid basal cell carcinoma syndrome (NBCCS) is an autosomal dominant disorder mainly caused by heterozygous mutations of PTCH1. In addition to characteristic clinical features, detection of a mutation in causative genes is reliable for the diagnosis of NBCCS; however, no mutations have been identified in some patients using conventional methods. To improve the method for the molecular diagnosis of NBCCS. We performed targeted exome sequencing (TES) analysis using a multi-gene panel, including PTCH1, PTCH2, SUFU, and other sonic hedgehog signaling pathway-related genes, based on next-generation sequencing (NGS) technology in 8 cases in whom possible causative mutations were not detected by previously performed conventional analysis and 2 recent cases of NBCCS. Subsequent analysis of gross deletion within or around PTCH1 detected by TES was performed using chromosomal microarray (CMA). Through TES analysis, specific single nucleotide variants or small indels of PTCH1 causing inferred amino acid changes were identified in 2 novel cases and 2 undiagnosed cases, whereas gross deletions within or around PTCH1, which are validated by CMA, were found in 3 undiagnosed cases. However, no mutations were detected even by TES in 3 cases. Among 3 cases with gross deletions of PTCH1, deletions containing the entire PTCH1 and additional neighboring genes were detected in 2 cases, one of which exhibited atypical clinical features, such as severe mental retardation, likely associated with genes located within the 4.3Mb deleted region, especially. TES-based simultaneous evaluation of sequences and copy number status in all targeted coding exons by NGS is likely to be more useful for the molecular diagnosis of NBCCS than conventional methods. CMA is recommended as a subsequent analysis for validation and detailed mapping of deleted regions, which may explain the atypical clinical features of NBCCS cases. Copyright © 2017 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.

Problems and processes in medical encounters: the cases method of dialogue analysis.

PubMed

Laws, M Barton; Taubin, Tatiana; Bezreh, Tanya; Lee, Yoojin; Beach, Mary Catherine; Wilson, Ira B

2013-05-01

To develop methods to reliably capture structural and dynamic temporal features of clinical interactions. Observational study of 50 audio-recorded routine outpatient visits to HIV specialty clinics, using innovative analytic methods. The comprehensive analysis of the structure of encounters system (CASES) uses transcripts coded for speech acts, then imposes larger-scale structural elements: threads--the problems or issues addressed; and processes within threads--basic tasks of clinical care labeled presentation, information, resolution (decision making) and Engagement (interpersonal exchange). Threads are also coded for the nature of resolution. 61% of utterances are in presentation processes. Provider verbal dominance is greatest in information and resolution processes, which also contain a high proportion of provider directives. About half of threads result in no action or decision. Information flows predominantly from patient to provider in presentation processes, and from provider to patient in information processes. Engagement is rare. In this data, resolution is provider centered; more time for patient participation in resolution, or interpersonal engagement, would have to come from presentation. Awareness of the use of time in clinical encounters, and the interaction processes associated with various tasks, may help make clinical communication more efficient and effective. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

PubMed

Kyathanahally, Sreenath P; Mocioiu, Victor; Pedrosa de Barros, Nuno; Slotboom, Johannes; Wright, Alan J; Julià-Sapé, Margarida; Arús, Carles; Kreis, Roland

2018-05-01

To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Expression of HIWI in human esophageal squamous cell carcinoma is significantly associated with poorer prognosis

PubMed Central

2009-01-01

Background HIWI, the human homologue of Piwi family, is present in CD34+ hematopoietic stem cells and germ cells, but not in well-differentiated cell populations, indicating that HIWI may play an impotent role in determining or maintaining stemness of these cells. That HIWI expression has been detected in several type tumours may suggest its association with clinical outcome in cancer patients. Methods With the methods of real-time PCR, western blot, immunocytochemistry and immunohistochemistry, the expression of HIWI in three esophageal squamous cancer cell lines KYSE70, KYSE140 and KYSE450 has been characterized. Then, we investigated HIWI expression in a series of 153 esophageal squamous cell carcinomas using immunohistochemistry and explored its association with clinicopathological features. Results The expression of HIWI was observed in tumour cell nuclei or/and cytoplasm in 137 (89.5%) cases, 16 (10.5%) cases were negative in both nuclei and cytoplasm. 86 (56.2%) were strongly positive in cytoplasm, while 49 (32.0%) were strongly positive in nuclei. The expression level of HIWI in cytoplasm of esophageal cancer cells was significantly associated with histological grade (P = 0.011), T stage (P = 0.035), and clinic outcome (P < 0.001), while there was no correlation between the nuclear HIWI expression and clinicopathological features. Conclusion The expression of HIWI in the cytoplasm of esophageal cancer cells is significantly associated with higher histological grade, clinical stage and poorer clinical outcome, indicating its possible involvement in cancer development. PMID:19995427

A relativity concept in mesenchymal stromal cell manufacturing.

PubMed

Martin, Ivan; De Boer, Jan; Sensebe, Luc

2016-05-01

Mesenchymal stromal cells (MSCs) are being experimentally tested in several biological systems and clinical settings with the aim of verifying possible therapeutic effects for a variety of indications. MSCs are also known to be heterogeneous populations, with phenotypic and functional features that depend heavily on the individual donor, the harvest site, and the culture conditions. In the context of this multidimensional complexity, a recurrent question is whether it is feasible to produce MSC batches as "standard" therapeutics, possibly within scalable manufacturing systems. Here, we provide a short overview of the literature on different culture methods for MSCs, including those employing innovative technologies, and of some typically assessed functional features (e.g., growth, senescence, genomic stability, clonogenicity, etc.). We then offer our perspective of a roadmap on how to identify and refine manufacturing systems for MSCs intended for specific clinical indications. We submit that the vision of producing MSCs according to a unique standard, although commercially attractive, cannot yet be scientifically substantiated. Instead, efforts should be concentrated on standardizing methods for characterization of MSCs generated by different groups, possibly covering a vast gamut of functionalities. Such assessments, combined with hypotheses on the therapeutic mode of action and associated clinical data, should ultimately allow definition of in-process controls and measurable release criteria for MSC manufacturing. These will have to be validated as predictive of potency in suitable pre-clinical models and of therapeutic efficacy in patients. Copyright © 2016 International Society for Cellular Therapy. Published by Elsevier Inc. All rights reserved.

Clinical features, proximate causes, and consequences of active convulsive epilepsy in Africa

PubMed Central

Kariuki, Symon M; Matuja, William; Akpalu, Albert; Kakooza-Mwesige, Angelina; Chabi, Martin; Wagner, Ryan G; Connor, Myles; Chengo, Eddie; Ngugi, Anthony K; Odhiambo, Rachael; Bottomley, Christian; White, Steven; Sander, Josemir W; Neville, Brian G R; Newton, Charles R J C

2014-01-01

Purpose Epilepsy is common in sub-Saharan Africa (SSA), but the clinical features and consequences are poorly characterized. Most studies are hospital-based, and few studies have compared different ecological sites in SSA. We described active convulsive epilepsy (ACE) identified in cross-sectional community-based surveys in SSA, to understand the proximate causes, features, and consequences. Methods We performed a detailed clinical and neurophysiologic description of ACE cases identified from a community survey of 584,586 people using medical history, neurologic examination, and electroencephalography (EEG) data from five sites in Africa: South Africa; Tanzania; Uganda; Kenya; and Ghana. The cases were examined by clinicians to discover risk factors, clinical features, and consequences of epilepsy. We used logistic regression to determine the epilepsy factors associated with medical comorbidities. Key Findings Half (51%) of the 2,170 people with ACE were children and 69% of seizures began in childhood. Focal features (EEG, seizure types, and neurologic deficits) were present in 58% of ACE cases, and these varied significantly with site. Status epilepticus occurred in 25% of people with ACE. Only 36% received antiepileptic drugs (phenobarbital was the most common drug [95%]), and the proportion varied significantly with the site. Proximate causes of ACE were adverse perinatal events (11%) for onset of seizures before 18 years; and acute encephalopathy (10%) and head injury prior to seizure onset (3%). Important comorbidities were malnutrition (15%), cognitive impairment (23%), and neurologic deficits (15%). The consequences of ACE were burns (16%), head injuries (postseizure) (1%), lack of education (43%), and being unmarried (67%) or unemployed (57%) in adults, all significantly more common than in those without epilepsy. Significance There were significant differences in the comorbidities across sites. Focal features are common in ACE, suggesting identifiable and preventable causes. Malnutrition and cognitive and neurologic deficits are common in people with ACE and should be integrated into the management of epilepsy in this region. Consequences of epilepsy such as burns, lack of education, poor marriage prospects, and unemployment need to be addressed. PMID:24116877

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

Histopathologic Grading of Anaplasia in Retinoblastoma

PubMed Central

Mendoza, Pia R.; Specht, Charles S.; Hubbard, G. Baker; Wells, Jill R.; Lynn, Michael J.; Zhang, Qing; Kong, Jun; Grossniklaus, Hans E.

