Perfusion MRI in Early Stage of Legg-Calvé-Perthes Disease to Predict Lateral Pillar Involvement: A Preliminary Study.

PubMed

Kim, Harry K W; Wiesman, Kathryn D; Kulkarni, Vedant; Burgess, Jamie; Chen, Elena; Brabham, Case; Ikram, Haseeb; Du, Jerry; Lu, Amanda; Kulkarni, Ashok V; Dempsey, Molly; Herring, J Anthony

2014-07-16

Current radiographic classifications for Legg-Calvé-Perthes disease cannot be applied at the early stages of the disease. The purpose of this study was to quantify the perfusion of the femoral epiphysis in the early stages of Legg-Calvé-Perthes disease with use of perfusion magnetic resonance imaging (MRI) and to determine if the extent of epiphyseal perfusion can predict the lateral pillar involvement at the mid-fragmentation stage. Twenty-nine patients had gadolinium-enhanced perfusion MRI at the initial stage or early fragmentation stage of Legg-Calvé-Perthes disease and were followed prospectively. The percent perfusion of the whole epiphysis and its lateral third was measured by four independent observers using image analysis software. The radiographs obtained at the mid-fragmentation stage were used for the lateral pillar classification. Intraclass correlation coefficient (ICC) and logistic regression analyses were performed. The mean age (and standard deviation) at diagnosis was 7.7 ± 1.7 years (range, 5.3 to 11.3 years). The mean interval between the MRI and the time of maximum fragmentation was 8.2 ± 5.5 months. The interobserver ICC for the percent perfusion of the lateral third of the epiphysis was 0.90 (95% confidence interval [CI]: 0.83 to 0.95). The mean percent perfusion of the lateral third of the epiphysis was 92% ± 2%, 68% ± 18%, and 46% ± 12% for the hips in which the lateral pillar was later classified as A, B, and C, respectively (p = 0.001). When the perfusion level was ≥90% in the lateral third of the epiphysis, the odds ratio of the lateral pillar being later classified as group A, as opposed to B or C, was 72.0 (CI: 3.5 to 1476). With a perfusion level of ≤55% in the lateral third of the epiphysis, the odds ratio of the lateral pillar being later classified as group C, as opposed to A or B, was 33.3 (CI: 2.8 to 392). Similar results were obtained for the whole epiphysis. Perfusion MRI measurements of the total epiphysis and its lateral third obtained at the early stages of Legg-Calvé-Perthes disease were predictive of lateral pillar involvement at the mid-fragmentation stage of the disease. Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence. Copyright © 2014 by The Journal of Bone and Joint Surgery, Incorporated.

Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.

The efficacy of the modified classification system of soft tissue injury in extension injury of the lower cervical spine.

PubMed

Song, Kyung-Jin; Kim, Gyu-Hyung; Lee, Kwang-Bok

2008-07-01

To classify comprehensively the severity of soft tissue injury for extension injuries of the lower cervical spine by magnetic resonance imaging (MRI). To investigate severity of extension injuries using a modified classification system for soft tissue injury by MRI, and to determine the possibility of predicting cord injury by determining the severity of soft tissue injury. It is difficult to diagnose extension injuries by plain radiography and computed tomography. MRI is considered to be the best method of diagnosing soft tissue injuries. The authors examined whether an MRI based diagnostic standard could be devised for extension injuries of the cervical spine. MRI was performed before surgery in 81 patients that had experienced a distractive-extension injury during the past 5 years. Severities of soft tissue injury were subdivided into 5 stages. The retropharyngeal space and the retrotracheal space were measured, and their correlations with the severity of soft tissue injury were examined, as was the relation between canal stenosis and cord injury. Cord injury developed in injuries greater than Grade III (according to our devised system) accompanied by posterior longitudinal ligament rupture (P < 0.01). As the severity of soft tissue injury increased, the cord signal change increased (P < 0.01), the retropharyngeal space and the retrotracheal space increased, and swelling severity in each stage were statistically significant (P < 0.01). In canal stenosis patients, soft tissue damage and cord injury were not found to be associated (P = 0.45). In cases of distractive-extension injury, levels of soft tissue injury were determined accurately by MRI. Moreover, the severity of soft tissue injury was found to be closely associated with the development of cord injury.

Italian experience on use of E.S.W. therapy for avascular necrosis of femoral head.

PubMed

Russo, Sergio; Sadile, Francesco; Esposito, Roberto; Mosillo, Giuseppe; Aitanti, Emanuele; Busco, Gennaro; Wang, Ching-Jen

2015-12-01

Osteonecrosis (avascular necrosis) of the femoral head is a clinical disease due to a severe bone vascular alteration associated with intense pain and loss of joint function, with an incidence of 0.1% and unknown aetiology. Many classifications exist to describe it and in the final stages the patient will need a total hip arthroplasty. In the early stages, ESWT has given excellent responses.
 The Neapolitan school studied more than 600 patients who had very good results in I and II stages of Ficat and Arlet Classification, with an improve of outcomes in VAS and HSS scores. Moreover it has shown a complete restoration of the signal intensity of the femoral head in MRI. Copyright © 2015. Published by Elsevier Ltd.

[Multiparameter magnetic resonance imaging in the diagnosis of cancer of the cervix uteri].

PubMed

Tarachkova, E V; Strel'tsova, O N; Panov, V O; Bazaeva, I Ya; Tyurin, I E

2015-01-01

Cancer of the cervix uteri (CCU) ranks third in the incidence of malignancies in women. The choice of CCU treatment mainly depends on the extent of the tumor process, i.e., the stage of the disease. Determining the stage of CCU is based on the clinical classification of the International Federation of Gynecology and Obstetrics (FIGO) (2009) and has a number of substantial limitations in evaluating parametrial invasion, tumor spread to the pelvic wall, and involvement of regional lymph nodes and in determining the true tumor sizes. Magnetic resonance imaging (MRI) is now the method of choice in staging invasive CCU. Multiparameter MRI will be able to enhance the efficiency of diagnosing microinvasive CCU as well (FIGO 2009), to plan surgical and/or chemoradiation treatment, to evaluate its efficiency, and to diagnose locally recurrent CCU.

T2* measurements of 3-T MRI with ultrashort TEs: capabilities of pulmonary function assessment and clinical stage classification in smokers.

PubMed

Ohno, Yoshiharu; Koyama, Hisanobu; Yoshikawa, Takeshi; Matsumoto, Keiko; Takahashi, Masaya; Van Cauteren, Marc; Sugimura, Kazuro

2011-08-01

The purpose of this study was to determine the usefulness of MRI with ultrashort TEs on a 3-T system and of thin-section MDCT for pulmonary function assessment and clinical stage classification of chronic obstructive pulmonary disease (COPD) in smokers. Forty smokers (24 men and 16 women; mean age ± SD, 68.0 ± 9.3 years) underwent MRI with ultrashort TEs and thin-section MDCT. Pulmonary function testing was also performed to determine the following: the ratio of forced expiratory volume in 1 second to forced vital capacity (percentage predicted) (FEV(1/)FVC%), percentage predicted forced expiratory volume in 1 second (%FEV(1)), and percentage predicted diffusing capacity of lung for carbon monoxide corrected for alveolar volume (%DLCO/V(A)). All subjects were classified into one of four groups as follows: smokers without COPD, with mild COPD, with moderate COPD, and with severe or very severe COPD. T2(*) maps were expressed using proprietary software. Regional T2(*) values were determined by region of interest measurements and were averaged to determine a mean T2(*) value for each subject. CT-based functional lung volume and the ratio of the wall area to the total airway area were also determined. All indexes were statistically correlated with pulmonary function parameters. Then, all indexes were compared among all groups by means of Tukey's honest significance test. All indexes had significant correlation with FEV(1)/FVC%, %FEV(1), and % DLCO/V(A) (p < 0.05). All indexes except WA% of smokers without COPD and smokers with mild COPD differed significantly from those of smokers with moderate COPD and smokers with severe or very severe COPD (p < 0.05). Moreover, the mean T2(*) value of the moderate COPD group was significantly different from that of the severe or very severe COPD group (p < 0.05). MRI with ultrashort TEs is potentially as useful as quantitatively assessed MDCT for pulmonary function loss assessment and clinical stage classification of COPD in smokers.

Suggestions for Lymph Node Classification of UICC/AJCC Staging System: A Retrospective Study Based on 1197 Nasopharyngeal Carcinoma Patients Treated With Intensity-Modulated Radiation Therapy

PubMed Central

Guo, Qiaojuan; Pan, Jianji; Zong, Jingfeng; Zheng, Wei; Zhang, Chun; Tang, Linbo; Chen, Bijuan; Cui, Xiaofei; Xiao, Youping; Chen, Yunbin; Lin, Shaojun

2015-01-01

Abstract This article provides suggestions for N classification of Union for International Cancer Control/American Joint Committee on Cancer (UICC/AJCC) staging system of nasopharyngeal carcinoma (NPC), purely based on magnetic resonance imaging (MRI) in intensity-modulated radiation therapy (IMRT) era. A total of 1197 nonmetastatic NPC patients treated with IMRT were enrolled, and all were scanned by MRI at nasopharynx and neck before treatment. MRI-based nodal variables including level, laterality, maximal axial diameter (MAD), extracapsular spread (ECS), and necrosis were analyzed as potential prognostic factors. Modifications of N classification were then proposed and verified. Only nodal level and laterality were considered to be significant variables affecting the treatment outcome. N classification was thus proposed accordingly: N0, no regional lymph node (LN) metastasis; N1, retropharyngeal LNs involvement (regardless of laterality), and/or unilateral levels I, II, III, and/or Va involvement; N2, bilateral levels I, II, III, and/or Va involvement; and N3, levels IV, Vb, and Vc involvement. This proposal showed significant predicting value in multivariate analysis. N3 patients indicated relatively inferior overall survival (OS) and distant metastasis-free survival (DMFS) than N2 patients; however, the difference showed no statistical significance (P = 0.673 and 0.265 for OS and DMFS, respectively), and this was considered to be correlated with the small sample sizes of N3 patients (79 patients, 6.6%). Nodal level and laterality, but not MAD, ECS, and necrosis, were considered to be significant predicting factors for NPC. The proposed N classification was proved to be powerfully predictive in our cohort; however, treatment outcome of the proposed N2 and N3 patients could not differ significantly from each other. This insignificance may be because of the small sample sizes of N3 patients. Our results are based on a single-center data, to develop a new N classification that is universally acceptable; further verification by data from multicenter is warranted. PMID:25997052

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

Single Subject Classification of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia Using Anatomical, Diffusion Tensor, and Resting-State Functional Magnetic Resonance Imaging.

PubMed

Bouts, Mark J R J; Möller, Christiane; Hafkemeijer, Anne; van Swieten, John C; Dopper, Elise; van der Flier, Wiesje M; Vrenken, Hugo; Wink, Alle Meije; Pijnenburg, Yolande A L; Scheltens, Philip; Barkhof, Frederik; Schouten, Tijn M; de Vos, Frank; Feis, Rogier A; van der Grond, Jeroen; de Rooij, Mark; Rombouts, Serge A R B

2018-01-01

Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known. Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC). Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41). Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI.

Dental age estimation in living individuals using 3.0 T MRI of lower third molars.

PubMed

Guo, Yucheng; Olze, Andreas; Ottow, Christian; Schmidt, Sven; Schulz, Ronald; Heindel, Walter; Pfeiffer, Heidi; Vieth, Volker; Schmeling, Andreas

2015-11-01

In order to increase the validity of age estimation in adolescents and young adults when there is no legitimation for X-ray examinations, it seems desirable to be able to assess the mineralization of third molars using X-ray-free imaging procedures. In the present study, the mineralization stages of lower third molars were determined prospectively in 269 male and 248 female individuals aged 12 to 24 years using 3.0 T MRI. The classification system of Demirjian et al. was used to determine the stages. This study presents the minima and maxima, means and standard deviations, median values, and lower and upper quartiles separately for both sexes, for the mineralization stages B-H. Statistically significant sex differences were observed for the mineralization stages C, E, F, and G, and a faster developmental rate was observed for males. It was concluded that magnetic resonance imaging is an X-ray-free alternative to orthopantomography when assessing mineralization of third molars.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Common component classification: what can we learn from machine learning?

PubMed

Anderson, Ariana; Labus, Jennifer S; Vianna, Eduardo P; Mayer, Emeran A; Cohen, Mark S

2011-05-15

Machine learning methods have been applied to classifying fMRI scans by studying locations in the brain that exhibit temporal intensity variation between groups, frequently reporting classification accuracy of 90% or better. Although empirical results are quite favorable, one might doubt the ability of classification methods to withstand changes in task ordering and the reproducibility of activation patterns over runs, and question how much of the classification machines' power is due to artifactual noise versus genuine neurological signal. To examine the true strength and power of machine learning classifiers we create and then deconstruct a classifier to examine its sensitivity to physiological noise, task reordering, and across-scan classification ability. The models are trained and tested both within and across runs to assess stability and reproducibility across conditions. We demonstrate the use of independent components analysis for both feature extraction and artifact removal and show that removal of such artifacts can reduce predictive accuracy even when data has been cleaned in the preprocessing stages. We demonstrate how mistakes in the feature selection process can cause the cross-validation error seen in publication to be a biased estimate of the testing error seen in practice and measure this bias by purposefully making flawed models. We discuss other ways to introduce bias and the statistical assumptions lying behind the data and model themselves. Finally we discuss the complications in drawing inference from the smaller sample sizes typically seen in fMRI studies, the effects of small or unbalanced samples on the Type 1 and Type 2 error rates, and how publication bias can give a false confidence of the power of such methods. Collectively this work identifies challenges specific to fMRI classification and methods affecting the stability of models. Copyright © 2010 Elsevier Inc. All rights reserved.

Secretin-stimulated MRI characterization of pancreatic morphology and function in patients with chronic pancreatitis.

PubMed

Madzak, Adnan; Olesen, Søren Schou; Haldorsen, Ingfrid Salvesen; Drewes, Asbjørn Mohr; Frøkjær, Jens Brøndum

Chronic pancreatitis (CP) is characterized by abnormal pancreatic morphology and impaired endocrine and exocrine function. However, little is known about the relationship between pancreatic morphology and function, and also the association with the etiology and clinical manifestations of CP. The aim was to explore pancreatic morphology and function with advanced MRI in patients with CP and healthy controls (HC) METHODS: Eighty-two patients with CP and 22 HC were enrolled in the study. Morphological imaging parameters included pancreatic main duct diameter, gland volume, fat signal fraction and apparent diffusion coefficient (ADC) values. Functional secretin-stimulated MRI (s-MRI) parameters included pancreatic secretion (bowel fluid volume) and changes in pancreatic ADC value before and after secretin stimulation. Patients were classified according to the modified Cambridge and M-ANNHEIM classification system and fecal elastase was collected. All imaging parameters differentiated CP patients from HC; however, correlations between morphological and functional parameters in CP were weak. Patients with alcoholic and non-alcoholic etiology had comparable s-MRI findings. Fecal elastase was positively correlated to pancreatic gland volume (r = 0.68, P = 0.0016) and negatively correlated to Cambridge classification (r = -0.35, P < 0.001). Additionally, gland volume was negatively correlated to the duration of CP (r = -0.39, P < 0.001) and baseline ADC (r = -0.35, P = 0.027). When stratified by clinical stage (M-ANNHEIM), the pancreatic gland volume was significantly decreased in the severe stages of CP (P = 0.001). S-MRI provides detailed information about pancreatic morphology and function and represents a promising non-invasive imaging method to characterize pancreatic pathophysiology and may enable monitoring of disease progression in patients with CP. Copyright © 2017 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI.

PubMed

Elliott, Colm; Arnold, Douglas L; Collins, D Louis; Arbel, Tal

2013-08-01

Detection of new Multiple Sclerosis (MS) lesions on magnetic resonance imaging (MRI) is important as a marker of disease activity and as a potential surrogate for relapses. We propose an approach where sequential scans are jointly segmented, to provide a temporally consistent tissue segmentation while remaining sensitive to newly appearing lesions. The method uses a two-stage classification process: 1) a Bayesian classifier provides a probabilistic brain tissue classification at each voxel of reference and follow-up scans, and 2) a random-forest based lesion-level classification provides a final identification of new lesions. Generative models are learned based on 364 scans from 95 subjects from a multi-center clinical trial. The method is evaluated on sequential brain MRI of 160 subjects from a separate multi-center clinical trial, and is compared to 1) semi-automatically generated ground truth segmentations and 2) fully manual identification of new lesions generated independently by nine expert raters on a subset of 60 subjects. For new lesions greater than 0.15 cc in size, the classifier has near perfect performance (99% sensitivity, 2% false detection rate), as compared to ground truth. The proposed method was also shown to exceed the performance of any one of the nine expert manual identifications.

Brain tumour classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding.

PubMed

Renjith, Arokia; Manjula, P; Mohan Kumar, P

2015-01-01

Brain tumour is one of the main causes for an increase in transience among children and adults. This paper proposes an improved method based on Magnetic Resonance Imaging (MRI) brain image classification and image segmentation approach. Automated classification is encouraged by the need of high accuracy when dealing with a human life. The detection of the brain tumour is a challenging problem, due to high diversity in tumour appearance and ambiguous tumour boundaries. MRI images are chosen for detection of brain tumours, as they are used in soft tissue determinations. First of all, image pre-processing is used to enhance the image quality. Second, dual-tree complex wavelet transform multi-scale decomposition is used to analyse texture of an image. Feature extraction extracts features from an image using gray-level co-occurrence matrix (GLCM). Then, the Neuro-Fuzzy technique is used to classify the stages of brain tumour as benign, malignant or normal based on texture features. Finally, tumour location is detected using Otsu thresholding. The classifier performance is evaluated based on classification accuracies. The simulated results show that the proposed classifier provides better accuracy than previous method.

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).

Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach.

PubMed

Ahmad, Rana Fayyaz; Malik, Aamir Saeed; Kamel, Nidal; Reza, Faruque; Amin, Hafeez Ullah; Hussain, Muhammad

2017-01-01

Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful. In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes. Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature. The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Bone marrow oedema on MR imaging indicates ARCO stage 3 disease in patients with AVN of the femoral head.

PubMed

Meier, Reinhard; Kraus, Tobias M; Schaeffeler, Christoph; Torka, Sebastian; Schlitter, Anna Melissa; Specht, Katja; Haller, Bernhard; Waldt, Simone; Rechl, Hans; Rummeny, Ernst J; Woertler, Klaus

2014-09-01

To test the hypothesis that bone marrow oedema (BME) observed on MRI in patients with avascular necrosis (AVN) of the femoral head represents an indicator of subchondral fracture. Thirty-seven symptomatic hips of 27 consecutive patients (53% women, mean age 49.2) with AVN of the femoral head and associated BME on magnetic resonance (MR) imaging were included. MR findings were correlated with computed tomography (CT) of the hip and confirmed by histopathological examination of the resected femoral head. Imaging studies were analysed by two radiologists with use of the ARCO classification. On MR imaging a fracture line could be identified in 19/37 (51%) cases, which were classified as ARCO stage 3 (n = 15) and stage 4 (n = 4). The remaining 18/37 (49%) cases were classified as ARCO stage 2. However, in all 37/37 (100%) cases a subchondral fracture was identified on CT, indicating ARCO stage 3/4 disease. The extent of subchondral fractures and the femoral head collapse was graded higher on CT as compared to MRI (P < 0.05). Histopathological analysis confirmed bone necrosis and subchondral fractures. In patients with AVN, BME of the femoral head represents a secondary sign of subchondral fracture and thus indicates ARCO stage 3 disease. BME on MRI in AVN of femoral head indicates a subchondral fracture. BME in AVN of the femoral head represents ARCO stage 3/4 disease. CT identifies subchondral fractures and femoral head collapse better than MR imaging. This knowledge helps to avoid understaging and to trigger adequate treatment.

Noninvasive Classification of Hepatic Fibrosis Based on Texture Parameters From Double Contrast-Enhanced Magnetic Resonance Images

PubMed Central

Bahl, Gautam; Cruite, Irene; Wolfson, Tanya; Gamst, Anthony C.; Collins, Julie M.; Chavez, Alyssa D.; Barakat, Fatma; Hassanein, Tarek; Sirlin, Claude B.

2016-01-01

Purpose To demonstrate a proof of concept that quantitative texture feature analysis of double contrast-enhanced magnetic resonance imaging (MRI) can classify fibrosis noninvasively, using histology as a reference standard. Materials and Methods A Health Insurance Portability and Accountability Act (HIPAA)-compliant Institutional Review Board (IRB)-approved retrospective study of 68 patients with diffuse liver disease was performed at a tertiary liver center. All patients underwent double contrast-enhanced MRI, with histopathology-based staging of fibrosis obtained within 12 months of imaging. The MaZda software program was used to compute 279 texture parameters for each image. A statistical regularization technique, generalized linear model (GLM)-path, was used to develop a model based on texture features for dichotomous classification of fibrosis category (F ≤2 vs. F ≥3) of the 68 patients, with histology as the reference standard. The model's performance was assessed and cross-validated. There was no additional validation performed on an independent cohort. Results Cross-validated sensitivity, specificity, and total accuracy of the texture feature model in classifying fibrosis were 91.9%, 83.9%, and 88.2%, respectively. Conclusion This study shows proof of concept that accurate, noninvasive classification of liver fibrosis is possible by applying quantitative texture analysis to double contrast-enhanced MRI. Further studies are needed in independent cohorts of subjects. PMID:22851409

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

PubMed

Schouten, Tijn M; Koini, Marisa; de Vos, Frank; Seiler, Stephan; van der Grond, Jeroen; Lechner, Anita; Hafkemeijer, Anne; Möller, Christiane; Schmidt, Reinhold; de Rooij, Mark; Rombouts, Serge A R B

2016-01-01

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning.