2014-01-01

Purpose To determine whether the degree of tumor anaplasia has prognostic value by evaluating its correlation with high-risk histopathologic features and clinical outcomes in a series of retinoblastoma patients. Design Retrospective clinicopathologic study. Methods The clinical and pathologic findings in 266 patients who underwent primary enucleation for retinoblastoma were reviewed. The histologic degree of anaplasia was graded as retinocytoma, mild, moderate, or severe as defined by increasing cellular pleomorphism, number of mitoses, nuclear size, and nuclear hyperchromatism. Nuclear morphometric characteristics were measured. The clinical and pathologic data of 125 patients were compared using Kaplan-Meier estimates of survival. Fisher's exact test and multivariate regression were used to analyze the association between anaplasia grade and high-risk histologic features. Results Increasing grade of anaplasia was associated with decreased overall survival (p=0.003) and increased risk of metastasis (p=0.0007). Histopathologic features that were associated with anaplasia included optic nerve invasion (p<0.0001), choroidal invasion (p=<0.0001), and anterior segment invasion (p=0.04). Multivariate analysis considering high-risk histopathology and anaplasia grading as predictors of distant metastasis and death showed that high-risk histopathology was statistically significant as an independent predictor (p=0.01 for metastasis, p=0.03 for death) but anaplasia was not (p=0.63 for metastasis, p=0.30 for death). In the absence of high-risk features, however, severe anaplasia identified an additional risk for metastasis (p=0.0004) and death (p=0.01). Conclusion Grading of anaplasia may be a useful adjunct to standard histopathologic criteria in identifying retinoblastoma patients who do not have high-risk histologic features but still have an increased risk of metastasis and may need adjuvant therapy. PMID:25528954

Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival

PubMed Central

Pérez-Beteta, Julián; Luque, Belén; Arregui, Elena; Calvo, Manuel; Borrás, José M; López, Carlos; Martino, Juan; Velasquez, Carlos; Asenjo, Beatriz; Benavides, Manuel; Herruzo, Ismael; Martínez-González, Alicia; Pérez-Romasanta, Luis; Arana, Estanislao; Pérez-García, Víctor M

2016-01-01

Objective: The main objective of this retrospective work was the study of three-dimensional (3D) heterogeneity measures of post-contrast pre-operative MR images acquired with T1 weighted sequences of patients with glioblastoma (GBM) as predictors of clinical outcome. Methods: 79 patients from 3 hospitals were included in the study. 16 3D textural heterogeneity measures were computed including run-length matrix (RLM) features (regional heterogeneity) and co-occurrence matrix (CM) features (local heterogeneity). The significance of the results was studied using Kaplan–Meier curves and Cox proportional hazards analysis. Correlation between the variables of the study was assessed using the Spearman's correlation coefficient. Results: Kaplan–Meyer survival analysis showed that 4 of the 11 RLM features and 4 of the 5 CM features considered were robust predictors of survival. The median survival differences in the most significant cases were of over 6 months. Conclusion: Heterogeneity measures computed on the post-contrast pre-operative T1 weighted MR images of patients with GBM are predictors of survival. Advances in knowledge: Texture analysis to assess tumour heterogeneity has been widely studied. However, most works develop a two-dimensional analysis, focusing only on one MRI slice to state tumour heterogeneity. The study of fully 3D heterogeneity textural features as predictors of clinical outcome is more robust and is not dependent on the selected slice of the tumour. PMID:27319577

Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

PubMed Central

Gruss, Sascha; Treister, Roi; Werner, Philipp; Traue, Harald C.; Crawcour, Stephen; Andrade, Adriano; Walter, Steffen

2015-01-01

Background The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. Methods In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. Results We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. Conclusion The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment. PMID:26474183

Low-dimensional dynamical characterization of human performance of cancer patients using motion data.

PubMed

Hasnain, Zaki; Li, Ming; Dorff, Tanya; Quinn, David; Ueno, Naoto T; Yennu, Sriram; Kolatkar, Anand; Shahabi, Cyrus; Nocera, Luciano; Nieva, Jorge; Kuhn, Peter; Newton, Paul K

2018-05-18

Biomechanical characterization of human performance with respect to fatigue and fitness is relevant in many settings, however is usually limited to either fully qualitative assessments or invasive methods which require a significant experimental setup consisting of numerous sensors, force plates, and motion detectors. Qualitative assessments are difficult to standardize due to their intrinsic subjective nature, on the other hand, invasive methods provide reliable metrics but are not feasible for large scale applications. Presented here is a dynamical toolset for detecting performance groups using a non-invasive system based on the Microsoft Kinect motion capture sensor, and a case study of 37 cancer patients performing two clinically monitored tasks before and after therapy regimens. Dynamical features are extracted from the motion time series data and evaluated based on their ability to i) cluster patients into coherent fitness groups using unsupervised learning algorithms and to ii) predict Eastern Cooperative Oncology Group performance status via supervised learning. The unsupervised patient clustering is comparable to clustering based on physician assigned Eastern Cooperative Oncology Group status in that they both have similar concordance with change in weight before and after therapy as well as unexpected hospitalizations throughout the study. The extracted dynamical features can predict physician, coordinator, and patient Eastern Cooperative Oncology Group status with an accuracy of approximately 80%. The non-invasive Microsoft Kinect sensor and the proposed dynamical toolset comprised of data preprocessing, feature extraction, dimensionality reduction, and machine learning offers a low-cost and general method for performance segregation and can complement existing qualitative clinical assessments. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

Automatic evaluation of intrapartum fetal heart rate recordings: a comprehensive analysis of useful features.

PubMed

Chudáček, V; Spilka, J; Janků, P; Koucký, M; Lhotská, L; Huptych, M

2011-08-01

Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2018-03-01

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Acquired bilateral telangiectatic macules: a distinct clinical entity.

PubMed

Park, Ji-Hye; Lee, Dong Jun; Lee, Yoo-Jung; Jang, Yong Hyun; Kang, Hee Young; Kim, You Chan

2014-09-01

We evaluated 13 distinct patients with multiple telangiectatic pigmented macules confined mostly to the upper arms to determine if the clinical and histopathological features of these cases might represent a specific clinical entity. We retrospectively investigated the clinical, histopathologic, and immunohistochemical features of 13 patients with multiple telangiectatic pigmented macules on the upper arms who presented between January 2003 and December 2012. Epidermal pigmentation, melanogenic activity, melanocyte number, vascularity, epidermal thickness, and perivascular mast cell number of the specimens were evaluated. Clinically, the condition favored middle-aged men. On histopathologic examination, the lesional skin showed capillary proliferation and telangiectasia in the upper dermis. Histochemical and immunohistochemical analysis revealed basal hyperpigmentation and increased melanogenic activity in the lesional skin (P < .05). No significant difference in epidermal thickness or mast cell number was observed between the normal perilesional skin and the lesional skin. The clinical and histopathologic features of these lesions were relatively consistent in all patients. In addition, the features are quite distinct from other diseases. Based on clinical and histologic features, we suggest the name acquired bilateral telangiectatic macules for this new entity.

Association between US features of primary tumor and axillary lymph node metastasis in patients with clinical T1-T2N0 breast cancer.

PubMed

Bae, Min Sun; Shin, Sung Ui; Song, Sung Eun; Ryu, Han Suk; Han, Wonshik; Moon, Woo Kyung

2018-04-01

Background Most patients with early-stage breast cancer have clinically negative lymph nodes (LNs). However, 15-20% of patients have axillary nodal metastasis based on the sentinel LN biopsy. Purpose To assess whether ultrasound (US) features of a primary tumor are associated with axillary LN metastasis in patients with clinical T1-T2N0 breast cancer. Material and Methods This retrospective study included 138 consecutive patients (median age = 51 years; age range = 27-78 years) who underwent breast surgery with axillary LN evaluation for clinically node-negative T1-T2 breast cancer. Three radiologists blinded to the axillary surgery results independently reviewed the US images. Tumor distance from the skin and distance from the nipple were determined based on the US report. Association between US features of a breast tumor and axillary LN metastasis was assessed using a multivariate logistic regression model after controlling for clinicopathologic variables. Results Of the 138 patients, 28 (20.3%) had nodal metastasis. At univariate analysis, tumor distance from the skin ( P = 0.019), tumor size on US ( P = 0.023), calcifications ( P = 0.036), architectural distortion ( P = 0.001), and lymphovascular invasion ( P = 0.049) were associated with axillary LN metastasis. At multivariate analysis, shorter skin-to-tumor distance (odds ratio [OR] = 4.15; 95% confidence interval [CI] = 1.01-16.19; P = 0.040) and masses with associated architectural distortion (OR = 3.80; 95% CI = 1.57-9.19; P = 0.003) were independent predictors of axillary LN metastasis. Conclusion US features of breast cancer can be promising factors associated with axillary LN metastasis in patients with clinically node-negative early-stage breast cancer.

Neuroimaging features in subacute encephalopathy with seizures in alcoholics (SESA syndrome)

PubMed Central

Drake-Pérez, Marta; de Lucas, Enrique Marco; Lyo, John; Fernández-Torre, José L.

2017-01-01

Purpose To describe the neuroimaging findings in subacute encephalopathy with seizures in alcoholics (SESA syndrome). Methods We reviewed all cases reported previously, as well as 4 patients diagnosed in our center. We included a total of 8 patients. All subjects had clinical and EEG findings compatible with SESA syndrome and at least one MRI study that did not show other underlying condition that could be responsible for the clinical presentation. Results Initial MRI studies revealed the following features: cortical-subcortical areas of increased T2/FLAIR signal and restricted diffusion (6 patients), hyperperfusion (3 patients), atrophy (5 patients), chronic microvascular ischemic changes (4 patients). Follow-up MRI was performed in half of the patients, all showing a resolution of the hyperintense lesions, but developing focal atrophic changes in 75%. Conclusions SESA syndrome should be included among the alcohol-related encephalopathies. Its radiological features include transient cortical-subcortical T2-hyperintense areas with restricted diffusion (overlapping the typical findings in status epilepticus) observed in a patient with atrophy and chronic multifocal vascular lesions. PMID:27391464

Mapping Gene Associations in Human Mitochondria using Clinical Disease Phenotypes

PubMed Central

Scharfe, Curt; Lu, Henry Horng-Shing; Neuenburg, Jutta K.; Allen, Edward A.; Li, Guan-Cheng; Klopstock, Thomas; Cowan, Tina M.; Enns, Gregory M.; Davis, Ronald W.

2009-01-01

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes. PMID:19390613

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning.

PubMed

Tuyisenge, Viateur; Trebaul, Lena; Bhattacharjee, Manik; Chanteloup-Forêt, Blandine; Saubat-Guigui, Carole; Mîndruţă, Ioana; Rheims, Sylvain; Maillard, Louis; Kahane, Philippe; Taussig, Delphine; David, Olivier

2018-03-01

Intracranial electroencephalographic (iEEG) recordings contain "bad channels", which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features. The features quantified signals' variance, spatial-temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers. We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data. The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data. This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Quantitative CT based radiomics as predictor of resectability of pancreatic adenocarcinoma

NASA Astrophysics Data System (ADS)

van der Putten, Joost; Zinger, Svitlana; van der Sommen, Fons; de With, Peter H. N.; Prokop, Mathias; Hermans, John

2018-02-01

In current clinical practice, the resectability of pancreatic ductal adenocarcinoma (PDA) is determined subjec- tively by a physician, which is an error-prone procedure. In this paper, we present a method for automated determination of resectability of PDA from a routine abdominal CT, to reduce such decision errors. The tumor features are extracted from a group of patients with both hypo- and iso-attenuating tumors, of which 29 were resectable and 21 were not. The tumor contours are supplied by a medical expert. We present an approach that uses intensity, shape, and texture features to determine tumor resectability. The best classification results are obtained with fine Gaussian SVM and the L0 Feature Selection algorithms. Compared to expert predictions made on the same dataset, our method achieves better classification results. We obtain significantly better results on correctly predicting non-resectability (+17%) compared to a expert, which is essential for patient treatment (negative prediction value). Moreover, our predictions of resectability exceed expert predictions by approximately 3% (positive prediction value).

Detection of EML4-ALK fusion gene and features associated with EGFR mutations in Chinese patients with non-small-cell lung cancer

PubMed Central

Wen, Miaomiao; Wang, Xuejiao; Sun, Ying; Xia, Jinghua; Fan, Liangbo; Xing, Hao; Zhang, Zhipei; Li, Xiaofei

2016-01-01

Purpose Echinoderm microtubule-associated protein-like 4–anaplastic lymphoma kinase (EML4-ALK) and epidermal growth factor receptor (EGFR) define specific molecular subsets of lung cancer with distinct clinical features. We aimed at revealing the clinical features of EML4-ALK fusion gene and EGFR mutation in non-small-cell lung cancer (NSCLC). Methods We enrolled 694 Chinese patients with NSCLC for analysis. EML4-ALK fusion gene was analyzed by real-time polymerase chain reaction, and EGFR mutations were analyzed by amplified refractory mutation system. Results Among the 694 patients, 60 (8.65%) patients had EML4-ALK fusions. In continuity correction χ2 test analysis, EML4-ALK fusion gene was correlated with sex, age, smoking status, and histology, but no significant association was observed between EML4-ALK fusion gene and clinical stage. A total of 147 (21.18%) patients had EGFR mutations. In concordance with previous reports, EGFR mutation was correlated with age, smoking status, histology, and clinical stage, whereas patient age was not significantly associated with EGFR mutation. Meanwhile, to our surprise, six (0.86%) patients had coexisting EML4-ALK fusions and EGFR mutations. Conclusion EML4-ALK fusion gene defines a new molecular subset in patients with NSCLC. Six patients who harbored both EML4-ALK fusion genes and EGFR mutations were identified in our study. The EGFR mutations and the EML4-ALK fusion genes are coexistent. PMID:27103824

Clinical and Imaging Findings in Childhood Posterior Reversible Encephalopathy Syndrome

PubMed Central

GUNGOR, Serdal; KILIC, Betul; TABEL, Yilmaz; SELIMOGLU, Ayse; OZGEN, Unsal; YILMAZ, Sezai

2018-01-01

Objective Posterior reversible encephalopathy syndrome (PRES) is characterized by typical radiologic findings in the posterior regions of the cerebral hemispheres and cerebellum. The symptoms include headache, nausea, vomiting, visual disturbances, focal neurologic deficits, and seizures. The aim of this study is to evaluate the clinical and radiological features of PRES in children and to emphasize the recognition of atypical features. Materials & Methods We retrospectively examined 23 children with PRES from Mar 2010-Apr 2015 in Inonu University Turgut Ozal Medical Center in Turkey. We compared the clinical features and cranial MRI findings between underlying diseases of PRES. Results The most common precipitating factors were hypertension (78.2%) and medications, namely immunosuppressive and antineoplastic agents (60.8%). Manifestations included mental changes (100%), seizures (95.6%), headache (60.8%), and visual disturbances (21.7%) of mean 3.6 (range 1-10) days' duration. Cranial magnetic resonance imaging (MRI) showed bilateral occipital lesions in all patients, associated in 82.6% with less typical distribution of lesions in frontal, temporal or parietal lobes, cerebellum, corpus callosum, basal ganglia, thalamus, and brain stem. Frontal involvement was predominant, observed in 56.5% of patients. Clinical recovery was followed by radiologic resolution in all patients. Conclusion PRES is often unsuspected by the clinician, thus radiologists may be the first to suggest this diagnosis on an MRI obtained for seizures or encephalopathy. Atypical MRI finding is seen quite often. Rapid diagnosis and treatment are required to avoid a devastating outcome. PMID:29379559

Severity of clinical disease and pathology in ferrets experimentally infected with influenza viruses is influenced by inoculum volume.