PubMed

Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda

2017-08-20

Alzheimer's disease (AD) is currently ranked as the sixth leading cause of death in the United States and recent estimates indicate that the disorder may rank third, just behind heart disease and cancer, as a cause of death for older people. Clearly, predicting this disease in the early stages and preventing it from progressing is of great importance. The diagnosis of Alzheimer's disease (AD) requires a variety of medical tests, which leads to huge amounts of multivariate heterogeneous data. It can be difficult and exhausting to manually compare, visualize, and analyze this data due to the heterogeneous nature of medical tests; therefore, an efficient approach for accurate prediction of the condition of the brain through the classification of magnetic resonance imaging (MRI) images is greatly beneficial and yet very challenging. In this paper, a novel approach is proposed for the diagnosis of very early stages of AD through an efficient classification of brain MRI images, which uses label propagation in a manifold-based semi-supervised learning framework. We first apply voxel morphometry analysis to extract some of the most critical AD-related features of brain images from the original MRI volumes and also gray matter (GM) segmentation volumes. The features must capture the most discriminative properties that vary between a healthy and Alzheimer-affected brain. Next, we perform a principal component analysis (PCA)-based dimension reduction on the extracted features for faster yet sufficiently accurate analysis. To make the best use of the captured features, we present a hybrid manifold learning framework which embeds the feature vectors in a subspace. Next, using a small set of labeled training data, we apply a label propagation method in the created manifold space to predict the labels of the remaining images and classify them in the two groups of mild Alzheimer's and normal condition (MCI/NC). The accuracy of the classification using the proposed method is 93.86% for the Open Access Series of Imaging Studies (OASIS) database of MRI brain images, providing, compared to the best existing methods, a 3% lower error rate.

Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group.

PubMed

Lambert, Robert G W; Bakker, Pauline A C; van der Heijde, Désirée; Weber, Ulrich; Rudwaleit, Martin; Hermann, K G; Sieper, Joachim; Baraliakos, Xenofon; Bennett, Alex; Braun, Jürgen; Burgos-Vargas, Rubén; Dougados, Maxime; Pedersen, Susanne Juhl; Jurik, Anne Grethe; Maksymowych, Walter P; Marzo-Ortega, Helena; Østergaard, Mikkel; Poddubnyy, Denis; Reijnierse, Monique; van den Bosch, Filip; van der Horst-Bruinsma, Irene; Landewé, Robert

2016-11-01

To review and update the existing definition of a positive MRI for classification of axial spondyloarthritis (SpA). The Assessment in SpondyloArthritis International Society (ASAS) MRI working group conducted a consensus exercise to review the definition of a positive MRI for inclusion in the ASAS classification criteria of axial SpA. Existing definitions and new data relevant to the MRI diagnosis and classification of sacroiliitis and spondylitis in axial SpA, published since the ASAS definition first appeared in print in 2009, were reviewed and discussed. The precise wording of the existing definition was examined in detail and the data and a draft proposal were presented to and voted on by the ASAS membership. The clear presence of bone marrow oedema on MRI in subchondral bone is still considered to be the defining observation that determines the presence of active sacroiliitis. Structural damage lesions seen on MRI may contribute to a decision by the observer that inflammatory lesions are genuinely due to SpA but are not required to meet the definition. The existing definition was clarified adding guidelines and images to assist in the application of the definition. The definition of a positive MRI for classification of axial SpA should continue to primarily depend on the imaging features of 'active sacroiliitis' until more data are available regarding MRI features of structural damage in the sacroiliac joint and MRI features in the spine and their utility when used for classification purposes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Usefulness of MR imaging of the parotid glands in patients with secondary Sjögren's syndrome associated with rheumatoid arthritis.

PubMed

Yokosawa, Masahiro; Tsuboi, Hiroto; Nasu, Katsuhiro; Hagiya, Chihiro; Hagiwara, Shinya; Hirota, Tomoya; Ebe, Hiroshi; Takahashi, Hiroyuki; Asashima, Hiromitsu; Kondo, Yuya; Ogishima, Hiroshi; Suzuki, Takeshi; Minami, Manabu; Bukawa, Hiroki; Matsumoto, Isao; Sumida, Takayuki

2015-05-01

To assess the correlation between MR imaging (MRI) of parotid glands with X-ray sialography, histopathology of the labial salivary glands, and salivary secretion, in patients with secondary Sjögren's syndrome (SS) associated with rheumatoid arthritis (RA). Non-contrast MRI of the parotid glands was performed in 13 secondary SS patients associated with RA who satisfied the revised Japanese diagnostic criteria for SS (1999), and the ACR/EULAR classification criteria for RA (2010). The MRI findings were classified according to the degree of high-intensity signal on T1-weighted images (T1WI) and short inversion time inversion recovery (STIR) images into five grades (0-4), using the modified Nagasaki University grading method. The results of MRI grading were compared with the Rubin and Holt staging of X-ray sialography (0-4), the Greenspan grading of labial salivary gland histopathology (0-4), and salivary secretion by the gum test (ml/10 min). All 13 patients were females, with a mean age of 50.2 ± 11.3 years. According to the MRI grading, 3 patients were Grade 1, 5 were Grade 2, 5 were Grade 3, and none was Grade 0 or Grade 4. The mean stage by X-ray sialography was 1.7 ± 1.0, the mean grade by histopathology was 2.4 ± 1.2, and the mean volume of salivary secretion was 9.7 ± 3.9 ml. The MRI grading correlated significantly with the Rubin and Holt staging and Greenspan grading (P < 0.01 each, Spearman's rank correlation), and significantly and inversely with the results of the gum test (P < 0.05). The results suggest that MRI of the parotid glands is a useful noninvasive tool for evaluating destruction and inflammation in the salivary glands.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE

PubMed Central

Chen, Ting; Rangarajan, Anand; Vemuri, Baba C.

2010-01-01

This paper presents a novel classification via aggregated regression algorithm – dubbed CAVIAR – and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature – the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain – and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS. PMID:21151847

CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.

PubMed

Chen, Ting; Rangarajan, Anand; Vemuri, Baba C

2010-04-14

This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature - the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain - and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS.

Can glenoid wear be accurately assessed using x-ray imaging? Evaluating agreement of x-ray and magnetic resonance imaging (MRI) Walch classification.

PubMed

Kopka, Michaela; Fourman, Mitchell; Soni, Ashish; Cordle, Andrew C; Lin, Albert

2017-09-01

The Walch classification is the most recognized means of assessing glenoid wear in preoperative planning for shoulder arthroplasty. This classification relies on advanced imaging, which is more expensive and less practical than plain radiographs. The purpose of this study was to determine whether the Walch classification could be accurately applied to x-ray images compared with magnetic resonance imaging (MRI) as the gold standard. We hypothesized that x-ray images cannot adequately replace advanced imaging in the evaluation of glenoid wear. Preoperative axillary x-ray images and MRI scans of 50 patients assessed for shoulder arthroplasty were independently reviewed by 5 raters. Glenoid wear was individually classified according to the Walch classification using each imaging modality. The raters then collectively reviewed the MRI scans and assigned a consensus classification to serve as the gold standard. The κ coefficient was used to determine interobserver agreement for x-ray images and independent MRI reads, as well as the agreement between x-ray images and consensus MRI. The inter-rater agreement for x-ray images and MRIs was "moderate" (κ = 0.42 and κ = 0.47, respectively) for the 5-category Walch classification (A1, A2, B1, B2, C) and "moderate" (κ = 0.54 and κ = 0.59, respectively) for the 3-category Walch classification (A, B, C). The agreement between x-ray images and consensus MRI was much lower: "fair-to-moderate" (κ = 0.21-0.51) for the 5-category and "moderate" (κ = 0.36-0.60) for the 3-category Walch classification. The inter-rater agreement between x-ray images and consensus MRI is "fair-to-moderate." This is lower than the previously reported reliability of the Walch classification using computed tomography scans. Accordingly, x-ray images are inferior to advanced imaging when assessing glenoid wear. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Treatment of osteosarcoma around the knee in skeletally immature patients

PubMed Central

Yao, Weitao; Cai, Qiqing; Wang, Jiaqiang; Gao, Songtao

2017-01-01

Limb sparing surgery in growing young patients with malignant tumors is difficult as invasion of the physis by the tumor or surgical resection through the metaphysis may cause significant limb discrepancy following surgery. At present, hinged tumor prosthesis or biological reconstructions are the main methods following tumor resection in these patients. The aim of the present study was to assess different procedures for the treatment of osteosarcoma around knee joints in immature patients. A retrospective study of 56 patients (<15 years old, open physis) who had been treated for osteosarcoma around the knee joint between January 2007 and December 2015 was performed. Clinical data collected included patient demographics (age at diagnosis, sex and date of diagnosis), tumor characteristics [location, Enneking stage and subtype on magnetic resonance imaging (MRI)], treatment (response to neoadjuvant chemotherapy and type of primary surgery) and clinical outcomes (limb function, discrepancy and overall survival). The median age at the time of diagnosis was 12.14 years (range, 3–15 years). There were 32 male patients (57.1%). A total of 41 (82%) tumors were located at the distal femur, and 15 (18%) at the proximal tibia. A total of 49 (87.5%) patients were diagnosed with stage IIB tumors, and 7 (12.5%) had stage III, according to the Enneking stage classification. Different surgical methods, including amputation, rotation-plasty, endoprosthesis and biological instructions (e.g., allograft) were performed according to MRI type classification. During follow-up, 21 patients (37.5%) succumbed to disease. The Musculoskeletal Tumor Society score ranged from excellent to fair functional result. Recurrence (2 cases, 16.67%) and infection (2, cases, 16.67%) were the main complications following endoprosthesis replacement, while delayed union (12 cases, 57.14%) and fracture (3 cases, 14.29%) were the main causes for biological reconstructions. Limb-length discrepancy ranged from 0–10 cm in limb-saving surgery. The overall survival rate was 57.66% with different cohorts in Enneking stages IIB and III, with or without involvement of the physis and different cycles of chemotherapy. Results of the present study indicated that different limb saving surgeries, including epiphysis/physis preservation with biological construction in patients with MRI types I to III and endoprosthetic/osteoarticular reconstruction in patients with MRI types IV and V, are useful in the management of osteosarcoma in growing young patients with proper surgery indications, and knee joint function was maintained with acceptable complications including limb discrepancy, delayed union, infection, recurrence and fracture. PMID:29113159

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

PubMed

Jin, Mingwu; Deng, Weishu

2018-05-15

There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study. Ensemble tree classifier with boosting is more powerful than SVM to predict the subject group. However, more advanced feature selection and classification methods or additional measures besides structural MRI may be needed to improve the prediction performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

PubMed

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of human coronary atherosclerotic plaques using ex vivo high-resolution multicontrast-weighted MRI compared with histopathology.

PubMed

Li, Tao; Li, Xin; Zhao, Xihai; Zhou, Weihua; Cai, Zulong; Yang, Li; Guo, Aitao; Zhao, Shaohong

2012-05-01

The objective of our study was to evaluate the feasibility of ex vivo high-resolution multicontrast-weighted MRI to accurately classify human coronary atherosclerotic plaques according to the American Heart Association classification. Thirteen human cadaver heart specimens were imaged using high-resolution multicontrast-weighted MR technique (T1-weighted, proton density-weighted, and T2-weighted). All MR images were matched with histopathologic sections according to the landmark of the bifurcation of the left main coronary artery. The sensitivity and specificity of MRI for the classification of plaques were determined, and Cohen's kappa analysis was applied to evaluate the agreement between MRI and histopathology in the classification of atherosclerotic plaques. One hundred eleven MR cross-sectional images obtained perpendicular to the long axis of the proximal left anterior descending artery were successfully matched with the histopathologic sections. For the classification of plaques, the sensitivity and specificity of MRI were as follows: type I-II (near normal), 60% and 100%; type III (focal lipid pool), 80% and 100%; type IV-V (lipid, necrosis, fibrosis), 96.2% and 88.2%; type VI (hemorrhage), 100% and 99.0%; type VII (calcification), 93% and 100%; and type VIII (fibrosis without lipid core), 100% and 99.1%, respectively. Isointensity, which indicates lipid composition on histopathology, was detected on MRI in 48.8% of calcified plaques. Agreement between MRI and histopathology for plaque classification was 0.86 (p < 0.001). Ex vivo high-resolution multicontrast-weighted MRI can accurately classify advanced atherosclerotic plaques in human coronary arteries.

A Robust Deep Model for Improved Classification of AD/MCI Patients

PubMed Central

Li, Feng; Tran, Loc; Thung, Kim-Han; Ji, Shuiwang; Shen, Dinggang; Li, Jiang

2015-01-01

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. PMID:25955998

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness.

PubMed

Hectors, Stefanie J; Besa, Cecilia; Wagner, Mathilde; Jajamovich, Guido H; Haines, George K; Lewis, Sara; Tewari, Ashutosh; Rastinehad, Ardeshir; Huang, Wei; Taouli, Bachir

2017-09-01

To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (K trans , v e , k ep [TM&SSM] and intracellular water molecule lifetime τ i [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: K trans +k ep 0.76, SSM: τ i +k ep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: K trans +τ i SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849. © 2017 International Society for Magnetic Resonance in Medicine.

Quantification of neonatal lung parenchymal density via ultrashort echo time MRI with comparison to CT.

PubMed

Higano, Nara S; Fleck, Robert J; Spielberg, David R; Walkup, Laura L; Hahn, Andrew D; Thomen, Robert P; Merhar, Stephanie L; Kingma, Paul S; Tkach, Jean A; Fain, Sean B; Woods, Jason C

2017-10-01

To demonstrate that ultrashort echo time (UTE) magnetic resonance imaging (MRI) can achieve computed tomography (CT)-like quantification of lung parenchyma in free-breathing, non-sedated neonates. Because infant CTs are used sparingly, parenchymal disease evaluation via UTE MRI has potential for translational impact. Two neonatal control cohorts without suspected pulmonary morbidities underwent either a research UTE MRI (n = 5; 1.5T) or a clinically-ordered CT (n = 9). Whole-lung means and anterior-posterior gradients of UTE-measured image intensity (arbitrary units, au, normalized to muscle) and CT-measured density (g/cm 3 ) were compared (Mann-Whitney U-test). Separately, a diseased neonatal cohort (n = 5) with various pulmonary morbidities underwent both UTE MRI and CT. UTE intensity and CT density were compared with Spearman correlations within ∼33 anatomically matched regions of interest (ROIs) in each diseased subject, spanning low- to high-density tissues. Radiological classifications were evaluated in all ROIs, with mean UTE intensities and CT densities compared in each classification. In control subjects, whole-lung UTE intensities (0.51 ± 0.04 au) were similar to CT densities (0.44 ± 0.09 g/cm 3 ) (P = 0.062), as were UTE (0.021 ± 0.020 au/cm) and CT (0.034 ± 0.024 [g/cm 3 ]/cm) anterior-posterior gradients (P = 0.351). In diseased subjects' ROIs, significant correlations were observed between UTE and CT (P ≤0.007 in each case). Relative differences between UTE and CT were small in all classifications (4-25%). These results demonstrate a strong association between UTE image intensity and CT density, both between whole-lung tissue in control patients and regional radiological pathologies in diseased patients. This indicates the potential for UTE MRI to longitudinally evaluate neonatal pulmonary disease and to provide visualization of pathologies similar to CT, without sedation/anesthesia or ionizing radiation. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:992-1000. © 2017 International Society for Magnetic Resonance in Medicine.

Pairwise Classifier Ensemble with Adaptive Sub-Classifiers for fMRI Pattern Analysis.

PubMed

Kim, Eunwoo; Park, HyunWook

2017-02-01

The multi-voxel pattern analysis technique is applied to fMRI data for classification of high-level brain functions using pattern information distributed over multiple voxels. In this paper, we propose a classifier ensemble for multiclass classification in fMRI analysis, exploiting the fact that specific neighboring voxels can contain spatial pattern information. The proposed method converts the multiclass classification to a pairwise classifier ensemble, and each pairwise classifier consists of multiple sub-classifiers using an adaptive feature set for each class-pair. Simulated and real fMRI data were used to verify the proposed method. Intra- and inter-subject analyses were performed to compare the proposed method with several well-known classifiers, including single and ensemble classifiers. The comparison results showed that the proposed method can be generally applied to multiclass classification in both simulations and real fMRI analyses.

Comparing the MRI-based Goutallier Classification to an experimental quantitative MR spectroscopic fat measurement of the supraspinatus muscle.

PubMed

Gilbert, Fabian; Böhm, Dirk; Eden, Lars; Schmalzl, Jonas; Meffert, Rainer H; Köstler, Herbert; Weng, Andreas M; Ziegler, Dirk

2016-08-22

The Goutallier Classification is a semi quantitative classification system to determine the amount of fatty degeneration in rotator cuff muscles. Although initially proposed for axial computer tomography scans it is currently applied to magnet-resonance-imaging-scans. The role for its clinical use is controversial, as the reliability of the classification has been shown to be inconsistent. The purpose of this study was to compare the semi quantitative MRI-based Goutallier Classification applied by 5 different raters to experimental MR spectroscopic quantitative fat measurement in order to determine the correlation between this classification system and the true extent of fatty degeneration shown by spectroscopy. MRI-scans of 42 patients with rotator cuff tears were examined by 5 shoulder surgeons and were graduated according to the MRI-based Goutallier Classification proposed by Fuchs et al. Additionally the fat/water ratio was measured with MR spectroscopy using the experimental SPLASH technique. The semi quantitative grading according to the Goutallier Classification was statistically correlated with the quantitative measured fat/water ratio using Spearman's rank correlation. Statistical analysis of the data revealed only fair correlation of the Goutallier Classification system and the quantitative fat/water ratio with R = 0.35 (p < 0.05). By dichotomizing the scale the correlation was 0.72. The interobserver and intraobserver reliabilities were substantial with R = 0.62 and R = 0.74 (p < 0.01). The correlation between the semi quantitative MRI based Goutallier Classification system and MR spectroscopic fat measurement is weak. As an adequate estimation of fatty degeneration based on standard MRI may not be possible, quantitative methods need to be considered in order to increase diagnostic safety and thus provide patients with ideal care in regard to the amount of fatty degeneration. Spectroscopic MR measurement may increase the accuracy of the Goutallier classification and thus improve the prediction of clinical results after rotator cuff repair. However, these techniques are currently only available in an experimental setting.

Early MRI Detection and Closed Bone Graft Epiphysiodesis May Alter the Course of Avascular Necrosis Following Unstable Slipped Capital Femoral Epiphysis.

PubMed

Napora, Joshua K; Gilmore, Allison; Son-Hing, Jochen P; Grimberg, Dominic C; Thompson, George H; Liu, Raymond W

2018-04-01

Unstable slipped capital femoral epiphysis (SCFE) has an increased incidence of avascular necrosis (AVN). Early identification and surgical intervention for AVN may help preserve the femoral head. We retrospectively reviewed 48 patients (50 hips) with unstable SCFE managed between 2000 and 2014. AVN was diagnosed based on 2 different postoperative protocols. Seventeen patients (17 hips) had a scheduled magnetic resonance imaging (MRI) between 1 and 6 months from initial surgery, and the remaining 31 patients (33 hips) were evaluated by plain radiographs alone. If AVN was diagnosed, we offered core decompression and closed bone graft epiphysiodesis (CBGE) to mitigate its affects. At final follow-up, we assessed progression of AVN using the Steinberg classification. Overall 13 hips (26%) with unstable SCFEs developed AVN. MRI revealed AVN in 7 of 17 hips (41%) at a mean of 2.5 months postoperatively (range, 1.0 to 5.2 mo). Six hips diagnosed by MRI received surgical intervention (4 CBGE, 1 free vascularized fibula graft, and 1 repinning due to screw cutout) at a mean of 4.1 months (range, 1.3 to 7.2 mo) postoperatively. None of the 4 patients treated with CBGE within 2 months postoperatively progressed to stage IVC AVN. The 2 patients treated after 4 months postoperatively both progressed to stage VC AVN.Plain radiographs demonstrated AVN in 6 of 33 hips (18%) at a mean of 6.8 months postoperatively (range, 2.1 to 21.1 mo). One patient diagnosed with stage IVB AVN at 2.4 months had screw cutout and received CBGE at 2.5 months from initial pinning. The remaining 5 were not offered surgical intervention. Five of the 6 radiographically diagnosed AVN, including the 1 treated with CBGE, progressed to stage IVC AVN or greater. Although all patients with positive MRI scans developed radiographic AVN, none of the 4 patients treated with CBGE within 2 months after pinning developed grade IVC or greater AVN. Early MRI detection and CBGE may mitigate the effects of AVN after SCFE. Level III-retrospective comparative study.

High-resolution 3-T MRI of the triangular fibrocartilage complex in the wrist: injury pattern and MR features.

PubMed

Zhan, Huili; Zhang, Huibo; Bai, Rongjie; Qian, Zhanhua; Liu, Yue; Zhang, Heng; Yin, Yuming

2017-12-01

To investigate if using high-resolution 3-T MRI can identify additional injuries of the triangular fibrocartilage complex (TFCC) beyond the Palmer classification. Eighty-six patients with surgically proven TFCC injury were included in this study. All patients underwent high-resolution 3-T MRI of the injured wrist. The MR imaging features of TFCC were analyzed according to the Palmer classification. According to the Palmer classification, 69 patients could be classified as having Palmer injuries (52 had traumatic tears and 17 had degenerative tears). There were 17 patients whose injuries could not be classified according to the Palmer classification: 13 had volar or dorsal capsular TFC detachment and 4 had a horizontal tear of the articular disk. Using high-resolution 3-T MRI, we have not only found all the TFCC injuries described in the Palmer classification, additional injury types were found in this study, including horizontal tear of the TFC and capsular TFC detachment. We propose the modified Palmer classification and add the injury types that were not included in the original Palmer classification.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

PubMed

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M Iqbal B; Ramli, Abdul Rahman B

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection

PubMed Central

Jalalian, Afsaneh; Mashohor, Syamsiah; Mahmud, Rozi; Karasfi, Babak; Saripan, M. Iqbal B.; Ramli, Abdul Rahman B.

2017-01-01

Breast cancer is the most prevalent cancer that affects women all over the world. Early detection and treatment of breast cancer could decline the mortality rate. Some issues such as technical reasons, which related to imaging quality and human error, increase misdiagnosis of breast cancer by radiologists. Computer-aided detection systems (CADs) are developed to overcome these restrictions and have been studied in many imaging modalities for breast cancer detection in recent years. The CAD systems improve radiologists' performance in finding and discriminating between the normal and abnormal tissues. These procedures are performed only as a double reader but the absolute decisions are still made by the radiologist. In this study, the recent CAD systems for breast cancer detection on different modalities such as mammography, ultrasound, MRI, and biopsy histopathological images are introduced. The foundation of CAD systems generally consist of four stages: Pre-processing, Segmentation, Feature extraction, and Classification. The approaches which applied to design different stages of CAD system are summarised. Advantages and disadvantages of different segmentation, feature extraction and classification techniques are listed. In addition, the impact of imbalanced datasets in classification outcomes and appropriate methods to solve these issues are discussed. As well as, performance evaluation metrics for various stages of breast cancer detection CAD systems are reviewed. PMID:28435432

Local recurrence of squamous cell carcinoma of the head and neck after radio(chemo)therapy: Diagnostic performance of FDG-PET/MRI with diffusion-weighted sequences.

PubMed

Becker, Minerva; Varoquaux, Arthur D; Combescure, Christophe; Rager, Olivier; Pusztaszeri, Marc; Burkhardt, Karim; Delattre, Bénédicte M A; Dulguerov, Pavel; Dulguerov, Nicolas; Katirtzidou, Eirini; Caparrotti, Francesca; Ratib, Osman; Zaidi, Habib; Becker, Christoph D

2018-02-01

To determine the diagnostic performance of FDG-PET/MRI with diffusion-weighted imaging (FDG-PET/DWIMRI) for detection and local staging of head and neck squamous cell carcinoma (HNSCC) after radio(chemo)therapy. This was a prospective study that included 74 consecutive patients with previous radio(chemo)therapy for HNSCC and in whom tumour recurrence or radiation-induced complications were suspected clinically. The patients underwent hybrid PET/MRI examinations with morphological MRI, DWI and FDG-PET. Experienced readers blinded to clinical/histopathological data evaluated images according to established diagnostic criteria taking into account the complementarity of multiparametric information. The standard of reference was histopathology with whole-organ sections and follow-up ≥24 months. Statistical analysis considered data clustering. The proof of diagnosis was histology in 46/74 (62.2%) patients and follow-up (mean ± SD = 34 ± 8 months) in 28/74 (37.8%). Thirty-eight patients had 43 HNSCCs and 46 patients (10 with and 36 without tumours) had 62 benign lesions/complications. Sensitivity, specificity, and positive and negative predictive value of PET/DWIMRI were 97.4%, 91.7%, 92.5% and 97.1% per patient, and 93.0%, 93.5%, 90.9%, and 95.1% per lesion, respectively. Agreement between imaging-based and pathological T-stage was excellent (kappa = 0.84, p < 0.001). FDG-PET/DWIMRI yields excellent results for detection and T-classification of HNSCC after radio(chemo)therapy. • FDG-PET/DWIMRI yields excellent results for the detection of post-radio(chemo)therapy HNSCC recurrence. • Prospective one-centre study showed excellent agreement between imaging-based and pathological T-stage. • 97.5% of positive concordant MRI, DWI and FDG-PET results correspond to recurrence. • 87% of discordant MRI, DWI and FDG-PET results correspond to benign lesions. • Multiparametric FDG-PET/DWIMRI facilitates planning of salvage surgery in the irradiated neck.

Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

PubMed

Zwemmer, J N P; Berkhof, J; Castelijns, J A; Barkhof, F; Polman, C H; Uitdehaag, B M J

2006-10-01

Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

[Magnetic resonance semiotics of prostate cancer according to the PI-RADS classification. The clinical diagnostic algorithm of a study].

PubMed

Korobkin, A S; Shariya, M A; Chaban, A S; Voskanvan, G A; Vinarov, A Z

2015-01-01

to elaborate the magnetic resonance imaging (MRI) signs of prostate cancer (PC) in accordance with the PI-RADS classification during multiparametric MRI (mpMRI). A total of 89 men aged 20 to 82 years were examined. A control group consisted of 8 (9%) healthy volunteers younger than 30 years of age with no urological history to obtain control images and MRI plots and 20 (22.5%) men aged 26-76 years, whose morphological changes were inflammatory and hyperplastic. The second age-matched group included 61 (68.5%) patients diagnosed with prostate cancer at morphological examination. A set of studies included digital rectal examination, serum prostate-specific antigen, and transrectal ultrasound-guided prostate biopsy. All the patients underwent prostate mpMRI applying a 3.0 T Achieva MRI scanner (Philips, the Netherlands). The patients have been found to have mpMRI signs that were typical of PC; its MRI semiotics according to the PI-RADS classification is presented. Each mpMRI procedure has been determined to be of importance and informative value in detecting PC. The comprehensive mpMRI approach to diagnosing PC improves the quality and diagnostic value of prostate MRI.

[The initial (I and II) and advanced (III and IV) stages of juvenile patellar chondromalacia. Its diagnosis by magnetic resonance using a 1.5-T magnet with FLASH sequences].

PubMed

Macarini, L; Rizzo, A; Martino, F; Zaccheo, N; Angelelli, G; Rotondo, A

1998-06-01

Juvenile patellar chondromalacia is a common orthopedic disorder which can mimic other conditions; early diagnosis is mandatory to prevent its evolution into osteoarthrosis. In the early stages of patellar chondromalacia (I and II), the lesions originate in the deep cartilage layer and the joint surface is not affected. Arthroscopy can demonstrate joint surface changes only and give indirect information about deeper lesions. We investigated the yield of 2D FLASH MRI with 30 degrees flip angle and a dedicated coil in the diagnosis of patellar chondromalacia, especially in its early stages. Eighteen patients (mean age: 21 years) with clinically suspected patellar chondromalacia were examined with MRI; 13 of them were also submitted to arthroscopy. A 1.5 T unit with a transmit-and-receive extremity coil was used. We acquired T1 SE sequences (TR/TE: 500-700/15/20) and 2D T2* FLASH sequence (TR/TE/FA: 500-800/18/30 degrees). The field of view was 160-180 mm and the matrix 192 x 256, with 2-3 NEX. The images were obtained on the axial plane. The lesions were classified in 4 stages according to Shahriaree classification. Agreement between MR and arthroscopic findings was good in both early and advanced lesions in 12/13 cases. Early lesions appeared as hyperintense focal thickening of the hyaline cartilage (stage I) or as small cystic lesions within the cartilage and no articular surface involvement (stage II). The medial patellar facet was the most frequent site. Advanced lesions appeared as articular surface ulcerations, thinning and cartilage hypointensity (stage III); stage IV lesions presented as complete erosions of the hyaline cartilage and hypointense underlying bone. 2D FLASH MRI with 30 degrees flip angle can show the differences in water content in the cartilage and thus permit to detect early chondromalacia lesions in the deep cartilage.

Nonlinearity-aware based dimensionality reduction and over-sampling for AD/MCI classification from MRI measures.

PubMed

Cao, Peng; Liu, Xiaoli; Yang, Jinzhu; Zhao, Dazhe; Huang, Min; Zhang, Jian; Zaiane, Osmar

2017-12-01

Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response). To better capture the complicated but more flexible relationship, we propose a multi-kernel based dimensionality reduction and over-sampling approaches. We combined Marginal Fisher Analysis with ℓ 2,1 -norm based multi-kernel learning (MKMFA) to achieve the sparsity of region-of-interest (ROI), which leads to simultaneously selecting a subset of the relevant brain regions and learning a dimensionality transformation. Meanwhile, a multi-kernel over-sampling (MKOS) was developed to generate synthetic instances in the optimal kernel space induced by MKMFA, so as to compensate for the class imbalanced distribution. We comprehensively evaluate the proposed models for the diagnostic classification (binary class and multi-class classification) including all subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The experimental results not only demonstrate the proposed method has superior performance over multiple comparable methods, but also identifies relevant imaging biomarkers that are consistent with prior medical knowledge. Copyright © 2017 Elsevier Ltd. All rights reserved.

Intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injuries.

PubMed

Wangensteen, Arnlaug; Tol, Johannes L; Roemer, Frank W; Bahr, Roald; Dijkstra, H Paul; Crema, Michel D; Farooq, Abdulaziz; Guermazi, Ali

2017-04-01

To assess and compare the intra- and interrater reliability of three different MRI grading and classification systems after acute hamstring injury. Male athletes (n=40) with clinical diagnosis of acute hamstring injury and MRI ≤5days were selected from a prospective cohort. Two radiologists independently evaluated the MRIs using standardised scoring form including the modified Peetrons grading system, the Chan acute muscle strain injury classification and the British Athletics Muscle Injury Classification. Intra-and interrater reliability was assessed with linear weighted kappa (κ) or unweighted Cohen's κ and percentage agreement was calculated. We observed 'substantial' to 'almost perfect' intra- (κ range 0.65-1.00) and interrater reliability (κ range 0.77-1.00) with percentage agreement 83-100% and 88-100%, respectively, for severity gradings, overall anatomical sites and overall classifications for the three MRI systems. We observed substantial variability (κ range -0.05 to 1.00) for subcategories within the Chan classification and the British Athletics Muscle Injury Classification, however, the prevalence of positive scorings was low for some subcategories. The modified Peetrons grading system, overall Chan classification and overall British Athletics Muscle Injury Classification demonstrated 'substantial' to 'almost perfect' intra- and interrater reliability when scored by experienced radiologists. The intra- and interrater reliability for the anatomical subcategories within the classifications remains unclear. Copyright © 2017 Elsevier B.V. All rights reserved.

Rotationally invariant clustering of diffusion MRI data using spherical harmonics

NASA Astrophysics Data System (ADS)

Liptrot, Matthew; Lauze, François

2016-03-01

We present a simple approach to the voxelwise classification of brain tissue acquired with diffusion weighted MRI (DWI). The approach leverages the power of spherical harmonics to summarise the diffusion information, sampled at many points over a sphere, using only a handful of coefficients. We use simple features that are invariant to the rotation of the highly orientational diffusion data. This provides a way to directly classify voxels whose diffusion characteristics are similar yet whose primary diffusion orientations differ. Subsequent application of machine-learning to the spherical harmonic coefficients therefore may permit classification of DWI voxels according to their inferred underlying fibre properties, whilst ignoring the specifics of orientation. After smoothing apparent diffusion coefficients volumes, we apply a spherical harmonic transform, which models the multi-directional diffusion data as a collection of spherical basis functions. We use the derived coefficients as voxelwise feature vectors for classification. Using a simple Gaussian mixture model, we examined the classification performance for a range of sub-classes (3-20). The results were compared against existing alternatives for tissue classification e.g. fractional anisotropy (FA) or the standard model used by Camino.1 The approach was implemented on both two publicly-available datasets: an ex-vivo pig brain and in-vivo human brain from the Human Connectome Project (HCP). We have demonstrated how a robust classification of DWI data can be performed without the need for a model reconstruction step. This avoids the potential confounds and uncertainty that such models may impose, and has the benefit of being computable directly from the DWI volumes. As such, the method could prove useful in subsequent pre-processing stages, such as model fitting, where it could inform about individual voxel complexities and improve model parameter choice.

Multimodal Classification of Schizophrenia Patients with MEG and fMRI Data Using Static and Dynamic Connectivity Measures

PubMed Central

Cetin, Mustafa S.; Houck, Jon M.; Rashid, Barnaly; Agacoglu, Oktay; Stephen, Julia M.; Sui, Jing; Canive, Jose; Mayer, Andy; Aine, Cheryl; Bustillo, Juan R.; Calhoun, Vince D.

2016-01-01

Mental disorders like schizophrenia are currently diagnosed by physicians/psychiatrists through clinical assessment and their evaluation of patient's self-reported experiences as the illness emerges. There is great interest in identifying biological markers of prognosis at the onset of illness, rather than relying on the evolution of symptoms across time. Functional network connectivity, which indicates a subject's overall level of “synchronicity” of activity between brain regions, demonstrates promise in providing individual subject predictive power. Many previous studies reported functional connectivity changes during resting-state using only functional magnetic resonance imaging (fMRI). Nevertheless, exclusive reliance on fMRI to generate such networks may limit the inference of the underlying dysfunctional connectivity, which is hypothesized to be a factor in patient symptoms, as fMRI measures connectivity via hemodynamics. Therefore, combination of connectivity assessments using fMRI and magnetoencephalography (MEG), which more directly measures neuronal activity, may provide improved classification of schizophrenia than either modality alone. Moreover, recent evidence indicates that metrics of dynamic connectivity may also be critical for understanding pathology in schizophrenia. In this work, we propose a new framework for extraction of important disease related features and classification of patients with schizophrenia based on using both fMRI and MEG to investigate functional network components in the resting state. Results of this study show that the integration of fMRI and MEG provides important information that captures fundamental characteristics of functional network connectivity in schizophrenia and is helpful for prediction of schizophrenia patient group membership. Combined fMRI/MEG methods, using static functional network connectivity analyses, improved classification accuracy relative to use of fMRI or MEG methods alone (by 15 and 12.45%, respectively), while combined fMRI/MEG methods using dynamic functional network connectivity analyses improved classification up to 5.12% relative to use of fMRI alone and up to 17.21% relative to use of MEG alone. PMID:27807403

Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity.

PubMed

Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B; Dodd, Keith C; Young, Brittany M; Jacobson, Tyler; McMillan, Matthew; Thoma, Jaclyn; Advani, Hemali; Nair, Veena A; Kang, Theresa J; Caldera, Kristin; Edwards, Dorothy F; Williams, Justin C; Prabhakaran, Vivek

2018-01-01

Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.

Whole-body muscle MRI in 20 patients suffering from late onset Pompe disease: Involvement patterns.

PubMed

Carlier, Robert-Yves; Laforet, Pascal; Wary, Claire; Mompoint, Dominique; Laloui, Kenza; Pellegrini, Nadine; Annane, Djillali; Carlier, Pierre G; Orlikowski, David

2011-11-01

To describe muscle involvement on whole-body MRI scans in adult patients at different stages of late-onset Pompe disease. Twenty patients aged 37 to 75 were examined. Five were bedridden and required ventilatory support. Axial and coronal T1 turbo-spin-echo sequences were performed on 1.5T or 3T systems. MRI was scored for 47 muscles using Mercuri's classification. Whole-body scans were obtained with a mean in-room time of 29 min. Muscle changes consisted of internal bright signals of fatty replacement without severe retraction of the muscles' corpus. Findings were consistent with previous descriptions of spine extensors and pelvic girdle, but also provided new information on recurrent muscle changes particularly in the tongue and subscapularis muscle. Moreover thigh involvement was more heterogeneous than previously described, in terms of distribution across muscles as well as with respect to the overall clinical presentation. Whole-body MRI provides a very evocative description of muscle involvement in Pompe disease in adults. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate detrending of fMRI signal drifts for real-time multiclass pattern classification.

PubMed

Lee, Dongha; Jang, Changwon; Park, Hae-Jeong

2015-03-01

Signal drift in functional magnetic resonance imaging (fMRI) is an unavoidable artifact that limits classification performance in multi-voxel pattern analysis of fMRI. As conventional methods to reduce signal drift, global demeaning or proportional scaling disregards regional variations of drift, whereas voxel-wise univariate detrending is too sensitive to noisy fluctuations. To overcome these drawbacks, we propose a multivariate real-time detrending method for multiclass classification that involves spatial demeaning at each scan and the recursive detrending of drifts in the classifier outputs driven by a multiclass linear support vector machine. Experiments using binary and multiclass data showed that the linear trend estimation of the classifier output drift for each class (a weighted sum of drifts in the class-specific voxels) was more robust against voxel-wise artifacts that lead to inconsistent spatial patterns and the effect of online processing than voxel-wise detrending. The classification performance of the proposed method was significantly better, especially for multiclass data, than that of voxel-wise linear detrending, global demeaning, and classifier output detrending without demeaning. We concluded that the multivariate approach using classifier output detrending of fMRI signals with spatial demeaning preserves spatial patterns, is less sensitive than conventional methods to sample size, and increases classification performance, which is a useful feature for real-time fMRI classification. Copyright © 2014 Elsevier Inc. All rights reserved.

Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect

PubMed Central

Folia, Vasiliki; Petersson, Karl Magnus

2014-01-01

In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs. PMID:24550865

Implicit structured sequence learning: an fMRI study of the structural mere-exposure effect.

PubMed

Folia, Vasiliki; Petersson, Karl Magnus

2014-01-01

In this event-related fMRI study we investigated the effect of 5 days of implicit acquisition on preference classification by means of an artificial grammar learning (AGL) paradigm based on the structural mere-exposure effect and preference classification using a simple right-linear unification grammar. This allowed us to investigate implicit AGL in a proper learning design by including baseline measurements prior to grammar exposure. After 5 days of implicit acquisition, the fMRI results showed activations in a network of brain regions including the inferior frontal (centered on BA 44/45) and the medial prefrontal regions (centered on BA 8/32). Importantly, and central to this study, the inclusion of a naive preference fMRI baseline measurement allowed us to conclude that these fMRI findings were the intrinsic outcomes of the learning process itself and not a reflection of a preexisting functionality recruited during classification, independent of acquisition. Support for the implicit nature of the knowledge utilized during preference classification on day 5 come from the fact that the basal ganglia, associated with implicit procedural learning, were activated during classification, while the medial temporal lobe system, associated with explicit declarative memory, was consistently deactivated. Thus, preference classification in combination with structural mere-exposure can be used to investigate structural sequence processing (syntax) in unsupervised AGL paradigms with proper learning designs.

Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers.

PubMed

De Martino, Federico; Gentile, Francesco; Esposito, Fabrizio; Balsi, Marco; Di Salle, Francesco; Goebel, Rainer; Formisano, Elia

2007-01-01

We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates of global properties of the ICs and are largely independent of a specific experimental design and stimulus timing. In the second step a machine learning algorithm automatically separates the IC-fingerprints into six general classes after preliminary training performed on a small subset of expert-labeled components. We illustrate this approach in a multisubject fMRI study employing visual structure-from-motion stimuli encoding faces and control random shapes. We show that: (1) IC-fingerprints are a valuable tool for the inspection, characterization and selection of fMRI-ICs and (2) automatic classifications of fMRI-ICs in new subjects present a high correspondence with those obtained by expert visual inspection of the components. Importantly, our classification procedure highlights several neurophysiologically interesting processes. The most intriguing of which is reflected, with high intra- and inter-subject reproducibility, in one IC exhibiting a transiently task-related activation in the 'face' region of the primary sensorimotor cortex. This suggests that in addition to or as part of the mirror system, somatotopic regions of the sensorimotor cortex are involved in disambiguating the perception of a moving body part. Finally, we show that the same classification algorithm can be successfully applied, without re-training, to fMRI collected using acquisition parameters, stimulation modality and timing considerably different from those used for training.

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

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.

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

PubMed

Ramírez, J; Górriz, J M; Ortiz, A; Martínez-Murcia, F J; Segovia, F; Salas-Gonzalez, D; Castillo-Barnes, D; Illán, I A; Puntonet, C G

2018-05-15

Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments. The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level. The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects. The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning. A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-class SVM model for fMRI-based classification and grading of liver fibrosis

NASA Astrophysics Data System (ADS)

Freiman, M.; Sela, Y.; Edrei, Y.; Pappo, O.; Joskowicz, L.; Abramovitch, R.