PubMed

Moore, Ian N; Lamirande, Elaine W; Paskel, Myeisha; Donahue, Danielle; Kenney, Heather; Qin, Jing; Subbarao, Kanta

2014-12-01

Ferrets are a valuable model for influenza virus pathogenesis, virus transmission, and antiviral therapy studies. However, the contributions of the volume of inoculum administered and the ferret's respiratory tract anatomy to disease outcome have not been explored. We noted variations in clinical disease outcomes and the volume of inoculum administered and investigated these differences by administering two influenza viruses (A/California/07/2009 [H1N1 pandemic] and A/Minnesota/11/2010 [H3N2 variant]) to ferrets intranasally at a dose of 10(6) 50% tissue culture infective doses in a range of inoculum volumes (0.2, 0.5, or 1.0 ml) and followed viral replication, clinical disease, and pathology over 6 days. Clinical illness and respiratory tract pathology were the most severe and most consistent when the viruses were administered in a volume of 1.0 ml. Using a modified micro-computed tomography imaging method and examining gross specimens, we found that the right main-stem bronchus was consistently larger in diameter than the left main-stem bronchus, though the latter was longer and straighter. These anatomic features likely influence the distribution of the inoculum in the lower respiratory tract. A 1.0-ml volume of inoculum is optimal for delivery of virus to the lower respiratory tract of ferrets, particularly when evaluation of clinical disease is desired. Furthermore, we highlight important anatomical features of the ferret lung that influence the kinetics of viral replication, clinical disease severity, and lung pathology. Ferrets are a valuable model for influenza virus pathogenesis, virus transmission, and antiviral therapy studies. Clinical disease in ferrets is an important parameter in evaluating the virulence of novel influenza viruses, and findings are extrapolated to virulence in humans. Therefore, it is highly desirable that the data from different laboratories be accurate and reproducible. We have found that, even when the same virus was administered at similar doses, different investigators reported a range of clinical disease outcomes, from asymptomatic infection to severe weight loss, ocular and nasal discharge, sneezing, and lethargy. We found that a wide range of inoculum volumes was used to experimentally infect ferrets, and we sought to determine whether the variations in disease outcome were the result of the volume of inoculum administered. These data highlight some less explored features of the model, methods of experimental infection, and clinical disease outcomes in a research setting. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Severity of Clinical Disease and Pathology in Ferrets Experimentally Infected with Influenza Viruses Is Influenced by Inoculum Volume

PubMed Central

Moore, Ian N.; Lamirande, Elaine W.; Paskel, Myeisha; Donahue, Danielle; Qin, Jing

2014-01-01

ABSTRACT Ferrets are a valuable model for influenza virus pathogenesis, virus transmission, and antiviral therapy studies. However, the contributions of the volume of inoculum administered and the ferret's respiratory tract anatomy to disease outcome have not been explored. We noted variations in clinical disease outcomes and the volume of inoculum administered and investigated these differences by administering two influenza viruses (A/California/07/2009 [H1N1 pandemic] and A/Minnesota/11/2010 [H3N2 variant]) to ferrets intranasally at a dose of 106 50% tissue culture infective doses in a range of inoculum volumes (0.2, 0.5, or 1.0 ml) and followed viral replication, clinical disease, and pathology over 6 days. Clinical illness and respiratory tract pathology were the most severe and most consistent when the viruses were administered in a volume of 1.0 ml. Using a modified micro-computed tomography imaging method and examining gross specimens, we found that the right main-stem bronchus was consistently larger in diameter than the left main-stem bronchus, though the latter was longer and straighter. These anatomic features likely influence the distribution of the inoculum in the lower respiratory tract. A 1.0-ml volume of inoculum is optimal for delivery of virus to the lower respiratory tract of ferrets, particularly when evaluation of clinical disease is desired. Furthermore, we highlight important anatomical features of the ferret lung that influence the kinetics of viral replication, clinical disease severity, and lung pathology. IMPORTANCE Ferrets are a valuable model for influenza virus pathogenesis, virus transmission, and antiviral therapy studies. Clinical disease in ferrets is an important parameter in evaluating the virulence of novel influenza viruses, and findings are extrapolated to virulence in humans. Therefore, it is highly desirable that the data from different laboratories be accurate and reproducible. We have found that, even when the same virus was administered at similar doses, different investigators reported a range of clinical disease outcomes, from asymptomatic infection to severe weight loss, ocular and nasal discharge, sneezing, and lethargy. We found that a wide range of inoculum volumes was used to experimentally infect ferrets, and we sought to determine whether the variations in disease outcome were the result of the volume of inoculum administered. These data highlight some less explored features of the model, methods of experimental infection, and clinical disease outcomes in a research setting. PMID:25187553

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2.

PubMed

Jack, Clifford R; Barnes, Josephine; Bernstein, Matt A; Borowski, Bret J; Brewer, James; Clegg, Shona; Dale, Anders M; Carmichael, Owen; Ching, Christopher; DeCarli, Charles; Desikan, Rahul S; Fennema-Notestine, Christine; Fjell, Anders M; Fletcher, Evan; Fox, Nick C; Gunter, Jeff; Gutman, Boris A; Holland, Dominic; Hua, Xue; Insel, Philip; Kantarci, Kejal; Killiany, Ron J; Krueger, Gunnar; Leung, Kelvin K; Mackin, Scott; Maillard, Pauline; Malone, Ian B; Mattsson, Niklas; McEvoy, Linda; Modat, Marc; Mueller, Susanne; Nosheny, Rachel; Ourselin, Sebastien; Schuff, Norbert; Senjem, Matthew L; Simonson, Alix; Thompson, Paul M; Rettmann, Dan; Vemuri, Prashanthi; Walhovd, Kristine; Zhao, Yansong; Zuk, Samantha; Weiner, Michael

2015-07-01

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders. We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3. Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials. Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

PubMed

Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

2017-09-15

Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code available at http://insilico.utulsa.edu/software/privateEC . brett-mckinney@utulsa.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Predictive Modeling of Implantation Outcome in an In Vitro Fertilization Setting: An Application of Machine Learning Methods.

PubMed

Uyar, Asli; Bener, Ayse; Ciray, H Nadir

2015-08-01

Multiple embryo transfers in in vitro fertilization (IVF) treatment increase the number of successful pregnancies while elevating the risk of multiple gestations. IVF-associated multiple pregnancies exhibit significant financial, social, and medical implications. Clinicians need to decide the number of embryos to be transferred considering the tradeoff between successful outcomes and multiple pregnancies. To predict implantation outcome of individual embryos in an IVF cycle with the aim of providing decision support on the number of embryos transferred. Retrospective cohort study. Electronic health records of one of the largest IVF clinics in Turkey. The study data set included 2453 embryos transferred at day 2 or day 3 after intracytoplasmic sperm injection (ICSI). Each embryo was represented with 18 clinical features and a class label, +1 or -1, indicating positive and negative implantation outcomes, respectively. For each classifier tested, a model was developed using two-thirds of the data set, and prediction performance was evaluated on the remaining one-third of the samples using receiver operating characteristic (ROC) analysis. The training-testing procedure was repeated 10 times on randomly split (two-thirds to one-third) data. The relative predictive values of clinical input characteristics were assessed using information gain feature weighting and forward feature selection methods. The naïve Bayes model provided 80.4% accuracy, 63.7% sensitivity, and 17.6% false alarm rate in embryo-based implantation prediction. Multiple embryo implantations were predicted at a 63.8% sensitivity level. Predictions using the proposed model resulted in higher accuracy compared with expert judgment alone (on average, 75.7% and 60.1%, respectively). A machine learning-based decision support system would be useful in improving the success rates of IVF treatment. © The Author(s) 2014.