2010-03-01

We present a novel non-invasive automatic method for the classification and grading of liver fibrosis from fMRI maps based on hepatic hemodynamic changes. This method automatically creates a model for liver fibrosis grading based on training datasets. Our supervised learning method evaluates hepatic hemodynamics from an anatomical MRI image and three T2*-W fMRI signal intensity time-course scans acquired during the breathing of air, air-carbon dioxide, and carbogen. It constructs a statistical model of liver fibrosis from these fMRI scans using a binary-based one-against-all multi class Support Vector Machine (SVM) classifier. We evaluated the resulting classification model with the leave-one out technique and compared it to both full multi-class SVM and K-Nearest Neighbor (KNN) classifications. Our experimental study analyzed 57 slice sets from 13 mice, and yielded a 98.2% separation accuracy between healthy and low grade fibrotic subjects, and an overall accuracy of 84.2% for fibrosis grading. These results are better than the existing image-based methods which can only discriminate between healthy and high grade fibrosis subjects. With appropriate extensions, our method may be used for non-invasive classification and progression monitoring of liver fibrosis in human patients instead of more invasive approaches, such as biopsy or contrast-enhanced imaging.

Early postoperative repair status after rotator cuff repair cannot be accurately classified using questionnaires of patient function and isokinetic strength evaluation.

PubMed

Colliver, Jessica; Wang, Allan; Joss, Brendan; Ebert, Jay; Koh, Eamon; Breidahl, William; Ackland, Timothy

2016-04-01

This study investigated if patients with an intact tendon repair or partial-thickness retear early after rotator cuff repair display differences in clinical evaluations and whether early tendon healing can be predicted using these assessments. We prospectively evaluated 60 patients at 16 weeks after arthroscopic supraspinatus repair. Evaluation included the Oxford Shoulder Score, 11-item version of the Disabilities of the Arm, Shoulder and Hand, visual analog scale for pain, 12-item Short Form Health Survey, isokinetic strength, and magnetic resonance imaging (MRI). Independent t tests investigated clinical differences in patients based on the Sugaya MRI rotator cuff classification system (grades 1, 2, or 3). Discriminant analysis determined whether intact repairs (Sugaya grade 1) and partial-thickness retears (Sugaya grades 2 and 3) could be predicted. No differences (P < .05) existed in the clinical or strength measures. Although discriminant analysis revealed the 11-item version of the Disabilities of the Arm, Shoulder and Hand produced a 97% true-positive rate for predicting partial thickness retears, it also produced a 90% false-positive rate whereby it incorrectly predicted a retear in 90% of patients whose repair was intact. The ability to discriminate between groups was enhanced with up to 5 variables entered; however, only 87% of the partial-retear group and 36% of the intact-repair group were correctly classified. No differences in clinical scores existed between patients stratified by the Sugaya MRI classification system at 16 weeks. An intact repair or partial-thickness retear could not be accurately predicted. Our results suggest that correct classification of healing in the early postoperative stages should involve imaging. Copyright © 2016 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

The value of CT and MRI in the classification and surgical decision-making among spine surgeons in thoracolumbar spinal injuries.

PubMed

Rajasekaran, Shanmuganathan; Vaccaro, Alexander R; Kanna, Rishi Mugesh; Schroeder, Gregory D; Oner, Frank Cumhur; Vialle, Luiz; Chapman, Jens; Dvorak, Marcel; Fehlings, Michael; Shetty, Ajoy Prasad; Schnake, Klaus; Maheshwaran, Anupama; Kandziora, Frank

2017-05-01

Although imaging has a major role in evaluation and management of thoracolumbar spinal trauma by spine surgeons, the exact role of computed tomography (CT) and magnetic resonance imaging (MRI) in addition to radiographs for fracture classification and surgical decision-making is unclear. Spine surgeons (n = 41) from around the world classified 30 thoracolumbar fractures. The cases were presented in a three-step approach: first plain radiographs, followed by CT and MRI images. Surgeons were asked to classify according to the AOSpine classification system and choose management in each of the three steps. Surgeons correctly classified 43.4 % of fractures with plain radiographs alone; after, additionally, evaluating CT and MRI images, this percentage increased by further 18.2 and 2.2 %, respectively. AO type A fractures were identified in 51.7 % of fractures with radiographs, while the number of type B fractures increased after CT and MRI. The number of type C fractures diagnosed was constant across the three steps. Agreement between radiographs and CT was fair for A-type (k = 0.31), poor for B-type (k = 0.19), but it was excellent between CT and MRI (k > 0.87). CT and MRI had similar sensitivity in identifying fracture subtypes except that MRI had a higher sensitivity (56.5 %) for B2 fractures (p < 0.001). The need for surgical fixation was deemed present in 72 % based on radiographs alone and increased to 81.7 % with CT images (p < 0.0001). The assessment for need of surgery did not change after an MRI (p = 0.77). For accurate classification, radiographs alone were insufficient except for C-type injuries. CT is mandatory for accurately classifying thoracolumbar fractures. Though MRI did confer a modest gain in sensitivity in B2 injuries, the study does not support the need for routine MRI in patients for classification, assessing instability or need for surgery.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Staging performance of whole-body DWI, PET/CT and PET/MRI in invasive ductal carcinoma of the breast.

PubMed

Catalano, Onofrio Antonio; Daye, Dania; Signore, Alberto; Iannace, Carlo; Vangel, Mark; Luongo, Angelo; Catalano, Marco; Filomena, Mazzeo; Mansi, Luigi; Soricelli, Andrea; Salvatore, Marco; Fuin, Niccolo; Catana, Ciprian; Mahmood, Umar; Rosen, Bruce Robert

2017-07-01

The aim of the present study was to evaluate the performance of whole-body diffusion-weighted imaging (WB-DWI), whole-body positron emission tomography with computed tomography (WB-PET/CT), and whole-body positron emission tomography with magnetic resonance imaging (WB-PET/MRI) in staging patients with untreated invasive ductal carcinoma of the breast. Fifty-one women with newly diagnosed invasive ductal carcinoma of the breast underwent WB-DWI, WB-PET/CT and WB-PET/MRI before treatment. A radiologist and a nuclear medicine physician reviewed in consensus the images from the three modalities and searched for occurrence, number and location of metastases. Final staging, according to each technique, was compared. Pathology and imaging follow-up were used as the reference. WB-DWI, WB-PET/CT and WB-PET/MRI correctly and concordantly staged 33/51 patients: stage IIA in 7 patients, stage IIB in 8 patients, stage IIIC in 4 patients and stage IV in 14 patients. WB-DWI, WB-PET/CT and WB-PET/MRI incorrectly and concordantly staged 1/51 patient as stage IV instead of IIIA. Discordant staging was reported in 17/51 patients. WB-PET/MRI resulted in improved staging when compared to WB-PET/CT (50 correctly staged on WB-PET/MRI vs. 38 correctly staged on WB-PET/CT; McNemar's test; p<0.01). Comparing the performance of WB-PET/MRI and WB-DWI (43 correct) did not reveal a statistically significant difference (McNemar test, p=0.14). WB-PET/MRI is more accurate in the initial staging of breast cancer than WB-DWI and WB-PET/CT, however, the discrepancies between WB-PET/MRI and WB-DWI were not statistically significant. When available, WB-PET/MRI should be considered for staging patient with invasive ductal breast carcinoma.

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.

PubMed

Duraisamy, Baskar; Shanmugam, Jayanthi Venkatraman; Annamalai, Jayanthi

2018-02-19

An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

MRI in T staging of rectal cancer: How effective is it?

PubMed Central

Mulla, MG; Deb, R; Singh, R

2010-01-01

Background: Rectal cancer constitutes about one-third of all gastrointestinal (GI) tract tumors. Because of the high recurrence rates (30%) in rectal cancer, it is vitally important to accurately stage these tumours preoperatively so that appropriate surgical resection can be undertaken. MRI is the ideal technique for the preoperative staging of these tumours. Aim: To determine the accuracy of local T staging of rectal cancer with MRI, using histopathological staging as the gold. Materials and Methods: Forty consecutive patients admitted with rectal cancer over a period of 18 months were included in this retrospective study. MRI scans were performed prior to surgery in all patients, on 1.5T scanners. Two radiologists, with a special interest in gastrointestinal imaging reported all images. Two dedicated histopathologists reported the histology slides. The accuracy of preoperative local MRI T staging was assessed by comparison with postoperative histopathological staging. Results: There was agreement between MRI and histopathology (TNM) staging in 12 patients (30%). The sensitivity and specificity of MRI for T staging was 89% and 67% respectively. The circumferential resection margin (CRM) status was accurately staged in 94.1% of the patients. Conclusions: Preoperative staging with MRI is sensitive in identifying CRM involvement, which is the main factor affecting the outcome of surgery. PMID:20607023

Oncologic PET/MRI, part 1: tumors of the brain, head and neck, chest, abdomen, and pelvis.

PubMed

Buchbender, Christian; Heusner, Till A; Lauenstein, Thomas C; Bockisch, Andreas; Antoch, Gerald

2012-06-01

In oncology, staging forms the basis for prognostic consideration and directly influences patient care by determining the therapeutic approach. Cross-sectional imaging techniques, especially when combined with PET information, play an important role in cancer staging. With the recent introduction of integrated whole-body PET/MRI into clinical practice, a novel metabolic-anatomic imaging technique is now available. PET/MRI seems to be highly accurate in T-staging of tumor entities for which MRI has traditionally been favored, such as squamous cell carcinomas of the head and neck. By adding functional MRI to PET, PET/MRI may further improve diagnostic accuracy in the differentiation of scar tissue from recurrence of tumors such as rectal cancer. This hypothesis will have to be assessed in future studies. With regard to N-staging, PET/MRI does not seem to provide a considerable benefit as compared with PET/CT but provides similar N-staging accuracy when applied as a whole-body staging approach. M-staging will benefit from MRI accuracy in the brain and the liver. The purpose of this review is to summarize the available first experiences with PET/MRI and to outline the potential value of PET/MRI in oncologic applications for which data on PET/MRI are still lacking.

MRI classification system (MRICS) for children with cerebral palsy: development, reliability, and recommendations.

PubMed

Himmelmann, Kate; Horber, Veronka; De La Cruz, Javier; Horridge, Karen; Mejaski-Bosnjak, Vlatka; Hollody, Katalin; Krägeloh-Mann, Ingeborg

2017-01-01

To develop and evaluate a classification system for magnetic resonance imaging (MRI) findings of children with cerebral palsy (CP) that can be used in CP registers. The classification system was based on pathogenic patterns occurring in different periods of brain development. The MRI classification system (MRICS) consists of five main groups: maldevelopments, predominant white matter injury, predominant grey matter injury, miscellaneous, and normal findings. A detailed manual for the descriptions of these patterns was developed, including test cases (www.scpenetwork.eu/en/my-scpe/rtm/neuroimaging/cp-neuroimaging/). A literature review was performed and MRICS was compared with other classification systems. An exercise was carried out to check applicability and interrater reliability. Professionals working with children with CP or in CP registers were invited to participate in the exercise and chose to classify either 18 MRIs or MRI reports of children with CP. Classification systems in the literature were compatible with MRICS and harmonization possible. Interrater reliability was found to be good overall (k=0.69; 0.54-0.82) among the 41 participants and very good (k=0.81; 0.74-0.92) using the classification based on imaging reports. Surveillance of Cerebral Palsy in Europe (SCPE) proposes the MRICS as a reliable tool. Together with its manual it is simple to apply for CP registers. © 2016 Mac Keith Press.

Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: a prospective, multicenter, European study.

PubMed

Taylor, Fiona G M; Quirke, Philip; Heald, Richard J; Moran, Brendan; Blomqvist, Lennart; Swift, Ian; Sebag-Montefiore, David J; Tekkis, Paris; Brown, Gina

2011-04-01

To assess local recurrence, disease-free survival, and overall survival in magnetic resonance imaging (MRI)-predicted good prognosis tumors treated by surgery alone. The MERCURY study reported that high-resolution MRI can accurately stage rectal cancer. The routine policy in most centers involved in the MERCURY study was primary surgery alone in MRI-predicted stage II or less and in MRI "good prognosis" stage III with selective avoidance of neoadjuvant therapy. Data were collected prospectively on all patients included in the MERCURY study who were staged as MRI-defined "good" prognosis tumors. "Good" prognosis included MRI-predicted safe circumferential resection margins, with MRI-predicted T2/T3a/T3b (less than 5 mm spread from muscularis propria), regardless of MRI N stage. None received preoperative or postoperative radiotherapy. Overall survival, disease-free survival, and local recurrence were calculated. Of 374 patients followed up in the MERCURY study, 122 (33%) were defined as "good prognosis" stage III or less on MRI. Overall and disease-free survival for all patients with MRI "good prognosis" stage I, II and III disease at 5 years was 68% and 85%, respectively. The local recurrence rate for this series of patients predicted to have a good prognosis tumor on MRI was 3%. The preoperative identification of good prognosis tumors using MRI will allow stratification of patients and better targeting of preoperative therapy. This study confirms the ability of MRI to select patients who are likely to have a good outcome with primary surgery alone.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.

PubMed

Zhang, Chuncheng; Song, Sutao; Wen, Xiaotong; Yao, Li; Long, Zhiying

2015-04-30

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data. In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection. Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding. LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Contrast-enhanced computed tomography plus gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging for gross classification of hepatocellular carcinoma.

PubMed

Chen, Chuang; Zhao, Hui; Fu, Xu; Huang, LuoShun; Tang, Min; Yan, XiaoPeng; Sun, ShiQuan; Jia, WenJun; Mao, Liang; Shi, Jiong; Chen, Jun; He, Jian; Zhu, Jin; Qiu, YuDong

2017-05-02

Accurate gross classification through imaging is critical for determination of hepatocellular carcinoma (HCC) patient prognoses and treatment strategies. The present retrospective study evaluated the utility of contrast-enhanced computed tomography (CE-CT) combined with gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) for diagnosis and classification of HCCs prior to surgery. Ninety-four surgically resected HCC nodules were classified as simple nodular (SN), SN with extranodular growth (SN-EG), confluent multinodular (CMN), or infiltrative (IF) types. SN-EG, CMN and IF samples were grouped as non-SN. The abilities of the two imaging modalities to differentiate non-SN from SN HCCs were assessed using the EOB-MRI hepatobiliary phase and CE-CT arterial, portal, and equilibrium phases. Areas under the ROC curves for non-SN diagnoses were 0.765 (95% confidence interval [CI]: 0.666-0.846) for CE-CT, 0.877 (95% CI: 0.793-0.936) for EOB-MRI, and 0.908 (95% CI: 0.830-0.958) for CE-CT plus EOB-MRI. Sensitivities, specificities, and accuracies with respect to identification of non-SN tumors of all sizes were 71.4%, 81.6%, and 75.5% for CE-CT; 96.4%, 78.9%, and 89.3% for EOB-MRI; and 98.2%, 84.2%, and 92.5% for CE-CT plus EOB-MRI. These results show that CE-CT combined with EOB-MRI offers a more accurate imaging evaluation for HCC gross classification than either modality alone.

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

DTIC Science & Technology

2013-03-01

fMRI data (e.g. Kamitami & Tong, 2005). This approach has been remarkably successful in classifying mental workload in complex tasks (Berka, et al...1991). These previous studies relied upon spectral comparison rather than classification. In previous research examining the stability of fMRI ...chose to focus on electrophysiology, as the collection conditions may be more carefully controlled across days than fMRI and it is more amenable to

Transperineal ultrasonography: First level exam in IBD patients with perianal disease.

PubMed

Terracciano, Fulvia; Scalisi, Giuseppe; Bossa, Fabrizio; Scimeca, Daniela; Biscaglia, Giuseppe; Mangiacotti, Michele; Valvano, Maria Rosa; Perri, Francesco; Simeone, Anna; Andriulli, Angelo

2016-08-01

A pelvic magnetic resonance imaging (MRI) represents the front-line method for evaluating perianal disease in patients with inflammatory bowel disease (IBD). Recently, transperineal ultrasonography (TPUS) has been proposed as a simple, safe, time-sparing and useful diagnostic technique to assess different pathological conditions of the pelvic floor. The aim of this prospective single centre study was to evaluate the accuracy of TPUS versus MRI for the detection and classification of perineal disease in IBD patients. From November 2013 to November 2014, 28 IBD patients underwent either TPUS or MRI. Fistulae and abscesses were classified according to Parks' and AGA's classification methods. A concordance was assessed by k statistics. Overall, 33 fistulae and 8 abscesses were recognized by TPUS (30 and 7 by MRI, respectively). The agreement between TPUS and MRI was 75% according to Parks' classification (k=0.67) and 86% according to AGA classification (k=0.83), while it was 36% (k=0.34) for classifying abscesses. TPUS proved to be as accurate as MRI for detecting superficial and small abscesses and for classifying perianal disease. Both examinations may be performed at the initial presentation of the patient, but TPUS is a cheaper, time-sparing procedure. The optimal use of TPUS might be in follow-up patients. Copyright © 2016 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

[MRI of the rotator cuff: evaluation of a new symptomatologic classification].

PubMed

Tavernier, T; Walch, G; Noël, E; Lapra, C; Bochu, M

1995-05-01

The different classifications use for the rotator cuff pathology seem to be incomplete. We propose a new classification with many advantages: 1) Differentiate the tendinopathy between less serious (grade 2A) and serious (grade 2B). 2) Recognize the intra-tendinous cleavage of the infra-spinatus associated with complete tear of the supra-spinatus. 3) Differentiate partial and complete tears of the supra-spinatus. We established this classification after a retrospective study of 42 patients operated on for a rotator cuff pathology. Every case had had a preoperative MRI. This classification is simple, reliable, especially for the associated intra tendinous cleavage.

Imaging in rectal cancer with emphasis on local staging with MRI

PubMed Central

Arya, Supreeta; Das, Deepak; Engineer, Reena; Saklani, Avanish

2015-01-01

Imaging in rectal cancer has a vital role in staging disease, and in selecting and optimizing treatment planning. High-resolution MRI (HR-MRI) is the recommended method of first choice for local staging of rectal cancer for both primary staging and for restaging after preoperative chemoradiation (CT-RT). HR-MRI helps decide between upfront surgery and preoperative CT-RT. It provides high accuracy for prediction of circumferential resection margin at surgery, T category, and nodal status in that order. MRI also helps assess resectability after preoperative CT-RT and decide between sphincter saving or more radical surgery. Accurate technique is crucial for obtaining high-resolution images in the appropriate planes for correct staging. The phased array external coil has replaced the endorectal coil that is no longer recommended. Non-fat suppressed 2D T2-weighted (T2W) sequences in orthogonal planes to the tumor are sufficient for primary staging. Contrast-enhanced MRI is considered inappropriate for both primary staging and restaging. Diffusion-weighted sequence may be of value in restaging. Multidetector CT cannot replace MRI in local staging, but has an important role for evaluating distant metastases. Positron emission tomography-computed tomography (PET/CT) has a limited role in the initial staging of rectal cancer and is reserved for cases with resectable metastatic disease before contemplating surgery. This article briefly reviews the comprehensive role of imaging in rectal cancer, describes the role of MRI in local staging in detail, discusses the optimal MRI technique, and provides a synoptic report for both primary staging and restaging after CT-RT in routine practice. PMID:25969638

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

PubMed

Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

2006-07-01

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Usefulness of Ilae 2010 classification in Mexican epilepsy patients.

PubMed

Leyva, Ildefonso Rodríguez; Gómez, Juan Francisco Hernández; Enríquez, Fernando Cortés; Sierra, Juan Francisco Hernández

2017-05-15

Advances in neuroimaging, genomics, and molecular biology have improved the understanding of the pathogenesis of epilepsy. That is why the International League Against Epilepsy (ILAE) has created a new classification system. The present study aims to evaluate the association between epilepsy cases classified by the ILAE 2010 classification proposal, electroencephalography (EEG), and magnetic resonance imaging brain findings (MRI). Prospective cross-sectional design of 277 cases of epilepsy seen at the Epilepsy Clinic, Hospital Central "Dr. Ignacio Morones Prieto", were compared with the ILAE classification based on the etiology and clinical manifestations and their MRI and EEG findings. Cochran, Mantell, Haenzel test with significance p<0.05. MRI findings were associated with the etiology of the ILAE classification. According to EEG findings, the structural-metabolic etiology patients had more dysfunctional reports than genetic or unknown etiology patients (p<0.05). The adoption of the ILAE classification is recommended, as it can provide useful guidance towards the etiology of cases of epilepsy even when brain MRIs and EEGs are not available. Copyright © 2017 Elsevier B.V. All rights reserved.

Event-Related fMRI of Category Learning: Differences in Classification and Feedback Networks

ERIC Educational Resources Information Center

Little, Deborah M.; Shin, Silvia S.; Sisco, Shannon M.; Thulborn, Keith R.

2006-01-01

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification…

Amygdala excitability to subliminally presented emotional faces distinguishes unipolar and bipolar depression: an fMRI and pattern classification study.

PubMed

Grotegerd, Dominik; Stuhrmann, Anja; Kugel, Harald; Schmidt, Simone; Redlich, Ronny; Zwanzger, Peter; Rauch, Astrid Veronika; Heindel, Walter; Zwitserlood, Pienie; Arolt, Volker; Suslow, Thomas; Dannlowski, Udo

2014-07-01

Bipolar disorder and Major depressive disorder are difficult to differentiate during depressive episodes, motivating research for differentiating neurobiological markers. Dysfunctional amygdala responsiveness during emotion processing has been implicated in both disorders, but the important rapid and automatic stages of emotion processing in the amygdala have so far never been investigated in bipolar patients. fMRI data of 22 bipolar depressed patients (BD), 22 matched unipolar depressed patients (MDD), and 22 healthy controls (HC) were obtained during processing of subliminal sad, happy and neutral faces. Amygdala responsiveness was investigated using standard univariate analyses as well as pattern-recognition techniques to differentiate the two clinical groups. Furthermore, medication effects on amygdala responsiveness were explored. All subjects were unaware of the emotional faces. Univariate analysis revealed a significant group × emotion interaction within the left amygdala. Amygdala responsiveness to sad>neutral faces was increased in MDD relative to BD. In contrast, responsiveness to happy>neutral faces showed the opposite pattern, with higher amygdala activity in BD than in MDD. Most of the activation patterns in both clinical groups differed significantly from activation patterns of HC--and therefore represent abnormalities. Furthermore, pattern classification on amygdala activation to sad>happy faces yielded almost 80% accuracy differentiating MDD and BD patients. Medication had no significant effect on these findings. Distinct amygdala excitability during automatic stages of the processing of emotional faces may reflect differential pathophysiological processes in BD versus MDD depression, potentially representing diagnosis-specific neural markers mostly unaffected by current psychotropic medication. Copyright © 2013 Wiley Periodicals, Inc.

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

You, D; Aryal, M; Samuels, S

Purpose: A previous study showed that large sub-volumes of tumor with low blood volume (BV) (poorly perfused) in head-and-neck (HN) cancers are significantly associated with local-regional failure (LRF) after chemoradiation therapy, and could be targeted with intensified radiation doses. This study aimed to develop an automated and scalable model to extract voxel-wise contrast-enhanced temporal features of dynamic contrastenhanced (DCE) MRI in HN cancers for predicting LRF. Methods: Our model development consists of training and testing stages. The training stage includes preprocessing of individual-voxel DCE curves from tumors for intensity normalization and temporal alignment, temporal feature extraction from the curves, featuremore » selection, and training classifiers. For feature extraction, multiresolution Haar discrete wavelet transformation is applied to each DCE curve to capture temporal contrast-enhanced features. The wavelet coefficients as feature vectors are selected. Support vector machine classifiers are trained to classify tumor voxels having either low or high BV, for which a BV threshold of 7.6% is previously established and used as ground truth. The model is tested by a new dataset. The voxel-wise DCE curves for training and testing were from 14 and 8 patients, respectively. A posterior probability map of the low BV class was created to examine the tumor sub-volume classification. Voxel-wise classification accuracy was computed to evaluate performance of the model. Results: Average classification accuracies were 87.2% for training (10-fold crossvalidation) and 82.5% for testing. The lowest and highest accuracies (patient-wise) were 68.7% and 96.4%, respectively. Posterior probability maps of the low BV class showed the sub-volumes extracted by our model similar to ones defined by the BV maps with most misclassifications occurred near the sub-volume boundaries. Conclusion: This model could be valuable to support adaptive clinical trials with further validation. The framework could be extendable and scalable to extract temporal contrastenhanced features of DCE-MRI in other tumors. We would like to acknowledge NIH for funding support: UO1 CA183848.« less

Accuracy of magnetic resonance imaging in the pre-operative staging of rectal adenocarcinoma: Experience from a regional Australian cancer center.

PubMed

White, Rohen; Ung, Kim Ann; Mathlum, Maitham

2013-12-01

Selection of the optimal treatment pathway in patients with rectal adenocarcinoma relies on accurate locoregional staging. This study aims to assess the accuracy of staging with magnetic resonance imaging (MRI) and in particular, its accuracy in differentiating patients with early stage disease from those with more advanced disease who benefit from a different treatment approach. Patients who were staged with MRI and received surgery as the first line of treatment for biopsy-proven adenocarcinoma of the rectum were identified. Comparison was made between the clinical stage on MRI and the pathological stage of the surgical specimen. The sensitivity, specificity and overall accuracy of MRI was assessed. In all, 58 eligible patients were identified. In 31% of patients, the extent of disease was underrepresented on preoperative MRI. Sensitivity, specificity and overall accuracy of anorectal MRI in detecting stage II/III disease status in this cohort was 59, 71 and 62%, respectively. MRI underestimated the pathological stage in many patients in this series who may have benefited from the addition of neoadjuvant radiotherapy to their management. This study supports further refinement of preoperative staging and demonstrates that impressive results from highly controlled settings may be difficult to reproduce in community practice. © 2012 Wiley Publishing Asia Pty Ltd.

Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Forensic age estimation based on magnetic resonance imaging of third molars: converting 2D staging into 3D staging.

PubMed

De Tobel, Jannick; Hillewig, Elke; Verstraete, Koenraad

2017-03-01

Established methods to stage development of third molars for forensic age estimation are based on the evaluation of radiographs, which show a 2D projection. It has not been investigated whether these methods require any adjustments in order to apply them to stage third molars on magnetic resonance imaging (MRI), which shows 3D information. To prospectively study root stage assessment of third molars in age estimation using 3 Tesla MRI and to compare this with panoramic radiographs, in order to provide considerations for converting 2D staging into 3D staging and to determine the decisive root. All third molars were evaluated in 52 healthy participants aged 14-26 years using MRI in three planes. Three staging methods were investigated by two observers. In sixteen of the participants, MRI findings were compared with findings on panoramic radiographs. Decisive roots were palatal in upper third molars and distal in lower third molars. Fifty-seven per cent of upper third molars were not assessable on the radiograph, while 96.9% were on MRI. Upper third molars were more difficult to evaluate on radiographs than on MRI (p < .001). Lower third molars were equally assessable on both imaging techniques (93.8% MRI, 98.4% radiograph), with no difference in level of difficulty (p = .375). Inter- and intra-observer agreement for evaluation was higher in MRI than in radiographs. In both imaging techniques lower third molars showed greater inter- and intra-observer agreement compared to upper third molars. MR images in the sagittal plane proved to be essential for staging. In age estimation, 3T MRI of third molars could be valuable. Some considerations are, however, necessary to transfer known staging methods to this 3D technique.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

PubMed

Pedreira, C; Vaudano, A E; Thornton, R C; Chaudhary, U J; Vulliemoz, S; Laufs, H; Rodionov, R; Carmichael, D W; Lhatoo, S D; Guye, M; Quian Quiroga, R; Lemieux, L

2014-10-01

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

PubMed

Sauwen, N; Acou, M; Van Cauter, S; Sima, D M; Veraart, J; Maes, F; Himmelreich, U; Achten, E; Van Huffel, S

2016-01-01

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

Comparison of pelvic phased-array versus endorectal coil magnetic resonance imaging at 3 Tesla for local staging of prostate cancer.

PubMed

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun; Yoo, Eun Sang

2012-05-01

Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI.

A PET/CT-Based Strategy Is a Stronger Predictor of Survival Than a Standard Imaging Strategy in Patients with Head and Neck Squamous Cell Carcinoma.

PubMed

Rohde, Max; Nielsen, Anne L; Pareek, Manan; Johansen, Jørgen; Sørensen, Jens A; Diaz, Anabel; Nielsen, Mie K; Christiansen, Janus M; Asmussen, Jon T; Nguyen, Nina; Gerke, Oke; Thomassen, Anders; Alavi, Abass; Høilund-Carlsen, Poul Flemming; Godballe, Christian

2018-04-01

Our purpose was to examine whether staging of head and neck squamous cell carcinoma (HNSCC) by upfront 18 F-FDG PET/CT (i.e., on the day of biopsy and before the biopsy) discriminates survival better than the traditional imaging strategies based on chest x-ray plus head and neck MRI (CXR/MRI) or chest CT plus head and neck MRI (CCT/MRI). Methods: We performed a masked prospective cohort study based on paired data. Consecutive patients with histologically verified primary HNSCC were recruited from Odense University Hospital from September 2013 to March 2016. All patients underwent CXR/MRI, CCT/MRI, and PET/CT on the same day. Tumors were categorized as localized (stages I and II), locally advanced (stages III and IVB), or metastatic (stage IVC). Discriminative ability for each imaging modality with respect to HNSCC staging were compared using Kaplan-Meier analysis, Cox proportional hazards regression with the Harrell C-index, and net reclassification improvement. Results: In total, 307 patients with histologically verified HNSCC were included. Use of PET/CT significantly altered the stratification of tumor stage when compared with either CXR/MRI or CCT/MRI (χ 2 , P < 0.001 for both). Cancer stages based on PET/CT, but not CXR/MRI or CCT/MRI, were associated with significant differences in mortality risk on Kaplan-Meier analyses ( P ≤ 0.002 for all PET/CT-based comparisons). Furthermore, overall discriminative ability was significantly greater for PET/CT (C-index, 0.712) than for CXR/MRI (C-index, 0.675; P = 0.04) or CCT/MRI (C-index, 0.657; P = 0.02). Finally, PET/CT was significantly associated with a positive net reclassification improvement when compared with CXR/MRI (0.184, P = 0.03) but not CCT/MRI (0.094%, P = 0.31). Conclusion: Tumor stages determined by PET/CT were associated with more distinct prognostic properties in terms of survival than those determined by standard imaging strategies. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Staging studies for cutaneous melanoma in the United States: a population-based analysis.

PubMed

Wasif, Nabil; Etzioni, David; Haddad, Dana; Gray, Richard J; Bagaria, Sanjay P; Pockaj, Barbara A

2015-04-01

Routine cross-sectional imaging for staging of early-stage cutaneous melanoma is not recommended. This study sought to investigate the use of imaging for staging of cutaneous melanoma in the United States. Patients with nonmetastatic cutaneous melanoma newly diagnosed between 2000 and 2007 were identified from the Surveillance Epidemiology End Results-Medicare registry. Any imaging study performed within 90 days after diagnosis was considered a staging study. The study identified 25,643 patients, 3,116 (12.2 %) of whom underwent cross-sectional imaging: positron emission tomography (PET) (7.2 %), computed tomography (CT) (5.9 %), and magnetic resonance imaging (MRI) (0.6 %). From 2000 to 2007, the use of cross-sectional imaging increased from 8.7 to 16.1 % (p < 0.001), driven predominantly by increased usage of PET (4.2-12.1 %). Stratification by T and N classification showed that cross-sectional imaging was used for 8.6 % of T1, 14.3 % of T2, 18.6 % of T3, and 26.7 % of T4 tumors (p < 0.001) and for 33.3 % of node-positive patients versus 11.1 % of node-negative patients (p < 0.001). Factors predictive of cross-sectional imaging included T classification [odds ratio (OR) for T4 vs T1, 2.66; 95 % confidence interval (CI) 2.33-3.03], node positivity (OR 2.70; 95 % CI 2.36-3.10), more recent year of diagnosis (OR 2.05 for 2007 vs 2000; 95 % CI 1.74-2.42), atypical histology, and non-Caucasian race (OR 1.32; 95 % CI 1.02-1.73). The use of cross-sectional imaging for staging of early-stage cutaneous melanoma is increasing in the Medicare population. Better dissemination of guidelines and judicious use of imaging should be encouraged.

Diagnostic tests in urology: magnetic resonance imaging (MRI) for the staging of prostate cancer.

PubMed

Preston, Mark A; Harisinghani, Mukesh G; Mucci, Lorelei; Witiuk, Kelsey; Breau, Rodney H

2013-03-01

WHAT'S KNOWN ON THE SUBJECT? AND WHAT DOES THE STUDY ADD?: The use of MRI for prostate cancer diagnosis and staging is increasing. Indications for prostate MRI are not defined and many clinicians are unsure of how best to use MRI to aid clinical decisions. This evidence-based medicine article addresses the clinical utility of prostate MRI for preoperative staging. Based on a common patient scenario, a guide to calculating the probability of extraprostatic extension is provided. © 2013 BJU International.

Whole-body MRI versus 18F-FDG PET/CT for pretherapeutic assessment and staging of lymphoma: a meta-analysis.

PubMed

Wang, Danyang; Huo, Yanlei; Chen, Suyun; Wang, Hui; Ding, Yingli; Zhu, Xiaochun; Ma, Chao

2018-01-01

18 F-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography/computed tomography (PET/CT) is the reference standard in staging of 18 F-FDG-avid lymphomas; however, there is no recommended functional imaging modality for indolent lymphomas. Therefore, we aimed to compare the performance of whole-body magnetic resonance imaging (WB-MRI) with that of 18 F-FDG PET/CT for lesion detection and initial staging in patients with aggressive or indolent lymphoma. We searched the MEDLINE, EMBASE, and CENTRAL databases for studies that compared WB-MRI with 18 F-FDG PET/CT for lymphoma staging or lesion detection. The methodological quality of the studies was assessed using version 2 of the "Quality Assessment of Diagnostic Accuracy Studies" tool. The pooled staging accuracy ( μ ) of WB-MRI and 18 F-FDG PET/CT for initial staging and for assessing possible heterogeneity ( χ 2 ) across studies were calculated using commercially available software. Eight studies comprising 338 patients were included. In terms of staging, the meta-analytic staging accuracies of WB-MRI and 18 F-FDG PET/CT for Hodgkin lymphoma and aggressive non-Hodgkin lymphoma (NHL) were 98% (95% CI, 94%-100%) and 98% (95% CI, 94%-100%), respectively. The pooled staging accuracy of 18 F-FDG PET/CT dropped to 87% (95% CI, 72%-97%) for staging in patients with indolent lymphoma, whereas that of WB-MRI remained 96% (95% CI, 91%-100%). Subgroup analysis indicated an even lower staging accuracy of 18 F-FDG PET/CT for staging of less FDG-avid indolent NHLs (60%; 95% CI, 23%-92%), in contrast to the superior performance of WB-MRI (98%; 95% CI, 88%-100%). WB-MRI is a promising radiation-free imaging technique that may serve as a viable alternative to 18 F-FDG PET/CT for staging of 18 FDG-avid lymphomas, where 18 F-FDG PET/CT remains the standard of care. Additionally, WB-MRI seems a less histology-dependent functional imaging test than 18 F-FDG PET/CT and may be the imaging test of choice for staging of indolent NHLs with low 18 F-FDG avidity.

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zhang, Daoqiang; Wang, Yaping; Zhou, Luping; Yuan, Hong; Shen, Dinggang

2011-01-01

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers. PMID:21236349

Time-resolved contrast-enhanced MRA (TWIST) with gadofosveset trisodium in the classification of soft-tissue vascular anomalies in the head and neck in children following updated 2014 ISSVA classification: first report on systematic evaluation of MRI and TWIST in a cohort of 47 children.

PubMed

Higgins, L J; Koshy, J; Mitchell, S E; Weiss, C R; Carson, K A; Huisman, T A G M; Tekes, A

2016-01-01

To evaluate the relative accuracy of contrast-enhanced time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced magnetic resonance imaging (MRI) following International Society for the Study of Vascular Anomalies updated 2014-based classification of soft-tissue vascular anomalies in the head and neck in children. Time-resolved angiography with interleaved stochastic trajectories versus conventional contrast-enhanced MRI of children with diagnosis of soft-tissue vascular anomalies in the head and neck referred for MRI between 2008 and 2014 were retrospectively reviewed. Forty-seven children (0-18 years) were evaluated. Two paediatric neuroradiologists evaluated time-resolved MRA and conventional MRI in two different sessions (30 days apart). Blood-pool endovascular MRI contrast agent gadofosveset trisodium was used. The present cohort had the following diagnoses: infantile haemangioma (n=6), venous malformation (VM; n=23), lymphatic malformation (LM; n=16), arteriovenous malformation (AVM; n=2). Time-resolved MRA alone accurately classified 38/47 (81%) and conventional MRI 42/47 (89%), respectively. Although time-resolved MRA alone is slightly superior to conventional MRI alone for diagnosis of infantile haemangioma, conventional MRI is slightly better for diagnosis of venous and LMs. Neither time-resolved MRA nor conventional MRI was sufficient for accurate diagnosis of AVM in this cohort. Conventional MRI combined with time-resolved MRA accurately classified 44/47 cases (94%). Time-resolved MRA using gadofosveset trisodium can accurately classify soft-tissue vascular anomalies in the head and neck in children. The addition of time-resolved MRA to existing conventional MRI protocols provides haemodynamic information, assisting the diagnosis of vascular anomalies in the paediatric population at one-third of the dose of other MRI contrast agents. Copyright © 2015 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

MRI in local staging of rectal cancer: an update

PubMed Central

Tapan, Ümit; Özbayrak, Mustafa; Tatlı, Servet

2014-01-01

Preoperative imaging for staging of rectal cancer has become an important aspect of current approach to rectal cancer management, because it helps to select suitable patients for neoadjuvant chemoradiotherapy and determine the appropriate surgical technique. Imaging modalities such as endoscopic ultrasonography, computed tomography, and magnetic resonance imaging (MRI) play an important role in assessing the depth of tumor penetration, lymph node involvement, mesorectal fascia and anal sphincter invasion, and presence of distant metastatic diseases. Currently, there is no consensus on a preferred imaging technique for preoperative staging of rectal cancer. However, high-resolution phased-array MRI is recommended as a standard imaging modality for preoperative local staging of rectal cancer, with excellent soft tissue contrast, multiplanar capability, and absence of ionizing radiation. This review will mainly focus on the role of MRI in preoperative local staging of rectal cancer and discuss recent advancements in MRI technique such as diffusion-weighted imaging and dynamic contrast-enhanced MRI. PMID:25010367

Posterior medial meniscus root ligament lesions: MRI classification and associated findings.

PubMed

Choi, Ja-Young; Chang, Eric Y; Cunha, Guilherme M; Tafur, Monica; Statum, Sheronda; Chung, Christine B

2014-12-01

The purposes of this study were to determine the prevalence of altered MRI appearances of "posterior medial meniscus root ligament (PMMRL)" lesions, introduce a classification of lesion types, and report associated findings. We retrospectively reviewed 419 knee MRI studies to identify the presence of PMMRL lesions. Classification was established on the basis of lesions encountered. The medial compartment was assessed for medial meniscal tears in the meniscus proper, medial meniscal extrusion, insertional PMMRL osseous changes, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament abnormality. PMMRL abnormalities occurred in 28.6% (120/419) of the studies: degeneration, 14.3% (60/419) and tear, 14.3% (60/419). Our classification system included degeneration and tearing. Tearing was categorized as partial or complete with delineation of the point of failure as entheseal, midsubstance, or junction to meniscus. Of all tears, 93.3% (56/60) occurred at the meniscal junction. Univariate analysis revealed significant differences between the knees with and without PMMRL lesions in age, medial meniscal tear, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture (p=0.017), and cruciate ligament degeneration (p<0.001). PMMRL lesions are commonly detected in symptomatic patients. We have introduced an MRI classification system. PMMRL lesions are significantly associated with age, medial meniscal tears, medial meniscal extrusion, insertional PMMRL osseous change, regional synovitis, osteoarthritis, insufficiency fracture, and cruciate ligament degeneration.

Choosing the Rules: Distinct and Overlapping Frontoparietal Representations of Task Rules for Perceptual Decisions

PubMed Central

Kriegeskorte, Nikolaus; Carlin, Johan D.; Rowe, James B.

2013-01-01

Behavior is governed by rules that associate stimuli with responses and outcomes. Human and monkey studies have shown that rule-specific information is widely represented in the frontoparietal cortex. However, it is not known how establishing a rule under different contexts affects its neural representation. Here, we use event-related functional MRI (fMRI) and multivoxel pattern classification methods to investigate the human brain's mechanisms of establishing and maintaining rules for multiple perceptual decision tasks. Rules were either chosen by participants or specifically instructed to them, and the fMRI activation patterns representing rule-specific information were compared between these contexts. We show that frontoparietal regions differ in the properties of their rule representations during active maintenance before execution. First, rule-specific information maintained in the dorsolateral and medial frontal cortex depends on the context in which it was established (chosen vs specified). Second, rule representations maintained in the ventrolateral frontal and parietal cortex are independent of the context in which they were established. Furthermore, we found that the rule-specific coding maintained in anticipation of stimuli may change with execution of the rule: representations in context-independent regions remain invariant from maintenance to execution stages, whereas rule representations in context-dependent regions do not generalize to execution stage. The identification of distinct frontoparietal systems with context-independent and context-dependent task rule representations, and the distinction between anticipatory and executive rule representations, provide new insights into the functional architecture of goal-directed behavior. PMID:23864675

High-resolution 3 T MRI of traumatic and degenerative triangular fibrocartilage complex (TFCC) abnormalities using Palmer and Outerbridge classifications.