A novel feature-tracking echocardiographic method for the quantitation of regional myocardial function: validation in an animal model of ischemia-reperfusion.

PubMed

Pirat, Bahar; Khoury, Dirar S; Hartley, Craig J; Tiller, Les; Rao, Liyun; Schulz, Daryl G; Nagueh, Sherif F; Zoghbi, William A

2008-02-12

The aim of this study was to validate a novel, angle-independent, feature-tracking method for the echocardiographic quantitation of regional function. A new echocardiographic method, Velocity Vector Imaging (VVI) (syngo Velocity Vector Imaging technology, Siemens Medical Solutions, Ultrasound Division, Mountain View, California), has been introduced, based on feature tracking-incorporating speckle and endocardial border tracking, that allows the quantitation of endocardial strain, strain rate (SR), and velocity. Seven dogs were studied during baseline, and various interventions causing alterations in regional function: dobutamine, 5-min coronary occlusion with reperfusion up to 1 h, followed by dobutamine and esmolol infusions. Echocardiographic images were acquired from short- and long-axis views of the left ventricle. Segment-length sonomicrometry crystals were used as the reference method. Changes in systolic strain in ischemic segments were tracked well with VVI during the different states of regional function. There was a good correlation between circumferential and longitudinal systolic strain by VVI and sonomicrometry (r = 0.88 and r = 0.83, respectively, p < 0.001). Strain measurements in the nonischemic basal segments also demonstrated a significant correlation between the 2 methods (r = 0.65, p < 0.001). Similarly, a significant relation was observed for circumferential and longitudinal SR between the 2 methods (r = 0.94, p < 0.001 and r = 0.90, p < 0.001, respectively). The endocardial velocity relation to changes in strain by sonomicrometry was weaker owing to significant cardiac translation. Velocity Vector Imaging, a new feature-tracking method, can accurately assess regional myocardial function at the endocardial level and is a promising clinical tool for the simultaneous quantification of regional and global myocardial function.

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

PubMed

Ganasala, Padma; Kumar, Vinod

2016-02-01

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning

PubMed Central

Cohen, Kevin Bretonnel; Glass, Benjamin; Greiner, Hansel M.; Holland-Bouley, Katherine; Standridge, Shannon; Arya, Ravindra; Faist, Robert; Morita, Diego; Mangano, Francesco; Connolly, Brian; Glauser, Tracy; Pestian, John

2016-01-01

Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient’s status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral. PMID:27257386

Early-Onset Psychoses: Comparison of Clinical Features and Adult Outcome in 3 Diagnostic Groups

ERIC Educational Resources Information Center

Ledda, Maria Giuseppina; Fratta, Anna Lisa; Pintor, Manuela; Zuddas, Alessandro; Cianchetti, Carlo

2009-01-01

A comparison of clinical features and adult outcome in adolescents with three types of psychotic disorders: schizophrenic (SPh), schizoaffective (SA) and bipolar with psychotic features (BPP). Subjects (n = 41) were finally diagnosed (DSM-IV criteria) with SPh (n = 17), SA (n = 11) or BPP (n = 13). Clinical evaluation took place at onset and at a…

Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach.

PubMed

Liu, Shuo; Zeng, Jinshu; Gong, Huizhou; Yang, Hongqin; Zhai, Jia; Cao, Yi; Liu, Junxiu; Luo, Yuling; Li, Yuhua; Maguire, Liam; Ding, Xuemei

2018-01-01

Breast cancer is the most prevalent cancer in women in most countries of the world. Many computer-aided diagnostic methods have been proposed, but there are few studies on quantitative discovery of probabilistic dependencies among breast cancer data features and identification of the contribution of each feature to breast cancer diagnosis. This study aims to fill this void by utilizing a Bayesian network (BN) modelling approach. A K2 learning algorithm and statistical computation methods are used to construct BN structure and assess the obtained BN model. The data used in this study were collected from a clinical ultrasound dataset derived from a Chinese local hospital and a fine-needle aspiration cytology (FNAC) dataset from UCI machine learning repository. Our study suggested that, in terms of ultrasound data, cell shape is the most significant feature for breast cancer diagnosis, and the resistance index presents a strong probabilistic dependency on blood signals. With respect to FNAC data, bare nuclei are the most important discriminating feature of malignant and benign breast tumours, and uniformity of both cell size and cell shape are tightly interdependent. The BN modelling approach can support clinicians in making diagnostic decisions based on the significant features identified by the model, especially when some other features are missing for specific patients. The approach is also applicable to other healthcare data analytics and data modelling for disease diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Disease features in horses with induced equine monocytic ehrlichiosis (Potomac horse fever).

PubMed

Dutta, S K; Penney, B E; Myrup, A C; Robl, M G; Rice, R M

1988-10-01

Fifty-five horses were inoculated IV and/or SC with materials containing Ehrlichia risticii, ie, infected whole blood, buffy coat cells, or cell culture, to study clinical and hematologic features of equine monocytic ehrlichiosis (Potomac horse fever). Major clinical and hematologic features of induced E risticii infection were biphasic increase in rectal temperature with peak increases of 38.9 C and 39.3 C on postinoculation days (PID) 5 and 12, respectively; depression; anorexia; decreased WBC count (maximal decrease of 47% on PID 12); and diarrhea from PID 14 to PID 18. Increased WBC count was an inconsistent feature, with a maximal increase of 51.5% on PID 20. During times of decreased and increased WBC counts, lymphocyte/neutrophil ratios remained fairly constant. However, not all horses had all clinical and hematologic features, and these features were present in different degrees among horses. Increased rectal temperature, depression, anorexia, and decreased WBC count were more consistent features, whereas diarrhea developed in 73% of the horses. Of 55 horses, 39 (71%) had all clinical and hematologic features of the disease (classic disease), whereas 16 (29%) horses did not have greater than or equal to 1 of these features (nonclassic disease). The E risticii titer in the blood (ehrlichemia) was maximum during the peak increase in rectal temperature. In 55 horses, mortality was 9%. Significant differences (P greater than 0.5) in clinical and hematologic features were not detected between horses that survived and those that died of E risticii infection.

3P: Personalized Pregnancy Prediction in IVF Treatment Process

NASA Astrophysics Data System (ADS)

Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths

NASA Astrophysics Data System (ADS)

Whitney, Heather M.; Drukker, Karen; Edwards, Alexandra; Papaioannou, John; Giger, Maryellen L.

2018-02-01

Radiomics features extracted from breast lesion images have shown potential in diagnosis and prognosis of breast cancer. As clinical institutions transition from 1.5 T to 3.0 T magnetic resonance imaging (MRI), it is helpful to identify robust features across these field strengths. In this study, dynamic contrast-enhanced MR images were acquired retrospectively under IRB/HIPAA compliance, yielding 738 cases: 241 and 124 benign lesions imaged at 1.5 T and 3.0 T and 231 and 142 luminal A cancers imaged at 1.5 T and 3.0 T, respectively. Lesions were segmented using a fuzzy C-means method. Extracted radiomic values for each group of lesions by cancer status and field strength of acquisition were compared using a Kolmogorov-Smirnov test for the null hypothesis that two groups being compared came from the same distribution, with p-values being corrected for multiple comparisons by the Holm-Bonferroni method. Two shape features, one texture feature, and three enhancement variance kinetics features were found to be potentially robust. All potentially robust features had areas under the receiver operating characteristic curve (AUC) statistically greater than 0.5 in the task of distinguishing between lesion types (range of means 0.57-0.78). The significant difference in voxel size between field strength of acquisition limits the ability to affirm more features as robust or not robust according to field strength alone, and inhomogeneities in static field strength and radiofrequency field could also have affected the assessment of kinetic curve features as robust or not. Vendor-specific image scaling could have also been a factor. These findings will contribute to the development of radiomic signatures that use features identified as robust across field strength.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data.