PubMed

Nozaki, T; Rafijah, G; Yang, L; Ueno, T; Horiuchi, S; Hitt, D; Yoshioka, H

2017-10-01

To investigate the usefulness of high-resolution 3 T magnetic resonance imaging (MRI) for the evaluation of traumatic and degenerative triangular fibrocartilage complex (TFCC) abnormalities among three groups: patients presenting with wrist pain who were (a) younger than age 50 years or (b) age 50 or older (PT<50 and PT≥50, respectively), and (c) asymptomatic controls who were younger than age 50 years (AC). High-resolution 3 T MRI was evaluated retrospectively in 96 patients, including 47 PT<50, 38 PT≥50, and 11 AC. Two board-certified radiologists reviewed the MRI images independently. MRI features of TFCC injury were analysed according to the Palmer classification, and cartilage degeneration around the TFCC was evaluated using the Outerbridge classification. Differences in MRI findings among these groups were detected using chi-square test. Cohen's kappa was calculated to assess interobserver and intra-observer reliability. The incidence of Palmer class 1A, 1C and 1D traumatic TFCC injury was significantly (p<0.05) higher in PT≥50 than in PT<50 (class 1A: 47.4% versus 27.7%, class 1C: 31.6% versus 12.8%, and class 1D: 21.1% versus 2.1%). Likewise, MRI findings of TFCC degeneration were observed more frequently in PT≥50 than in PT<50 (p<0.01). Outerbridge grade 2 or higher cartilage degeneration was significantly (p<0.01) more frequently seen in PT≥50 than in PT<50 (55.3% versus 17% in the lunate, 28.9% versus 4.3% in the triquetrum, 73.7% versus 12.8% in the ulna). High-resolution wrist MRI at 3 T enables detailed evaluation of TFCC traumatic injury and degenerative changes using the Palmer and Outerbridge classifications, with good or excellent interobserver and intra-observer reliability. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Comparison of Pelvic Phased-Array versus Endorectal Coil Magnetic Resonance Imaging at 3 Tesla for Local Staging of Prostate Cancer

PubMed Central

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun

2012-01-01

Purpose Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Materials and Methods Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Results Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Conclusion Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI. PMID:22476999

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

Investigation of the Relationship between Anterior Knee Pain and Chondromalacia Patellae and Patellofemoral Malalignment.

PubMed

Aysin, Idil Kurut; Askin, Ayhan; Mete, Berna Dirim; Guvendi, Ece; Aysin, Murat; Kocyigit, Hikmet

2018-02-01

The study aimed to investigate whether there is any association of anterior knee pain and knee function with chondromalacia stage and patellofemoral alignment in patients with anterior knee pain for over a month and with chondromalacia patellae (CMP) detected by magnetic resonance imaging (MRI). We reviewed the medical records of 38 patients who underwent a knee MRI examination and were diagnosed with chondromalacia based on the MRI. Knee MRI images were evaluated by a radiologist for chondromalacia staging. Patients were divided into two groups as early stage (stage 1-2) and advanced stage (stage 3-4) chondromalacia. Patients' demographical data (age, sex, and occupation), clinical features, physical examination findings and patellofemoral pain severity scale, kujala patellofemoral scoring system, and functional index questionnaire scores were obtained from their medical records. Trochlear sulcus angle, sulcus depth, lateral patellofemoral angle, patellar translation, and Insall-Salvati index were measured using the MRI images. The mean patient age was higher in the advanced stage CMP group compared to the early stage CMP group (p=0.038). There was no statistically significant difference regarding other demographical data (p>0.05). MRI measurement parameters did not show difference between the groups (p>0.05). Patients in the advanced stage CMP group had higher patellofemoral pain severity score, lower kujala patellofemoral score, and lower functional index questionnaire score compared to the early stage CMP group. The differences were statistically significant (p=0.008, p=0.012, and p=0.026, respectively). As chondromalacia stage advances, the symptom severity worsens and knee functions decline; however, MRI measurements do not show difference between early and advanced stage CMP patients.

Investigation of the Relationship between Anterior Knee Pain and Chondromalacia Patellae and Patellofemoral Malalignment

PubMed Central

Aysin, Idil Kurut; Askin, Ayhan; Mete, Berna Dirim; Guvendi, Ece; Aysin, Murat; Kocyigit, Hikmet

2018-01-01

Objective: The study aimed to investigate whether there is any association of anterior knee pain and knee function with chondromalacia stage and patellofemoral alignment in patients with anterior knee pain for over a month and with chondromalacia patellae (CMP) detected by magnetic resonance imaging (MRI). Materials and Methods: We reviewed the medical records of 38 patients who underwent a knee MRI examination and were diagnosed with chondromalacia based on the MRI. Knee MRI images were evaluated by a radiologist for chondromalacia staging. Patients were divided into two groups as early stage (stage 1–2) and advanced stage (stage 3–4) chondromalacia. Patients’ demographical data (age, sex, and occupation), clinical features, physical examination findings and patellofemoral pain severity scale, kujala patellofemoral scoring system, and functional index questionnaire scores were obtained from their medical records. Trochlear sulcus angle, sulcus depth, lateral patellofemoral angle, patellar translation, and Insall–Salvati index were measured using the MRI images. Results: The mean patient age was higher in the advanced stage CMP group compared to the early stage CMP group (p=0.038). There was no statistically significant difference regarding other demographical data (p>0.05). MRI measurement parameters did not show difference between the groups (p>0.05). Patients in the advanced stage CMP group had higher patellofemoral pain severity score, lower kujala patellofemoral score, and lower functional index questionnaire score compared to the early stage CMP group. The differences were statistically significant (p=0.008, p=0.012, and p=0.026, respectively). Conclusion: As chondromalacia stage advances, the symptom severity worsens and knee functions decline; however, MRI measurements do not show difference between early and advanced stage CMP patients. PMID:29531488

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

PubMed Central

Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2013-01-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts. PMID:24236224

Automatic classification of scar tissue in late gadolinium enhancement cardiac MRI for the assessment of left-atrial wall injury after radiofrequency ablation

NASA Astrophysics Data System (ADS)

Perry, Daniel; Morris, Alan; Burgon, Nathan; McGann, Christopher; MacLeod, Robert; Cates, Joshua

2012-03-01

Radiofrequency ablation is a promising procedure for treating atrial fibrillation (AF) that relies on accurate lesion delivery in the left atrial (LA) wall for success. Late Gadolinium Enhancement MRI (LGE MRI) at three months post-ablation has proven effective for noninvasive assessment of the location and extent of scar formation, which are important factors for predicting patient outcome and planning of redo ablation procedures. We have developed an algorithm for automatic classification in LGE MRI of scar tissue in the LA wall and have evaluated accuracy and consistency compared to manual scar classifications by expert observers. Our approach clusters voxels based on normalized intensity and was chosen through a systematic comparison of the performance of multivariate clustering on many combinations of image texture. Algorithm performance was determined by overlap with ground truth, using multiple overlap measures, and the accuracy of the estimation of the total amount of scar in the LA. Ground truth was determined using the STAPLE algorithm, which produces a probabilistic estimate of the true scar classification from multiple expert manual segmentations. Evaluation of the ground truth data set was based on both inter- and intra-observer agreement, with variation among expert classifiers indicating the difficulty of scar classification for a given a dataset. Our proposed automatic scar classification algorithm performs well for both scar localization and estimation of scar volume: for ground truth datasets considered easy, variability from the ground truth was low; for those considered difficult, variability from ground truth was on par with the variability across experts.

Automatic Denoising of Functional MRI Data: Combining Independent Component Analysis and Hierarchical Fusion of Classifiers

PubMed Central

Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F; Glasser, Matthew F; Griffanti, Ludovica; Smith, Stephen M

2014-01-01

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject “at rest”). ICA decomposes fMRI data into patterns of activity (a set of spatial maps and their corresponding time series) that are statistically independent and add linearly to explain voxel-wise time series. Given the set of ICA components, if the components representing “signal” (brain activity) can be distinguished form the “noise” components (effects of motion, non-neuronal physiology, scanner artefacts and other nuisance sources), the latter can then be removed from the data, providing an effective cleanup of structured noise. Manual classification of components is labour intensive and requires expertise; hence, a fully automatic noise detection algorithm that can reliably detect various types of noise sources (in both task and resting fMRI) is desirable. In this paper, we introduce FIX (“FMRIB’s ICA-based X-noiseifier”), which provides an automatic solution for denoising fMRI data via accurate classification of ICA components. For each ICA component FIX generates a large number of distinct spatial and temporal features, each describing a different aspect of the data (e.g., what proportion of temporal fluctuations are at high frequencies). The set of features is then fed into a multi-level classifier (built around several different Classifiers). Once trained through the hand-classification of a sufficient number of training datasets, the classifier can then automatically classify new datasets. The noise components can then be subtracted from (or regressed out of) the original data, to provide automated cleanup. On conventional resting-state fMRI (rfMRI) single-run datasets, FIX achieved about 95% overall accuracy. On high-quality rfMRI data from the Human Connectome Project, FIX achieves over 99% classification accuracy, and as a result is being used in the default rfMRI processing pipeline for generating HCP connectomes. FIX is publicly available as a plugin for FSL. PMID:24389422

Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Elliott, R.; Shih, N.; Feldman, M.; Barentsz, J. O.; Hulsbergen-van de Kaa, C. A.; Kovacs, I.; Huisman, H. J.; Madabhushi, A.

2014-03-01

Learning how to separate benign confounders from prostate cancer is important because the imaging characteristics of these confounders are poorly understood. Furthermore, the typical representations of the MRI parameters might not be enough to allow discrimination. The diagnostic uncertainty this causes leads to a lower diagnostic accuracy. In this paper a new cascaded classifier is introduced to separate prostate cancer and benign confounders on MRI in conjunction with specific computer-extracted features to distinguish each of the benign classes (benign prostatic hyperplasia (BPH), inflammation, atrophy or prostatic intra-epithelial neoplasia (PIN). In this study we tried to (1) calculate different mathematical representations of the MRI parameters which more clearly express subtle differences between different classes, (2) learn which of the MRI image features will allow to distinguish specific benign confounders from prostate cancer, and (2) find the combination of computer-extracted MRI features to best discriminate cancer from the confounding classes using a cascaded classifier. One of the most important requirements for identifying MRI signatures for adenocarcinoma, BPH, atrophy, inflammation, and PIN is accurate mapping of the location and spatial extent of the confounder and cancer categories from ex vivo histopathology to MRI. Towards this end we employed an annotated prostatectomy data set of 31 patients, all of whom underwent a multi-parametric 3 Tesla MRI prior to radical prostatectomy. The prostatectomy slides were carefully co-registered to the corresponding MRI slices using an elastic registration technique. We extracted texture features from the T2-weighted imaging, pharmacokinetic features from the dynamic contrast enhanced imaging and diffusion features from the diffusion-weighted imaging for each of the confounder classes and prostate cancer. These features were selected because they form the mainstay of clinical diagnosis. Relevant features for each of the classes were selected using maximum relevance minimum redundancy feature selection, allowing us to perform classifier independent feature selection. The selected features were then incorporated in a cascading classifier, which can focus on easier sub-tasks at each stage, leaving the more difficult classification tasks for later stages. Results show that distinct features are relevant for each of the benign classes, for example the fraction of extra-vascular, extra-cellular space in a voxel is a clear discriminator for inflammation. Furthermore, the cascaded classifier outperforms both multi-class and one-shot classifiers in overall accuracy for discriminating confounders from cancer: 0.76 versus 0.71 and 0.62.

A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear.

PubMed

Zarandi, M H Fazel; Khadangi, A; Karimi, F; Turksen, I B

2016-12-01

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. λ-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "η" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

PET/MRI of metabolic activity in osteoarthritis: A feasibility study.

PubMed

Kogan, Feliks; Fan, Audrey P; McWalter, Emily J; Oei, Edwin H G; Quon, Andrew; Gold, Garry E

2017-06-01

To evaluate positron emission tomography / magnetic resonance imaging (PET/MRI) knee imaging to detect and characterize osseous metabolic abnormalities and correlate PET radiotracer uptake with osseous abnormalities and cartilage degeneration observed on MRI. Both knees of 22 subjects with knee pain or injury were scanned at one timepoint, without gadolinium, on a hybrid 3.0T PET-MRI system following injection of 18 F-fluoride or 18 F-fluorodeoxyglucose (FDG). A musculoskeletal radiologist identified volumes of interest (VOIs) around bone abnormalities on MR images and scored bone marrow lesions (BMLs) and osteophytes using a MOAKS scoring system. Cartilage appearance adjacent to bone abnormalities was graded with MRI-modified Outerbridge classifications. On PET standardized uptake values (SUV) maps, VOIs with SUV greater than 5 times the SUV in normal-appearing bone were identified as high-uptake VOI (VOI High ). Differences in 18 F-fluoride uptake between bone abnormalities, BML, and osteophyte grades and adjacent cartilage grades on MRI were identified using Mann-Whitney U-tests. SUV max in all subchondral bone lesions (BML, osteophytes, sclerosis) was significantly higher than that of normal-appearing bone on MRI (P < 0.001 for all). Of the 172 high-uptake regions on 18 F-fluoride PET, 63 (37%) corresponded to normal-appearing subchondral bone on MRI. Furthermore, many small grade 1 osteophytes (40 of 82 [49%]), often described as the earliest signs of osteoarthritis (OA), did not show high uptake. Lastly, PET SUV max in subchondral bone adjacent to grade 0 cartilage was significantly lower compared to that of grades 1-2 (P < 0.05) and grades 3-4 cartilage (P < 0.001). PET/MRI can simultaneously assess multiple early metabolic and morphologic markers of knee OA across multiple tissues in the joint. Our findings suggest that PET/MR may detect metabolic abnormalities in subchondral bone, which appear normal on MRI. 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;45:1736-1745. © 2016 International Society for Magnetic Resonance in Medicine.

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

Pötter, Richard; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna, Vienna; Federico, Mario

Purpose: To define, in the setting of cervical cancer, to what extent information from additional pretreatment magnetic resonance imaging (MRI) without the brachytherapy applicator improves conformity of CT-based high-risk clinical target volume (CTV{sub HR}) contours, compared with the MRI for various tumor stages (International Federation of Gynecology and Obstetrics [FIGO] stages I-IVA). Methods and Materials: The CTV{sub HR} was contoured in 39 patients with cervical cancer (FIGO stages I-IVA) (1) on CT images based on clinical information (CTV{sub HR}-CT{sub Clinical}) alone; and (2) using an additional MRI before brachytherapy, without the applicator (CTV{sub HR}-CT{sub pre-BT} {sub MRI}). The CT contours were compared withmore » reference contours on MRI with the applicator in place (CTV{sub HR}-MRI{sub ref}). Width, height, thickness, volumes, and topography were analyzed. Results: The CT-MRI{sub ref} differences hardly varied in stage I tumors (n=8). In limited-volume stage IIB and IIIB tumors (n=19), CTV{sub HR}-CT{sub pre-BT} {sub MRI}–MRI{sub ref} volume differences (2.6 cm{sup 3} [IIB], 7.3 cm{sup 3} [IIIB]) were superior to CTV{sub HR}-CT{sub Clinical}–MRI{sub ref} (11.8 cm{sup 3} [IIB], 22.9 cm{sup 3} [IIIB]), owing to significant improvement of height and width (P<.05). In advanced disease (n=12), improved agreement with MR volume, width, and height was achieved for CTV{sub HR}-CT{sub pre-BT} {sub MRI}. In 5 of 12 cases, MRI{sub ref} contours were partly missed on CT. Conclusions: Pre-BT MRI helps to define CTV{sub HR} before BT implantation appropriately, if only CT images with the applicator in place are available for BT planning. Significant improvement is achievable in limited-volume stage IIB and IIIB tumors. In more advanced disease (extensive IIB to IVA), improvement of conformity is possible but may be associated with geographic misses. Limited impact on precision of CTV{sub HR}-CT is expected in stage IB tumors.« less

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Frontoparietal Priority Maps as Biomarkers for mTBI

DTIC Science & Technology

2015-10-01

preliminary data analysis is underway. 15. SUBJECT TERMS mTBI, fMRI , DTI, psychophysics, vision, convergence insufficiency 16. SECURITY CLASSIFICATION OF...biomarkers and behavioral measures of visual performance in veterans who have and have not experienced mTBI. KEYWORDS mTBI fMRI DTI psychophysics...MRI protocol prepared 8 Delayed to month 15 due to recruitment delays. Major Task 5: acquire MRI measures, which include DTI and fMRI Complete

Fast and effective characterization of 3D region of interest in medical image data

NASA Astrophysics Data System (ADS)

Kontos, Despina; Megalooikonomou, Vasileios

2004-05-01

We propose a framework for detecting, characterizing and classifying spatial Regions of Interest (ROIs) in medical images, such as tumors and lesions in MRI or activation regions in fMRI. A necessary step prior to classification is efficient extraction of discriminative features. For this purpose, we apply a characterization technique especially designed for spatial ROIs. The main idea of this technique is to extract a k-dimensional feature vector using concentric spheres in 3D (or circles in 2D) radiating out of the ROI's center of mass. These vectors form characterization signatures that can be used to represent the initial ROIs. We focus on classifying fMRI ROIs obtained from a study that explores neuroanatomical correlates of semantic processing in Alzheimer's disease (AD). We detect a ROI highly associated with AD and apply the feature extraction technique with different experimental settings. We seek to distinguish control from patient samples. We study how classification can be performed using the extracted signatures as well as how different experimental parameters affect classification accuracy. The obtained classification accuracy ranged from 82% to 87% (based on the selected ROI) suggesting that the proposed classification framework can be potentially useful in supporting medical decision-making.

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas.

PubMed

Zheng, Weili; Ackley, Elena S; Martínez-Ramón, Manel; Posse, Stefan

2013-02-01

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation. Copyright © 2013 Elsevier Inc. All rights reserved.

Systematic review and meta-analysis of the accuracy of MRI and endorectal ultrasound in the restaging and response assessment of rectal cancer following neoadjuvant therapy.

PubMed

Memon, S; Lynch, A C; Bressel, M; Wise, A G; Heriot, A G

2015-09-01

Restaging imaging by MRI or endorectal ultrasound (ERUS) following neoadjuvant chemoradiotherapy is not routinely performed, but the assessment of response is becoming increasingly important to facilitate individualization of management. A search of the MEDLINE and Scopus databases was performed for studies that evaluated the accuracy of restaging of rectal cancer following neoadjuvant chemoradiotherapy with MRI or ERUS against the histopathological outcome. A systematic review of selected studies was performed. The methodological quality of studies that qualified for meta-analysis was critically assessed to identify studies suitable for inclusion in the meta-analysis. Sixty-three articles were included in the systematic review. Twelve restaging MRI studies and 18 restaging ERUS studies were eligible for meta-analysis of T-stage restaging accuracy. Overall, ERUS T-stage restaging accuracy (mean [95% CI]: 65% [56-72%]) was nonsignificantly higher than MRI T-stage accuracy (52% [44-59%]). Restaging MRI is accurate at excluding circumferential resection margin involvement. Restaging MRI and ERUS were equivalent for prediction of nodal status: the accuracy of both investigations was 72% with over-staging and under-staging occurring in 10-15%. The heterogeneity amongst restaging studies is high, limiting conclusive findings regarding their accuracies. The accuracy of restaging imaging is different for different pathological T stages and highest for T3 tumours. Morphological assessment of T- or N-stage by MRI or ERUS is currently not accurate or consistent enough for clinical application. Restaging MRI appears to have a role in excluding circumferential resection margin involvement. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway.

PubMed

Mohsenzadeh, Yalda; Qin, Sheng; Cichy, Radoslaw M; Pantazis, Dimitrios

2018-06-21

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions. © 2018, Mohsenzadeh et al.

Neural and Behavioral Sequelae of Blast-Related Traumatic Brain Injury

DTIC Science & Technology

2012-11-01

testing and advanced MRI techniques [task-activated functional MRI (fMRI) and diffusion tensor imaging ( DTI )] to gain a comprehensive understanding of... DTI fiber tracking) and neurobehavioral testing (computerized assessment and standard neuropsychological testing) on 60 chronic trauma patients: 15...data analysis. 15. SUBJECT TERMS Blast-related traumatic brain injury (TBI), fMRI, DTI , cognition 16. SECURITY CLASSIFICATION OF: 17. LIMITATION

Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks.

PubMed

Dvornek, Nicha C; Ventola, Pamela; Pelphrey, Kevin A; Duncan, James S

2017-09-01

Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series. We used the entire large, multi-site Autism Brain Imaging Data Exchange (ABIDE) I dataset for training and testing the LSTM models. Under a cross-validation framework, we achieved classification accuracy of 68.5%, which is 9% higher than previously reported methods that used fMRI data from the whole ABIDE cohort. Finally, we presented interpretation of the trained LSTM weights, which highlight potential functional networks and regions that are known to be implicated in ASD.

Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks

PubMed Central

Dvornek, Nicha C.; Ventola, Pamela; Pelphrey, Kevin A.; Duncan, James S.

2017-01-01

Functional magnetic resonance imaging (fMRI) has helped characterize the pathophysiology of autism spectrum disorders (ASD) and carries promise for producing objective biomarkers for ASD. Recent work has focused on deriving ASD biomarkers from resting-state functional connectivity measures. However, current efforts that have identified ASD with high accuracy were limited to homogeneous, small datasets, while classification results for heterogeneous, multi-site data have shown much lower accuracy. In this paper, we propose the use of recurrent neural networks with long short-term memory (LSTMs) for classification of individuals with ASD and typical controls directly from the resting-state fMRI time-series. We used the entire large, multi-site Autism Brain Imaging Data Exchange (ABIDE) I dataset for training and testing the LSTM models. Under a cross-validation framework, we achieved classification accuracy of 68.5%, which is 9% higher than previously reported methods that used fMRI data from the whole ABIDE cohort. Finally, we presented interpretation of the trained LSTM weights, which highlight potential functional networks and regions that are known to be implicated in ASD. PMID:29104967

Assessment of sexual orientation using the hemodynamic brain response to visual sexual stimuli.

PubMed

Ponseti, Jorge; Granert, Oliver; Jansen, Olav; Wolff, Stephan; Mehdorn, Hubertus; Bosinski, Hartmut; Siebner, Hartwig

2009-06-01

The assessment of sexual orientation is of importance to the diagnosis and treatment of sex offenders and paraphilic disorders. Phallometry is considered gold standard in objectifying sexual orientation, yet this measurement has been criticized because of its intrusiveness and limited reliability. To evaluate whether the spatial response pattern to sexual stimuli as revealed by a change in blood oxygen level-dependent (BOLD) signal can be used for individual classification of sexual orientation. We used a preexisting functional MRI (fMRI) data set that had been acquired in a nonclinical sample of 12 heterosexual men and 14 homosexual men. During fMRI, participants were briefly exposed to pictures of same-sex and opposite-sex genitals. Data analysis involved four steps: (i) differences in the BOLD response to female and male sexual stimuli were calculated for each subject; (ii) these contrast images were entered into a group analysis to calculate whole-brain difference maps between homosexual and heterosexual participants; (iii) a single expression value was computed for each subject expressing its correspondence to the group result; and (iv) based on these expression values, Fisher's linear discriminant analysis and the kappa-nearest neighbor classification method were used to predict the sexual orientation of each subject. Sensitivity and specificity of the two classification methods in predicting individual sexual orientation. Both classification methods performed well in predicting individual sexual orientation with a mean accuracy of >85% (Fisher's linear discriminant analysis: 92% sensitivity, 85% specificity; kappa-nearest neighbor classification: 88% sensitivity, 92% specificity). Despite the small sample size, the functional response patterns of the brain to sexual stimuli contained sufficient information to predict individual sexual orientation with high accuracy. These results suggest that fMRI-based classification methods hold promise for the diagnosis of paraphilic disorders (e.g., pedophilia).

Primary vaginal cancer: role of MRI in diagnosis, staging and treatment

PubMed Central

Sunil, J; Klopp, A H; Devine, C E; Sagebiel, T; Viswanathan, C; Bhosale, P R

2015-01-01

Primary carcinoma of the vagina is rare, accounting for 1–3% of all gynaecological malignancies. MRI has an increasing role in diagnosis, staging, treatment and assessment of complications in gynaecologic malignancy. In this review, we illustrate the utility of MRI in patients with primary vaginal cancer and highlight key aspects of staging, treatment, recurrence and complications. PMID:25966291

Comparison of Contrast-Enhanced Ultrasound and Gadolinium-Ethoxybenzyl-Diethylenetriamine Pentaacetic Acid-Enhanced MRI for the Diagnosis of Macroscopic Type of Hepatocellular Carcinoma.

PubMed

Iwamoto, Takayuki; Imai, Yasuharu; Kogita, Sachiyo; Igura, Takumi; Sawai, Yoshiyuki; Fukuda, Kazuto; Yamaguchi, Yoshitaka; Matsumoto, Yasushi; Nakahara, Masanori; Morimoto, Osakuni; Seki, Yasushi; Ohashi, Hiroshi; Fujita, Norihiko; Kudo, Masatoshi; Takehara, Tetsuo

We compared the efficacy of contrast-enhanced ultrasound sonography (CEUS) with sonazoid and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI for the assessment of macroscopic classification of nodular hepatocellular carcinoma (HCC). Seventy-seven consecutive patients with 79 surgically resected HCCs who underwent both preoperative CEUS and Gd-EOB-DTPA-enhanced MRI were enrolled in this retrospective study. Based on the macroscopic diagnosis of resected specimens, nodules were categorized into the simple nodular (SN) and non-SN type HCC. Two hepatologists independently assessed image datasets of the post-vascular phase of CEUS and hepatobiliary phase of Gd-EOB-DTPA-enhanced MRI to compare their diagnostic performance. Gd-EOB-DTPA-enhanced MRI enabled the evaluation of macroscopic classification in a significantly larger number of nodules than CEUS (78/79 (98.7%) vs. 70/79 (88.6%), p < 0.05). Of 70 nodules that could be evaluated by both modalities, 41 and 29 nodules were pathologically categorized as SN and non-SN, respectively. The areas under the receiver operating characteristic curve (AUC) for non-SN did not differ between CEUS and Gd-EOB-DTPA-enhanced MRI (reader 1: 0.748 for CEUS, 0.808 for MRI; reader 2: 0.759 for CEUS, 0.787 for MRI). The AUC of combined CEUS and Gd-EOB-DTPA-enhanced MRI for SN HCC was 0.855 (reader 1) and 0.824 (reader 2), indicating higher AUC values for the combined modalities. The diagnostic performance for macroscopic classification of nodular HCC of CEUS was comparable with that of Gd-EOB-DTPA-enhanced MRI, although some HCCs could not be evaluated by CEUS owing to lower detectability. The combination of the 2 modalities had a more accurate diagnostic performance. © 2016 S. Karger AG, Basel.

Use of Magnetic Resonance Imaging as Well as Clinical Disease Activity in the Clinical Classification of Multiple Sclerosis and Assessment of Its Course

PubMed Central

Dhib-Jalbut, Suhayl; Dowling, Peter; Durelli, Luca; Ford, Corey; Giovannoni, Gavin; Halper, June; Harris, Colleen; Herbert, Joseph; Li, David; Lincoln, John A.; Lisak, Robert; Lublin, Fred D.; Lucchinetti, Claudia F.; Moore, Wayne; Naismith, Robert T.; Oehninger, Carlos; Simon, Jack; Sormani, Maria Pia

2012-01-01

It has recently been suggested that the Lublin-Reingold clinical classification of multiple sclerosis (MS) be modified to include the use of magnetic resonance imaging (MRI). An international consensus conference sponsored by the Consortium of Multiple Sclerosis Centers (CMSC) was held from March 5 to 7, 2010, to review the available evidence on the need for such modification of the Lublin-Reingold criteria and whether the addition of MRI or other biomarkers might lead to a better understanding of MS pathophysiology and disease course over time. The conference participants concluded that evidence of new MRI gadolinium-enhancing (Gd+) T1-weighted lesions and unequivocally new or enlarging T2-weighted lesions (subclinical activity, subclinical relapses) should be added to the clinical classification of MS in distinguishing relapsing inflammatory from progressive forms of the disease. The consensus was that these changes to the classification system would provide more rigorous definitions and categorization of MS course, leading to better insights as to the evolution and treatment of MS. PMID:24453741

Normalization of T2W-MRI prostate images using Rician a priori

NASA Astrophysics Data System (ADS)

Lemaître, Guillaume; Rastgoo, Mojdeh; Massich, Joan; Vilanova, Joan C.; Walker, Paul M.; Freixenet, Jordi; Meyer-Baese, Anke; Mériaudeau, Fabrice; Martí, Robert

2016-03-01

Prostate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods.

Noninvasive Assessment of Biochemical and Mechanical Properties of Lumbar Discs Through Quantitative Magnetic Resonance Imaging in Asymptomatic Volunteers.

PubMed

Foltz, Mary H; Kage, Craig C; Johnson, Casey P; Ellingson, Arin M

2017-11-01

Intervertebral disc degeneration is a prevalent phenomenon associated with back pain. It is of critical clinical interest to discriminate disc health and identify early stages of degeneration. Traditional clinical T2-weighted magnetic resonance imaging (MRI), assessed using the Pfirrmann classification system, is subjective and fails to adequately capture initial degenerative changes. Emerging quantitative MRI techniques offer a solution. Specifically, T2* mapping images water mobility in the macromolecular network, and our preliminary ex vivo work shows high predictability of the disc's glycosaminoglycan content (s-GAG) and residual mechanics. The present study expands upon this work to predict the biochemical and biomechanical properties in vivo and assess their relationship with both age and Pfirrmann grade. Eleven asymptomatic subjects (range: 18-62 yrs) were enrolled and imaged using a 3T MRI scanner. T2-weighted images (Pfirrmann grade) and quantitative T2* maps (predict s-GAG and residual stress) were acquired. Surface maps based on the distribution of these properties were generated and integrated to quantify the surface volume. Correlational analyses were conducted to establish the relationship between each metric of disc health derived from the quantitative T2* maps with both age and Pfirrmann grade, where an inverse trend was observed. Furthermore, the nucleus pulposus (NP) signal in conjunction with volumetric surface maps provided the ability to discern differences during initial stages of disc degeneration. This study highlights the ability of T2* mapping to noninvasively assess the s-GAG content, residual stress, and distributions throughout the entire disc, which may provide a powerful diagnostic tool for disc health assessment.

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.

Association of medial meniscal extrusion with medial tibial osteophyte distance detected by T2 mapping MRI in patients with early-stage knee osteoarthritis.

PubMed

Hada, Shinnosuke; Ishijima, Muneaki; Kaneko, Haruka; Kinoshita, Mayuko; Liu, Lizu; Sadatsuki, Ryo; Futami, Ippei; Yusup, Anwajan; Takamura, Tomohiro; Arita, Hitoshi; Shiozawa, Jun; Aoki, Takako; Takazawa, Yuji; Ikeda, Hiroshi; Aoki, Shigeki; Kurosawa, Hisashi; Okada, Yasunori; Kaneko, Kazuo

2017-09-12

Medial meniscal extrusion (MME) is associated with progression of medial knee osteoarthritis (OA), but no or little information is available for relationships between MME and osteophytes, which are found in cartilage and bone parts. Because of the limitation in detectability of the cartilage part of osteophytes by radiography or conventional magnetic resonance imaging (MRI), the rate of development and size of osteophytes appear to have been underestimated. Because T2 mapping MRI may enable us to evaluate the cartilage part of osteophytes, we aimed to examine the association between MME and OA-related changes, including osteophytes, by using conventional and T2 mapping MRI. Patients with early-stage knee OA (n = 50) were examined. MRI-detected OA-related changes, in addition to MME, were evaluated according to the Whole-Organ Magnetic Resonance Imaging Score. T2 values of the medial meniscus and osteophytes were measured on T2 mapping images. Osteophytes surgically removed from patients with end-stage knee OA were histologically analyzed and compared with findings derived by radiography and MRI. Medial side osteophytes were detected by T2 mapping MRI in 98% of patients with early-stage knee OA, although the detection rate was 48% by conventional MRI and 40% by radiography. Among the OA-related changes, medial tibial osteophyte distance was most closely associated with MME, as determined by multiple logistic regression analysis, in the patients with early-stage knee OA (β = 0.711, p < 0.001). T2 values of the medial meniscus were directly correlated with MME in patients with early-stage knee OA, who showed ≥ 3 mm of MME (r = 0.58, p = 0.003). The accuracy of osteophyte evaluation by T2 mapping MRI was confirmed by histological analysis of the osteophytes removed from patients with end-stage knee OA. Our study demonstrates that medial tibial osteophyte evaluated by T2 mapping MRI is frequently observed in the patients with early-stage knee OA, showing close association with MME, and that MME is positively correlated with the meniscal degeneration.

Neural and Behavioral Sequelae of Blast-Related Traumatic Brain Injury

DTIC Science & Technology

2012-09-01

fMRI, DTI , cognition 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a...techniques [task-activated functional MRI (fMRI) and diffusion tensor imaging ( DTI )] to gain a comprehensive understanding of the neural changes...orthopedic injuries. We accomplished this goal by conducting advanced neuroimaging (task-activated fMRI and DTI fiber tracking) and neurobehavioral

The clinical outcomes of deep gray matter injury in children with cerebral palsy in relation with brain magnetic resonance imaging.

PubMed

Choi, Ja Young; Choi, Yoon Seong; Rha, Dong-Wook; Park, Eun Sook

2016-08-01

In the present study we investigated the nature and extent of clinical outcomes using various classifications and analyzed the relationship between brain magnetic resonance imaging (MRI) findings and the extent of clinical outcomes in children with cerebral palsy (CP) with deep gray matter injury. The deep gray matter injuries of 69 children were classified into hypoxic ischemic encephalopathy (HIE) and kernicterus patterns. HIE patterns were divided into four groups (I-IV) based on severity. Functional classification was investigated using the gross motor function classification system-expanded and revised, manual ability classification system, communication function classification system, and tests of cognitive function, and other associated problems. The severity of HIE pattern on brain MRI was strongly correlated with the severity of clinical outcomes in these various domains. Children with a kernicterus pattern showed a wide range of clinical outcomes in these areas. Children with severe HIE are at high risk of intellectual disability (ID) or epilepsy and children with a kernicterus pattern are at risk of hearing impairment and/or ID. Grading severity of HIE pattern on brain MRI is useful for predicting overall outcomes. The clinical outcomes of children with a kernicterus pattern range widely from mild to severe. Delineation of the clinical outcomes of children with deep gray matter injury, which are a common abnormal brain MRI finding in children with CP, is necessary. The present study provides clinical outcomes for various domains in children with deep gray matter injury on brain MRI. The deep gray matter injuries were divided into two major groups; HIE and kernicterus patterns. Our study showed that severity of HIE pattern on brain MRI was strongly associated with the severity of impairments in gross motor function, manual ability, communication function, and cognition. These findings suggest that severity of HIE pattern can be useful for predicting the severity of impairments. Conversely, children with a kernicterus pattern showed a wide range of clinical outcomes in various domains. Children with severe HIE pattern are at high risk of ID or epilepsy and children with kernicterus pattern are at risk of hearing impairment or ID. The strength of our study was the assessment of clinical outcomes after 3 years of age using standardized classification systems in various domains in children with deep gray matter injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

PubMed

Rondina, Jane Maryam; Ferreira, Luiz Kobuti; de Souza Duran, Fabio Luis; Kubo, Rodrigo; Ono, Carla Rachel; Leite, Claudia Costa; Smid, Jerusa; Nitrini, Ricardo; Buchpiguel, Carlos Alberto; Busatto, Geraldo F

2018-01-01

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18 F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18 F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18 F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18 F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

Value of Magnetic Resonance Imaging Without or With Applicator in Place for Target Definition in Cervix Cancer Brachytherapy.

PubMed

Pötter, Richard; Federico, Mario; Sturdza, Alina; Fotina, Irina; Hegazy, Neamat; Schmid, Maximilian; Kirisits, Christian; Nesvacil, Nicole

2016-03-01

To define, in the setting of cervical cancer, to what extent information from additional pretreatment magnetic resonance imaging (MRI) without the brachytherapy applicator improves conformity of CT-based high-risk clinical target volume (CTVHR) contours, compared with the MRI for various tumor stages (International Federation of Gynecology and Obstetrics [FIGO] stages I-IVA). The CTVHR was contoured in 39 patients with cervical cancer (FIGO stages I-IVA) (1) on CT images based on clinical information (CTVHR-CTClinical) alone; and (2) using an additional MRI before brachytherapy, without the applicator (CTVHR-CTpre-BT MRI). The CT contours were compared with reference contours on MRI with the applicator in place (CTVHR-MRIref). Width, height, thickness, volumes, and topography were analyzed. The CT-MRIref differences hardly varied in stage I tumors (n=8). In limited-volume stage IIB and IIIB tumors (n=19), CTVHR-CTpre-BT MRI-MRIref volume differences (2.6 cm(3) [IIB], 7.3 cm(3) [IIIB]) were superior to CTVHR-CTClinical-MRIref (11.8 cm(3) [IIB], 22.9 cm(3) [IIIB]), owing to significant improvement of height and width (P<.05). In advanced disease (n=12), improved agreement with MR volume, width, and height was achieved for CTVHR-CTpre-BT MRI. In 5 of 12 cases, MRIref contours were partly missed on CT. Pre-BT MRI helps to define CTVHR before BT implantation appropriately, if only CT images with the applicator in place are available for BT planning. Significant improvement is achievable in limited-volume stage IIB and IIIB tumors. In more advanced disease (extensive IIB to IVA), improvement of conformity is possible but may be associated with geographic misses. Limited impact on precision of CTVHR-CT is expected in stage IB tumors. Copyright © 2016 Elsevier Inc. All rights reserved.

Relationship between preoperative breast MRI and surgical treatment of non-metastatic breast cancer.

PubMed

Onega, Tracy; Weiss, Julie E; Goodrich, Martha E; Zhu, Weiwei; DeMartini, Wendy B; Kerlikowske, Karla; Ozanne, Elissa; Tosteson, Anna N A; Henderson, Louise M; Buist, Diana S M; Wernli, Karen J; Herschorn, Sally D; Hotaling, Elise; O'Donoghue, Cristina; Hubbard, Rebecca

2017-12-01

More extensive surgical treatments for early stage breast cancer are increasing. The patterns of preoperative MRI overall and by stage for this trend has not been well established. Using Breast Cancer Surveillance Consortium registry data from 2010 through 2014, we identified women with an incident non-metastatic breast cancer and determined use of preoperative MRI and initial surgical treatment (mastectomy, with or without contralateral prophylactic mastectomy (CPM), reconstruction, and breast conserving surgery ± radiation). Clinical and sociodemographic covariates were included in multivariable logistic regression models to estimate adjusted odds ratios and 95% confidence intervals. Of the 13 097 women, 2217 (16.9%) had a preoperative MRI. Among the women with MRI, results indicated 32% higher odds of unilateral mastectomy compared to breast conserving surgery and of mastectomy with CPM compared to unilateral mastectomy. Women with preoperative MRI also had 56% higher odds of reconstruction. Preoperative MRI in women with DCIS and early stage invasive breast cancer is associated with more frequent mastectomy, CPM, and reconstruction surgical treatment. Use of more extensive surgical treatment and reconstruction among women with DCIS and early stage invasive cancer whom undergo MRI warrants further investigation. © 2017 Wiley Periodicals, Inc.

Editorial Commentary: Single-Image Slice Magnetic Resonance Imaging Assessments Do Not Predict 3-Dimensional Muscle Volume.

PubMed

Brand, Jefferson C

2016-01-01

No single-image magnetic resonance imaging (MRI) assessment-Goutallier classification, Fuchs classification, or cross-sectional area-is predictive of whole-muscle volume or fatty atrophy of the supraspinatus or infraspinatus. Rather, 3-dimensional MRI measurement of whole-muscle volume and fat-free muscle volume is required and is associated with shoulder strength, which is clinically relevant. Three-dimensional MRI may represent a new gold standard for assessment of the rotator cuff musculature using imaging and may help to predict the feasibility of repair of a rotator cuff tear as well as the postoperative outcome. Unfortunately, 3-dimensional MRI assessment of muscle volume is labor intensive and is not widely available for clinical use. Copyright © 2016 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

PubMed

Schouten, Tijn M; Koini, Marisa; Vos, Frank de; Seiler, Stephan; Rooij, Mark de; Lechner, Anita; Schmidt, Reinhold; Heuvel, Martijn van den; Grond, Jeroen van der; Rombouts, Serge A R B

2017-05-15

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI. Copyright © 2017 Elsevier Inc. All rights reserved.

Multi-channel MRI segmentation with graph cuts using spectral gradient and multidimensional Gaussian mixture model

NASA Astrophysics Data System (ADS)

Lecoeur, Jérémy; Ferré, Jean-Christophe; Collins, D. Louis; Morrisey, Sean P.; Barillot, Christian

2009-02-01

A new segmentation framework is presented taking advantage of multimodal image signature of the different brain tissues (healthy and/or pathological). This is achieved by merging three different modalities of gray-level MRI sequences into a single RGB-like MRI, hence creating a unique 3-dimensional signature for each tissue by utilising the complementary information of each MRI sequence. Using the scale-space spectral gradient operator, we can obtain a spatial gradient robust to intensity inhomogeneity. Even though it is based on psycho-visual color theory, it can be very efficiently applied to the RGB colored images. More over, it is not influenced by the channel assigment of each MRI. Its optimisation by the graph cuts paradigm provides a powerful and accurate tool to segment either healthy or pathological tissues in a short time (average time about ninety seconds for a brain-tissues classification). As it is a semi-automatic method, we run experiments to quantify the amount of seeds needed to perform a correct segmentation (dice similarity score above 0.85). Depending on the different sets of MRI sequences used, this amount of seeds (expressed as a relative number in pourcentage of the number of voxels of the ground truth) is between 6 to 16%. We tested this algorithm on brainweb for validation purpose (healthy tissue classification and MS lesions segmentation) and also on clinical data for tumours and MS lesions dectection and tissues classification.

MRI-guidance in percutaneous core decompression of osteonecrosis of the femoral head.