PubMed

Liu, Zhenqiu; Sun, Fengzhu; McGovern, Dermot P

2017-01-01

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L 1 , SCAD and MC+. However, none of the existing algorithms optimizes L 0 , which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L 0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L 0 optimization directly. Even though the original L 0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms ( L 0 ADRIDGE) for L 0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L 0 ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

Application of L1/2 regularization logistic method in heart disease diagnosis.

PubMed

Zhang, Bowen; Chai, Hua; Yang, Ziyi; Liang, Yong; Chu, Gejin; Liu, Xiaoying

2014-01-01

Heart disease has become the number one killer of human health, and its diagnosis depends on many features, such as age, blood pressure, heart rate and other dozens of physiological indicators. Although there are so many risk factors, doctors usually diagnose the disease depending on their intuition and experience, which requires a lot of knowledge and experience for correct determination. To find the hidden medical information in the existing clinical data is a noticeable and powerful approach in the study of heart disease diagnosis. In this paper, sparse logistic regression method is introduced to detect the key risk factors using L(1/2) regularization on the real heart disease data. Experimental results show that the sparse logistic L(1/2) regularization method achieves fewer but informative key features than Lasso, SCAD, MCP and Elastic net regularization approaches. Simultaneously, the proposed method can cut down the computational complexity, save cost and time to undergo medical tests and checkups, reduce the number of attributes needed to be taken from patients.

The Diagnosis and Understanding of Apraxia of Speech: Why Including Neurodegenerative Etiologies May Be Important

ERIC Educational Resources Information Center

Duffy, Joseph R.; Josephs, Keith A.

2012-01-01

Purpose: To discuss apraxia of speech (AOS) as it occurs in neurodegenerative disease (progressive AOS [PAOS]) and how its careful study may contribute to general concepts of AOS and help refine its diagnostic criteria. Method: The article summarizes our current understanding of the clinical features and neuroanatomical and pathologic correlates…

Conducting Cognitive Exercises for Early Dementia with the Use of Apps on iPads

ERIC Educational Resources Information Center

Kong, Anthony Pak-Hin

2015-01-01

A list of iTunes apps was compiled for usage with early stage or mild dementia participants. The method in choosing these apps and determining salient features of the most successful apps was reported. The results will advance the knowledge base on innovative use of smart technology in clinical settings.

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

PubMed Central

Hu, Danqing; Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei; Lu, Xudong; Duan, Huilong

2016-01-01

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. PMID:27649220

Clinical and vascular features of Takayasu arteritis at the time of ischemic stroke.

PubMed

de Paula, Luiz Eduardo; Alverne, Andrea Rocha; Shinjo, Samuel K

2013-01-01

Takayasus arteritis (TA) is a systemic vasculitis whose clinical presentation varies from asymptomatic to serious neurovascular events, including stroke. However, few studies are currently available assessing stroke in TA. Thus, we described the clinical and laboratory characteristics and vascular imaging features in patients with TA at the time of stroke. This is a single center retrospective cohort study investigating the clinical and demographic data of 18 (15.0%) patients with a history of stroke confirmed by imaging methods, among 120 patients with TA, assessed in the 1985-2012 period. The mean age of the 18 patients at the time of stroke was 29.4+/-10.9 years, with 94.4% female and 88.9% Caucasian. Of these patients, 14 (77.8%) had previous stroke at diagnosis of TA, while in four cases the stroke occurred after confirmed TA diagnosis. Regarding the clinical course, 12 (66.7%) had peripheral neurological sequelae and one patient died as a result of cerebral hyperperfusion syndrome after carotid revascularization. Our results showed a high prevalence of stroke in TA and revealed most of these events occurred concomitantly with diagnosed TA. Moreover, although four patients had strokes after diagnosis of TA, these occurred at a young age, demonstrating they were most likely the result of vascular changes secondary to TA.

3D-2D registration in endovascular image-guided surgery: evaluation of state-of-the-art methods on cerebral angiograms.

PubMed

Mitrović, Uroš; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

2018-02-01

Image guidance for minimally invasive surgery is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D-2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. Herein we perform a quantitative and comparative evaluation of ten state-of-the-art methods for 3D-2D registration on a public dataset of clinical angiograms. Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms or arteriovenous malformations. On each of the datasets, highly accurate "gold-standard" registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D-2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration accuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra-interventional 2D images.

Epileptic spasms are a feature of DEPDC5 mTORopathy

PubMed Central

Carvill, Gemma L.; Crompton, Douglas E.; Regan, Brigid M.; McMahon, Jacinta M.; Saykally, Julia; Zemel, Matthew; Schneider, Amy L.; Dibbens, Leanne; Howell, Katherine B.; Mandelstam, Simone; Leventer, Richard J.; Harvey, A. Simon; Mullen, Saul A.; Berkovic, Samuel F.; Sullivan, Joseph; Scheffer, Ingrid E.

2015-01-01

Objective: To assess the presence of DEPDC5 mutations in a cohort of patients with epileptic spasms. Methods: We performed DEPDC5 resequencing in 130 patients with spasms, segregation analysis of variants of interest, and detailed clinical assessment of patients with possibly and likely pathogenic variants. Results: We identified 3 patients with variants in DEPDC5 in the cohort of 130 patients with spasms. We also describe 3 additional patients with DEPDC5 alterations and epileptic spasms: 2 from a previously described family and a third ascertained by clinical testing. Overall, we describe 6 patients from 5 families with spasms and DEPDC5 variants; 2 arose de novo and 3 were familial. Two individuals had focal cortical dysplasia. Clinical outcome was highly variable. Conclusions: While recent molecular findings in epileptic spasms emphasize the contribution of de novo mutations, we highlight the relevance of inherited mutations in the setting of a family history of focal epilepsies. We also illustrate the utility of clinical diagnostic testing and detailed phenotypic evaluation in characterizing the constellation of phenotypes associated with DEPDC5 alterations. We expand this phenotypic spectrum to include epileptic spasms, aligning DEPDC5 epilepsies more with the recognized features of other mTORopathies. PMID:27066554

Comparative phenomenology of ataques de nervios, panic attacks, and panic disorder.

PubMed

Lewis-Fernández, Roberto; Guarnaccia, Peter J; Martínez, Igda E; Salmán, Ester; Schmidt, Andrew; Liebowitz, Michael

2002-06-01

This article examines a clinical sample of 66 Dominican and Puerto Rican subjects who reported ataques de nervios and also psychiatric disorder, and disentangles the phenomenological experiences of ataque de nervios, panic attacks, and panic disorder. In-depth cultural interviews assessed the symptomatic phenomenology of ataque episodes from the local perspective as well as in terms of key panic features, such as recurrence, rapid peaking of symptoms, and lack of provocation. Independent diagnostic assessments of panic attacks and disorder were also used to establish the phenomenological overlap between ataque and panic. Our findings indicate that 36 percent of ataques de nervios fulfill criteria for panic attacks and between 17 percent and 33 percent for panic disorder, depending on the overlap method used. The main features distinguishing ataques that fulfill panic criteria from ataques that do not include whether the episodes were provoked by an upsetting event in the person's life and the rapidity of crescendo of the actual attack. A key finding is that ataques often share individual phenomenological features with panic episodes, but that these features usually do not "run together" during the ataque experience. This confirms previous findings that ataque is a more inclusive construct than panic disorder. The importance of these findings for the clinical diagnosis and treatment of persons with ataques is discussed.

Predictive modeling of structured electronic health records for adverse drug event detection

PubMed Central

2015-01-01

Background The digitization of healthcare data, resulting from the increasingly widespread adoption of electronic health records, has greatly facilitated its analysis by computational methods and thereby enabled large-scale secondary use thereof. This can be exploited to support public health activities such as pharmacovigilance, wherein the safety of drugs is monitored to inform regulatory decisions about sustained use. To that end, electronic health records have emerged as a potentially valuable data source, providing access to longitudinal observations of patient treatment and drug use. A nascent line of research concerns predictive modeling of healthcare data for the automatic detection of adverse drug events, which presents its own set of challenges: it is not yet clear how to represent the heterogeneous data types in a manner conducive to learning high-performing machine learning models. Methods Datasets from an electronic health record database are used for learning predictive models with the purpose of detecting adverse drug events. The use and representation of two data types, as well as their combination, are studied: clinical codes, describing prescribed drugs and assigned diagnoses, and measurements. Feature selection is conducted on the various types of data to reduce dimensionality and sparsity, while allowing for an in-depth feature analysis of the usefulness of each data type and representation. Results Within each data type, combining multiple representations yields better predictive performance compared to using any single representation. The use of clinical codes for adverse drug event detection significantly outperforms the use of measurements; however, there is no significant difference over datasets between using only clinical codes and their combination with measurements. For certain adverse drug events, the combination does, however, outperform using only clinical codes. Feature selection leads to increased predictive performance for both data types, in isolation and combined. Conclusions We have demonstrated how machine learning can be applied to electronic health records for the purpose of detecting adverse drug events and proposed solutions to some of the challenges this presents, including how to represent the various data types. Overall, clinical codes are more useful than measurements and, in specific cases, it is beneficial to combine the two. PMID:26606038

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

NASA Astrophysics Data System (ADS)

Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

2014-03-01

Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease.

PubMed

Memedi, Mevludin; Sadikov, Aleksander; Groznik, Vida; Žabkar, Jure; Možina, Martin; Bergquist, Filip; Johansson, Anders; Haubenberger, Dietrich; Nyholm, Dag

2015-09-17

A challenge for the clinical management of advanced Parkinson's disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Planning: supporting and optimizing clinical guidelines execution.

PubMed

Anselma, Luca; Montani, Stefania

2008-01-01

A crucial feature of computerized clinical guidelines (CGs) lies in the fact that they may be used not only as conventional documents (as if they were just free text) describing general procedures that users have to follow. In fact, thanks to a description of their actions and control flow in some semiformal representation language, CGs can also take advantage of Computer Science methods and Information Technology infrastructures and techniques, to become executable documents, in the sense that they may support clinical decision making and clinical procedures execution. In order to reach this goal, some advanced planning techniques, originally developed within the Artificial Intelligence (AI) community, may be (at least partially) resorted too, after a proper adaptation to the specific CG needs has been carried out.

Integrated Analysis of Pharmacologic, Clinical, and SNP Microarray Data using Projection onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing

PubMed Central

Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun; Campana, Dario; Evans, William E.; Pui, Ching-Hon; Relling, Mary V.

2010-01-01

Powerful methods for integrated analysis of multiple biological data sets are needed to maximize interpretation capacity and acquire meaningful knowledge. We recently developed Projection Onto the Most Interesting Statistical Evidence (PROMISE). PROMISE is a statistical procedure that incorporates prior knowledge about the biological relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical, and genome-wide genotype data that incorporating knowledge about the biological relationships among pharmacologic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE is a powerful statistical tool for identifying genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables. PMID:21516175

[A study of culture-based easy identification system for Malassezia].

PubMed

Kaneko, Takamasa

2011-01-01

Most species of this genus are lipid-dependent yeasts, which colonize the seborrheic part of the skin, and they have been reported to be associated with pityriasis versicolor, Malassezia folliculitis, seborrheic dermatitis, and atopic dermatitis. Malassezia have been re-classified into 7 species based on molecular biological analysis of nuclear ribosomal DNA/RNA and new Malassezia species were reported. As members of the genus Malassezia share similar morphological and biochemical characteristics, it was thought to be difficult to differentiate between them based on phenotypic features. While molecular biological techniques are the most reliable methods for identification of Malassezia, they are not available in most clinical laboratories. We studied ( i ) development of an efficient isolation media and culture based easy identification system, ( ii ) the incidence of atypical biochemical features in Malassezia species and propose a culture-based easy identification system for clinically important Malassezia species, M. globosa, M. restricta, and M. furfur.

One Year of Glaucoma Research in Review: 2013 to 2014

PubMed Central

Van Tassel, Sarah H.; Radcliffe, Nathan M.; Demetriades, Anna M.

2015-01-01

Purpose The purpose of this study was to provide the practicing clinical ophthalmologist with an update of relevant glaucoma literature published from 2013 to 2014. Design Literature review. Methods The authors conducted a 1-year (October 1, 2013, to September 30, 2014) English-language glaucoma literature search on PubMed of articles containing “glaucoma” or “glaucomatous” with title/abstract as a filter. Medical Subject Headings (MeSH) filtered searching was not performed because of the newness of the reviewed material. Results Literature review yielded 2,314 articles, after which we excluded reviews and letters to the editor. We highlighted articles featuring new or updated approaches to the pathophysiology, diagnosis, or treatment of glaucoma and gave preference to human research. Conclusions This review features literature that is of interest to ophthalmologists in practice and also highlights studies that may provide insight to future developments applicable to clinical ophthalmology. PMID:26197218

A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data

PubMed Central

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Milos

2013-01-01

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. PMID:25309815

Pattern of congenital heart diseases in Rwandan children with genetic defects

PubMed Central

Teteli, Raissa; Uwineza, Annette; Butera, Yvan; Hitayezu, Janvier; Murorunkwere, Seraphine; Umurerwa, Lamberte; Ndinkabandi, Janvier; Hellin, Anne-Cécile; Jamar, Mauricette; Caberg, Jean-Hubert; Muganga, Narcisse; Mucumbitsi, Joseph; Rusingiza, Emmanuel Kamanzi; Mutesa, Leon

2014-01-01

Introduction Congenital heart diseases (CHD) are commonly associated with genetic defects. Our study aimed at determining the occurrence and pattern of CHD association with genetic defects among pediatric patients in Rwanda. Methods A total of 125 patients with clinical features suggestive of genetic defects were recruited. Echocardiography and standard karyotype studies were performed in all patients. Results CHDs were detected in the majority of patients with genetic defects. The commonest isolated CHD was ventricular septal defect found in many cases of Down syndrome. In total, chromosomal abnormalities represented the majority of cases in our cohort and were associated with various types of CHDs. Conclusion Our findings showed that CHDs are common in Rwandan pediatric patients with genetic defects. These results suggest that a routine echocardiography assessment combined with systematic genetic investigations including standard karyotype should be mandatory in patients presenting characteristic clinical features in whom CHD is suspected to be associated with genetic defect. PMID:25722758

Cold blast furnace syndrome: a new source of toxic inhalation by nitrogen oxides

PubMed Central

Tague, I; Llewellin, P; Burton, K; Buchan, R; Yates, D

2004-01-01

Methods: Fourteen workers developed acute respiratory symptoms shortly after exposure to "air blast" from blast furnace tuyeres. These included chest tightness, dyspnoea, rigors, and diaphoresis. Chest radiographs showed pulmonary infiltrates, and lung function a restrictive abnormality. This report includes a description of clinical features of the affected workers and elucidation of the probable cause of the outbreak. Results: Clinical features and occupational hygiene measurements suggested the most likely cause was inhalation of nitrogen oxides at high pressure and temperature. While the task could not be eliminated, engineering controls were implemented to control the hazard. No further cases have occurred. Conclusions: "Cold blast furnace syndrome" represents a previously undescribed hazard of blast furnace work, probably due to inhalation of nitrogen oxides. It should be considered in the differential diagnosis of acute toxic inhalational injuries in blast furnace workers. PMID:15090669

An examination of generalized anxiety disorder and dysthymia utilizing the Rorschach inkblot method.

PubMed

Slavin-Mulford, Jenelle; Clements, Alyssa; Hilsenroth, Mark; Charnas, Jocelyn; Zodan, Jennifer

2016-06-30

This study examined transdiagnostic features of generalized anxiety disorder (GAD) and dysthymia in an outpatient clinical sample. Fifteen patients who met DSM-IV criteria for GAD and twenty-one patients who met DSM-IV criteria for dysthymia but who did not have comorbid anxiety disorder were evaluated utilizing the Rorschach. Salient clinical variables were then compared. Results showed that patients with GAD scored significantly higher on variables related to cognitive agitation and a desire/need for external soothing. In addition, there was a trend for patients with GAD to produce higher scores on a measure of ruminative focus on negative aspects of the self. Thus, not surprisingly, GAD patients' experienced more distress than the dysthymic patients. The implications of these findings are discussed with regards to better understanding the shared and distinct features of GAD and dysthymia. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients

NASA Astrophysics Data System (ADS)

Mu, Wei; Qi, Jin; Lu, Hong; Schabath, Matthew; Balagurunathan, Yoganand; Tunali, Ilke; Gillies, Robert James

2018-02-01

Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Structures of the recurrence plot of heart rate variability signal as a tool for predicting the onset of paroxysmal atrial fibrillation.

PubMed

Mohebbi, Maryam; Ghassemian, Hassan; Asl, Babak Mohammadzadeh

2011-05-01

This paper aims to propose an effective paroxysmal atrial fibrillation (PAF) predictor which is based on the analysis of the heart rate variability (HRV) signal. Predicting the onset of PAF, based on non-invasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. This method consists of four steps: Preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In the next step, the recurrence plot (RP) of HRV signal is obtained and six features are extracted to characterize the basic patterns of the RP. These features consist of length of longest diagonal segments, average length of the diagonal lines, entropy, trapping time, length of longest vertical line, and recurrence trend. In the third step, these features are reduced to three features by the linear discriminant analysis (LDA) technique. Using LDA not only reduces the number of the input features, but also increases the classification accuracy by selecting the most discriminating features. Finally, a support vector machine-based classifier is used to classify the HRV signals. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database which consists of both 30-minutes ECG recordings end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, and positive predictivity were 96.55%, 100%, and 100%, respectively.

Fluorescence lifetime technique for surgical imaging, guidance and augmented reality (Conference Presentation)

NASA Astrophysics Data System (ADS)

Marcu, Laura

2017-02-01

The surgeon's limited ability to accurately delineate the tumor margin during surgical interventions is one key challenge in clinical management of cancer. New methods for guiding tumor resection decisions are needed. Numerous studies have shown that tissue autofluorescence properties have the potential to asses biochemical features associates with distinct pathologies in tissue and to distinguish various cancers from normal tissues. However, despite these promising reports, autofluorescence techniques were sparsely adopted in clinical settings. Moreover, when adopted they were primarily used for pre-operative diagnosis rather than guiding interventions. To address this need, we have researched and engineered instrumentation that utilizes label-free fluorescence lifetime contrast to characterize tissue biochemical features in vivo in patients and methodologies conducive to real-time (few seconds) diagnosis of tissue pathologies during surgical procedures. This presentation overviews clinically-compatible multispectral fluorescence lifetime imaging techniques developed in our laboratory and their ability to operate as stand-alone tools, integrated in a biopsy needle and in conjunction with the da Vinci surgical robot. We present pre-clinical and clinical studies in patients that demonstrate the potential of these techniques for intraoperative assessment of brain tumors and head and neck cancer. Current results demonstrate that intrinsic fluorescence signals can provide useful contrast for delineation distinct types of tissues including tumors intraoperatively. Challenges and solutions in the clinical implementation of these techniques are discussed.

Oral lichenoid lesions: distinguishing the benign from the deadly.

PubMed

Müller, Susan

2017-01-01

Oral lichen planus is a chronic inflammatory disease of unknown etiology or pathogenesis with varied disease severity that waxes and wanes over a long period of time. Although a common oral mucosal disease, accurate diagnosis is often challenging due to the overlapping clinical and histopathological features of oral lichen planus and other mucosal diseases. Other immune-mediated mucocutaneous diseases can exhibit lichenoid features including mucous membrane pemphigoid, chronic graft-versus-host disease, and discoid lupus erythematosus. Reactive changes to dental materials or to systemic medications can mimic oral lichen planus both clinically and histologically. In these situations the clinical presentation can be useful, as oral lichen planus presents as a multifocal process and is usually symmetrical and bilateral. Dysplasia of the oral cavity can exhibit a lichenoid histology, which may mask the potentially premalignant features. Proliferative verrucous leukoplakia, an unusual clinical disease, can often mimic oral lichen planus clinically, requiring careful correlation of the clinical and pathologic features.

Cardiovascular Disease and 10-Year Mortality in Postmenopausal Women with Clinical Features of Polycystic Ovary Syndrome.

PubMed

Merz, C Noel Bairey; Shaw, Leslee J; Azziz, Ricardo; Stanczyk, Frank Z; Sopko, George; Braunstein, Glenn D; Kelsey, Sheryl F; Kip, Kevin E; Cooper-DeHoff, Rhonda M; Johnson, B Delia; Vaccarino, Viola; Reis, Steven E; Bittner, Vera; Hodgson, T Keta; Rogers, William; Pepine, Carl J

2016-09-01

Women with polycystic ovary syndrome (PCOS) have greater cardiac risk factor clustering but the link with mortality is incompletely described. To evaluate outcomes in 295 postmenopausal women enrolled in the National Institutes of Health-National Heart, Lung, and Blood Institute (NIH-NHLBI) sponsored Women's Ischemia Syndrome Evaluation (WISE) study according to clinical features of PCOS. A total of 25/295 (8%) women had clinical features of PCOS defined by a premenopausal history of irregular menses and current biochemical evidence of hyperandrogenemia, defined as the top quartile of androstenedione (≥701 pg/mL), testosterone (≥30.9 ng/dL), or free testosterone (≥4.5 pg/mL). Cox proportional hazard model estimated death (n = 80). Women with clinical features of PCOS had an earlier menopause (p = 0.01), were more often smokers (p < 0.04), and trended toward more angiographic coronary artery disease (CAD) (p = 0.07) than women without these features. Cumulative 10-year mortality was 28% for women with (n = 25) versus 27% without clinical features of PCOS (n = 270) (p = 0.85). PCOS was not a significant predictor (p = NS) in prognostic models including diabetes, waist circumference, hypertension, and angiographic CAD. From this longer-term follow up of a relatively small cohort of postmenopausal women with suspected ischemia, the prevalence of PCOS is similar to the general population, and clinical features of PCOS are not associated with CAD or mortality. These findings question whether identification of clinical features of PCOS in postmenopausal women who already have known cardiovascular disease provides any additional opportunity for risk factor intervention.

Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps

PubMed Central

Silfee, Valerie J; Waring, Molly E; Boudreaux, Edwin D; Sadasivam, Rajani S; Mullen, Sean P; Carey, Jennifer L; Hayes, Rashelle B; Ding, Eric Y; Bennett, Gary G; Pagoto, Sherry L

2017-01-01

Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps. PMID:29254914

Transarterial embolization for massive gastrointestinal hemorrhage following abdominal surgery

PubMed Central

Zhou, Chun-Gao; Shi, Hai-Bin; Liu, Sheng; Yang, Zheng-Qiang; Zhao, Lin-Bo; Xia, Jin-Guo; Zhou, Wei-Zhong; Li, Lin-Sun

2013-01-01

AIM: To evaluate the clinical results of angiography and embolization for massive gastrointestinal hemorrhage after abdominal surgery. METHODS: This retrospective study included 26 patients with postoperative hemorrhage after abdominal surgery. All patients underwent emergency transarterial angiography, and 21 patients underwent emergency embolization. We retrospectively analyzed the angiographic features and the clinical outcomes of transcatheter arterial embolization. RESULTS: Angiography showed that a discrete bleeding focus was detected in 21 (81%) of 26 patients. Positive angiographic findings included extravasations of contrast medium (n = 9), pseudoaneurysms (n = 9), and fusiform aneurysms (n = 3). Transarterial embolization was technically successful in 21 (95%) of 22 patients. Clinical success was achieved in 18 (82%) of 22 patients. No postembolization complications were observed. Three patients died of rebleeding. CONCLUSION: The positive rate of angiographic findings in 26 patients with postoperative gastrointestinal hemorrhage was 81%. Transcatheter arterial embolization seems to be an effective and safe method in the management of postoperative gastrointestinal hemorrhage. PMID:24187463