PubMed

Kerimaa, Pekka; Väänänen, Matti; Ojala, Risto; Hyvönen, Pekka; Lehenkari, Petri; Tervonen, Osmo; Blanco Sequeiros, Roberto

2016-04-01

The purpose of this study was to evaluate the usefulness of MRI-guidance for core decompression of avascular necrosis of the femoral head. Twelve MRI-guided core decompressions were performed on patients with different stages of avascular necrosis of the femoral head. The patients were asked to evaluate their pain and their ability to function before and after the procedure and imaging findings were reviewed respectively. Technical success in reaching the target was 100 % without complications. Mean duration of the procedure itself was 54 min. All patients with ARCO stage 1 osteonecrosis experienced clinical benefit and pathological MRI findings were seen to diminish. Patients with more advanced disease gained less, if any, benefit and total hip arthroplasty was eventually performed on four patients. MRI-guidance seems technically feasible, accurate and safe for core decompression of avascular necrosis of the femoral head. Patients with early stage osteonecrosis may benefit from the procedure. • MRI is a useful guidance method for minimally invasive musculoskeletal interventions. • Bone drilling seems beneficial at early stages of avascular necrosis. • MRI-guidance is safe and accurate for bone drilling.

Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

NASA Astrophysics Data System (ADS)

Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

2014-03-01

A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

Classification of hepatocellular carcinoma stages from free-text clinical and radiology reports

PubMed Central

Yim, Wen-wai; Kwan, Sharon W; Johnson, Guy; Yetisgen, Meliha

2017-01-01

Cancer stage information is important for clinical research. However, they are not always explicitly noted in electronic medical records. In this paper, we present our work on automatic classification of hepatocellular carcinoma (HCC) stages from free-text clinical and radiology notes. To accomplish this, we defined 11 stage parameters used in the three HCC staging systems, American Joint Committee on Cancer (AJCC), Barcelona Clinic Liver Cancer (BCLC), and Cancer of the Liver Italian Program (CLIP). After aggregating stage parameters to the patient-level, the final stage classifications were achieved using an expert-created decision logic. Each stage parameter relevant for staging was extracted using several classification methods, e.g. sentence classification and automatic information structuring, to identify and normalize text as cancer stage parameter values. Stage parameter extraction for the test set performed at 0.81 F1. Cancer stage prediction for AJCC, BCLC, and CLIP stage classifications were 0.55, 0.50, and 0.43 F1.

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

PubMed

Bouts, Mark J R J; Westmoreland, Susan V; de Crespigny, Alex J; Liu, Yutong; Vangel, Mark; Dijkhuizen, Rick M; Wu, Ona; D'Arceuil, Helen E

2015-12-15

Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

Non-contrast magnetic resonance imaging for bladder cancer: fused high b value diffusion-weighted imaging and T2-weighted imaging helps evaluate depth of invasion.

PubMed

Lee, Minsu; Shin, Su-Jin; Oh, Young Taik; Jung, Dae Chul; Cho, Nam Hoon; Choi, Young Deuk; Park, Sung Yoon

2017-09-01

To investigate the utility of fused high b value diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) for evaluating depth of invasion in bladder cancer. We included 62 patients with magnetic resonance imaging (MRI) and surgically confirmed urothelial carcinoma in the urinary bladder. An experienced genitourinary radiologist analysed the depth of invasion (T stage <2 or ≥2) using T2WI, DWI, T2WI plus DWI, and fused DWI and T2WI (fusion MRI). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were investigated. Area under the curve (AUC) was analysed to identify T stage ≥2. The rate of patients with surgically confirmed T stage ≥2 was 41.9% (26/62). Sensitivity, specificity, PPV, NPV and accuracy were 50.0%, 55.6%, 44.8%, 60.6% and 53.2%, respectively, with T2WI; 57.7%, 77.8%, 65.2%, 71.8% and 69.4%, respectively, with DWI; 65.4%, 80.6%, 70.8%, 76.3% and 74.2%, respectively, with T2WI plus DWI and 80.8%, 77.8%, 72.4%, 84.9% and 79.0%, respectively, with fusion MRI. AUC was 0.528 with T2WI, 0.677 with DWI, 0.730 with T2WI plus DWI and 0.793 with fusion MRI for T stage ≥2. Fused high b value DWI and T2WI may be a promising non-contrast MRI technique for assessing depth of invasion in bladder cancer. • Accuracy of fusion MRI was 79.0% for T stage ≥2 in bladder cancer. • AUC of fusion MRI was 0.793 for T stage ≥2 in bladder cancer. • Diagnostic performance of fusion MRI was comparable with T2WI plus DWI. • As a non-contrast MRI technique, fusion MRI is useful for bladder cancer.

Surgical Quality in Rectal Cancer Management: What Can Be Achieved by a Voluntary Observational Study?

PubMed Central

Dziki, Łukasz; Otto, Ronny; Lippert, Hans; Jannasch, Olof

2018-01-01

Purpose Countries with nationwide quality programmes in colorectal cancer report an improved outcome. In Germany, a self-organized and self-financed observational quality assurance project exists, based on voluntary participation. The object of the present study was to ascertain whether this nationwide project also improves the outcome of colorectal cancer. Methods The German Quality Assurance in Colorectal Cancer Project started in 2000 and by 2012 contained 85,000 patients. Inclusion criteria for the study were participation for the entire period of 13 years and treatment of rectal cancer. The following parameters were analysed: (1) patient related: age, gender, ASA classification, T-stage, and N-stage, (2) system related: frequency of preoperative CT and MRI, and (3) outcome related: CRM status, complications, and hospital mortality. Results Forty-one of the 345 hospitals treating 11,597 patients fulfilled the inclusion criteria. The median age increased from 67 to 69 years (p = 0.002). ASA stages III and IV increased from 32.0% to 37.6% (p = 0.005) and from 2.0% to 3.3% (p = 0.022), respectively. The use of CT rose from 67.2% to 88.8% (p < 0.001) and that of MRI from 5.0% to 35.2% (p < 0.001). The proportion of patients suffering from complications decreased from 7.9% to 5.3% (p < 0.001) for intraoperative and from 28.0% to 18.6% (p < 0.001) for postoperative surgical complications, but general postoperative complications increased from 25.8% to 29.5% (p = 0.006). The distribution of histopathological stage, anastomotic leakage, and in-hospital mortality did not change significantly. Conclusion Participation in a quality assurance project improves compliance with treatment standards, especially for diagnostic procedures. An improvement of surgical results will require further investment in training. PMID:29853860

Surgical Quality in Rectal Cancer Management: What Can Be Achieved by a Voluntary Observational Study?

PubMed

Dziki, Łukasz; Otto, Ronny; Lippert, Hans; Mroczkowski, Paweł; Jannasch, Olof

2018-01-01

Countries with nationwide quality programmes in colorectal cancer report an improved outcome. In Germany, a self-organized and self-financed observational quality assurance project exists, based on voluntary participation. The object of the present study was to ascertain whether this nationwide project also improves the outcome of colorectal cancer. The German Quality Assurance in Colorectal Cancer Project started in 2000 and by 2012 contained 85,000 patients. Inclusion criteria for the study were participation for the entire period of 13 years and treatment of rectal cancer. The following parameters were analysed: (1) patient related: age, gender, ASA classification, T-stage, and N-stage, (2) system related: frequency of preoperative CT and MRI, and (3) outcome related: CRM status, complications, and hospital mortality. Forty-one of the 345 hospitals treating 11,597 patients fulfilled the inclusion criteria. The median age increased from 67 to 69 years ( p = 0.002). ASA stages III and IV increased from 32.0% to 37.6% ( p = 0.005) and from 2.0% to 3.3% ( p = 0.022), respectively. The use of CT rose from 67.2% to 88.8% ( p < 0.001) and that of MRI from 5.0% to 35.2% ( p < 0.001). The proportion of patients suffering from complications decreased from 7.9% to 5.3% ( p < 0.001) for intraoperative and from 28.0% to 18.6% ( p < 0.001) for postoperative surgical complications, but general postoperative complications increased from 25.8% to 29.5% ( p = 0.006). The distribution of histopathological stage, anastomotic leakage, and in-hospital mortality did not change significantly. Participation in a quality assurance project improves compliance with treatment standards, especially for diagnostic procedures. An improvement of surgical results will require further investment in training.

Application of magnetic resonance imaging in diagnosis of Uterus Cervical Carcinoma.

PubMed

Peng, Jidong; Wang, Weiqiang; Zeng, Daohui

2017-01-01

Effective treatment of Uterus Cervical Carcinoma (UCC) rely heavily on the precise pre-surgical staging. The conventional International Federation of Gynecology and Obstetrics (FIGO) system based on clinical examination is being applied worldwide for UCC staging. Yet its performance just appears passable. Thus, this study aims to investigate the value of applying Magnetic Resonance Imaging (MRI) with clinical examination in staging of UCC. A retrospective dataset involving 164 patients diagnosed with UCC was enrolled in this study. The mean age of this study population was 46.1 years (range, 28-#x2013;75 years). All patients underwent operations and UCC types were confirmed by pathological examinations. The tumor stages were determined by two experienced Gynecologist independently based on FIGO examinations and MRI. The diagnostic results were also compared with the post-operative pathologic reports. Statistical data analysis on diagnostic performance was then done and reported. The study results showed that the overall accuracy of applying MRI in UCC staging was 82.32%, while using FIGO staging method, the staging accuracy was 59.15%. MRI is suitable to evaluate tumor extent with high accuracy, and it can offer more objective information for the diagnosis and staging of UCC. Compared with clinical examinations based on FIGO, MRI illustrated relatively high accuracy in evaluating UCC staging, and is worthwhile to be recommended in future clinical practice.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects.

PubMed

Dey, Soumyabrata; Rao, A Ravishankar; Shah, Mubarak

2014-01-01

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two reasons. First, it is one of the most commonly found childhood disorders and second, the root cause of the problem is still unknown. Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool for the analysis of ADHD, which is the focus of our current research. In this paper we propose a novel framework for the automatic classification of the ADHD subjects using their resting state fMRI (rs-fMRI) data of the brain. We construct brain functional connectivity networks for all the subjects. The nodes of the network are constructed with clusters of highly active voxels and edges between any pair of nodes represent the correlations between their average fMRI time series. The activity level of the voxels are measured based on the average power of their corresponding fMRI time-series. For each node of the networks, a local descriptor comprising of a set of attributes of the node is computed. Next, the Multi-Dimensional Scaling (MDS) technique is used to project all the subjects from the unknown graph-space to a low dimensional space based on their inter-graph distance measures. Finally, the Support Vector Machine (SVM) classifier is used on the low dimensional projected space for automatic classification of the ADHD subjects. Exhaustive experimental validation of the proposed method is performed using the data set released for the ADHD-200 competition. Our method shows promise as we achieve impressive classification accuracies on the training (70.49%) and test data sets (73.55%). Our results reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.

Can we improve accuracy and reliability of MRI interpretation in children with optic pathway glioma? Proposal for a reproducible imaging classification.

PubMed

Lambron, Julien; Rakotonjanahary, Josué; Loisel, Didier; Frampas, Eric; De Carli, Emilie; Delion, Matthieu; Rialland, Xavier; Toulgoat, Frédérique

2016-02-01

Magnetic resonance (MR) images from children with optic pathway glioma (OPG) are complex. We initiated this study to evaluate the accuracy of MR imaging (MRI) interpretation and to propose a simple and reproducible imaging classification for MRI. We randomly selected 140 MRIs from among 510 MRIs performed on 104 children diagnosed with OPG in France from 1990 to 2004. These images were reviewed independently by three radiologists (F.T., 15 years of experience in neuroradiology; D.L., 25 years of experience in pediatric radiology; and J.L., 3 years of experience in radiology) using a classification derived from the Dodge and modified Dodge classifications. Intra- and interobserver reliabilities were assessed using the Bland-Altman method and the kappa coefficient. These reviews allowed the definition of reliable criteria for MRI interpretation. The reviews showed intraobserver variability and large discrepancies among the three radiologists (kappa coefficient varying from 0.11 to 1). These variabilities were too large for the interpretation to be considered reproducible over time or among observers. A consensual analysis, taking into account all observed variabilities, allowed the development of a definitive interpretation protocol. Using this revised protocol, we observed consistent intra- and interobserver results (kappa coefficient varying from 0.56 to 1). The mean interobserver difference for the solid portion of the tumor with contrast enhancement was 0.8 cm(3) (limits of agreement = -16 to 17). We propose simple and precise rules for improving the accuracy and reliability of MRI interpretation for children with OPG. Further studies will be necessary to investigate the possible prognostic value of this approach.

Classification of autistic individuals and controls using cross-task characterization of fMRI activity

PubMed Central

Chanel, Guillaume; Pichon, Swann; Conty, Laurence; Berthoz, Sylvie; Chevallier, Coralie; Grèzes, Julie

2015-01-01

Multivariate pattern analysis (MVPA) has been applied successfully to task-based and resting-based fMRI recordings to investigate which neural markers distinguish individuals with autistic spectrum disorders (ASD) from controls. While most studies have focused on brain connectivity during resting state episodes and regions of interest approaches (ROI), a wealth of task-based fMRI datasets have been acquired in these populations in the last decade. This calls for techniques that can leverage information not only from a single dataset, but from several existing datasets that might share some common features and biomarkers. We propose a fully data-driven (voxel-based) approach that we apply to two different fMRI experiments with social stimuli (faces and bodies). The method, based on Support Vector Machines (SVMs) and Recursive Feature Elimination (RFE), is first trained for each experiment independently and each output is then combined to obtain a final classification output. Second, this RFE output is used to determine which voxels are most often selected for classification to generate maps of significant discriminative activity. Finally, to further explore the clinical validity of the approach, we correlate phenotypic information with obtained classifier scores. The results reveal good classification accuracy (range between 69% and 92.3%). Moreover, we were able to identify discriminative activity patterns pertaining to the social brain without relying on a priori ROI definitions. Finally, social motivation was the only dimension which correlated with classifier scores, suggesting that it is the main dimension captured by the classifiers. Altogether, we believe that the present RFE method proves to be efficient and may help identifying relevant biomarkers by taking advantage of acquired task-based fMRI datasets in psychiatric populations. PMID:26793434

Brain Decoding-Classification of Hand Written Digits from fMRI Data Employing Bayesian Networks

PubMed Central

Yargholi, Elahe'; Hossein-Zadeh, Gholam-Ali

2016-01-01

We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection. PMID:27468261

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

[Histological and molecular classification of endometrial carcinoma and therapeutical implications].

PubMed

Genestie, Catherine; Leary, Alexandra; Devouassoux, Mojgan; Auguste, Aurélie

2017-12-01

Endometrial cancer is the fourth cause of cancer in women in France and is the second most common cancer of the gynecologic cancer after breast cancer with 7275 new cases in 2012. The incidence of this neoplasm tends to increase with population aging, diabetes and obesity's augmentation. In rare cases, a hereditary factor has been described: Lynch's syndrome. The therapeutic management of the patient depends on the endometrial biopsy which specifies the histological type and the histo-prognostic grade as well as the MRI which allow the tumor staging. Within the last decade, improvement in technologies such as genomic, transcriptomic and histological analyses, allowed the establishment of new and finer classifications of endometrial carcinomas. The latest classification proposed by The Cancer Genomic Atlas (TCGA), has been made routinely applicable through the international consortium TransPORTEC. It consists of 4 groups listed from good to poor prognosis: (1) ultra-mutated "POLE"; (2) hyper-mutated "MSI"; (3) low copy number "NSMP" and (4) high number of copies "TP53 mutated" (serous-like). This integrated characterization combined with mutational data opens new opportunities for therapeutic strategies. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

Diagnostic Accuracy of MRI, DWI MRI, FDG-PET/CT and FEC PET/CT in the Detection of Lymph Node Metastases in Surgically Staged Endometrial and Cervical Carcinoma

ClinicalTrials.gov

2017-08-30

Surgically Staged Endometrial and Cervical Carcinoma; Cervical Cancer: Invasive Disease, FIGO Stage 1B1 or Higher; Endometrial Cancer; Stage 1A With Myometrial Invasion or Any Higher Stage and Grade 3; Stage 1A With Myometrial Invasion or Any Other Higher Stage and Serous Papillary or Clear Cell Sub-types; Stage II Disease or Above and Any Histology Grade

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Diagnostic Classification of Schizophrenia Patients on the Basis of Regional Reward-Related fMRI Signal Patterns

PubMed Central

Koch, Stefan P.; Hägele, Claudia; Haynes, John-Dylan; Heinz, Andreas; Schlagenhauf, Florian; Sterzer, Philipp

2015-01-01

Functional neuroimaging has provided evidence for altered function of mesolimbic circuits implicated in reward processing, first and foremost the ventral striatum, in patients with schizophrenia. While such findings based on significant group differences in brain activations can provide important insights into the pathomechanisms of mental disorders, the use of neuroimaging results from standard univariate statistical analysis for individual diagnosis has proven difficult. In this proof of concept study, we tested whether the predictive accuracy for the diagnostic classification of schizophrenia patients vs. healthy controls could be improved using multivariate pattern analysis (MVPA) of regional functional magnetic resonance imaging (fMRI) activation patterns for the anticipation of monetary reward. With a searchlight MVPA approach using support vector machine classification, we found that the diagnostic category could be predicted from local activation patterns in frontal, temporal, occipital and midbrain regions, with a maximal cluster peak classification accuracy of 93% for the right pallidum. Region-of-interest based MVPA for the ventral striatum achieved a maximal cluster peak accuracy of 88%, whereas the classification accuracy on the basis of standard univariate analysis reached only 75%. Moreover, using support vector regression we could additionally predict the severity of negative symptoms from ventral striatal activation patterns. These results show that MVPA can be used to substantially increase the accuracy of diagnostic classification on the basis of task-related fMRI signal patterns in a regionally specific way. PMID:25799236

PROSPECTIVE COMPARISON OF TUMOR STAGING USING COMPUTED TOMOGRAPHY VERSUS MAGNETIC RESONANCE IMAGING FINDINGS IN DOGS WITH NASAL NEOPLASIA: A PILOT STUDY.

PubMed

Lux, Cassie N; Culp, William T N; Johnson, Lynelle R; Kent, Michael; Mayhew, Philipp; Daniaux, Lise A; Carr, Alaina; Puchalski, Sarah

2017-05-01

Identification of nasal neoplasia extension and tumor staging in dogs is most commonly performed using computed tomography (CT), however magnetic resonance imaging (MRI) is routinely used in human medicine. A prospective pilot study enrolling six dogs with nasal neoplasia was performed with CT and MRI studies acquired under the same anesthetic episode. Interobserver comparison and comparison between the two imaging modalities with regard to bidimensional measurements of the nasal tumors, tumor staging using historical schemes, and assignment of an ordinal scale of tumor margin clarity at the tumor-soft tissue interface were performed. The hypotheses included that MRI would have greater tumor measurements, result in higher tumor staging, and more clearly define the tumor soft tissue interface when compared to CT. Evaluation of bone involvement of the nasal cavity and head showed a high level of agreement between CT and MRI. Estimation of tumor volume using bidimensional measurements was higher on MRI imaging in 5/6 dogs, and resulted in a median tumor volume which was 18.4% higher than CT imaging. Disagreement between CT and MRI was noted with meningeal enhancement, in which two dogs were positive for meningeal enhancement on MRI and negative on CT. One of six dogs had a higher tumor stage on MRI compared to CT, while the remaining five agreed. Magnetic resonance imaging resulted in larger bidimensional measurements and tumor volume estimates, along with a higher likelihood of identifying meningeal enhancement when compared to CT imaging. Magnetic resonance imaging may provide integral information for tumor staging, prognosis, and treatment planning. © 2017 American College of Veterinary Radiology.

Non-hypervascular hypointense nodules on Gd-EOB-DTPA-enhanced MRI as a predictor of outcomes for early-stage HCC.

PubMed

Toyoda, Hidenori; Kumada, Takashi; Tada, Toshifumi; Sone, Yasuhiro; Maeda, Atsuyuki; Kaneoka, Yuji

2015-01-01

In patients with hepatocellular carcinoma (HCC), gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) often identifies non-hypervascular hypointense hepatic nodules during the hepatobiliary phase, but their prognostic significance is unclear. We conducted a prospective observational study to investigate the impact of non-hypervascular hypointense hepatic nodules detected by Gd-EOB-DTPA-enhanced MRI on the outcome of patients with early-stage HCC. Post-treatment recurrence and survival rates were analyzed in 138 patients with non-recurrent, early-stage HCC [Barcelona Clinic Liver Cancer (BCLC) stage 0 or A] and Child-Pugh A liver function according to the presence of non-hypervascular hypointense nodules on pretreatment Gd-EOB-DTPA-enhanced MRI. Non-hypervascular hypointense hepatic nodules were detected in 51 (37.0%) patients with early-stage HCC on pretreatment Gd-EOB-DTPA-enhanced MRI. Recurrence rates were significantly higher in patients with non-hypervascular hypointense nodules (p < 0.0001). Based on a multivariate analysis, the presence of non-hypervascular hypointense hepatic nodules on Gd-EOB-DTPA-enhanced MRI was independently associated with an increased recurrence rate, independent of tumor progression or treatment (p = 0.0005). The survival rate was significantly lower in patients with non-hypervascular hypointense nodules on Gd-EOB-DTPA-enhanced MRI (p = 0.0108). In patients with early-stage typical HCC (BCLC 0 or A), the presence of concurrent non-hypervascular hypointense hepatic nodules in the hepatobiliary phase of pretreatment Gd-EOB-DTPA-enhanced MRI is an indicator of higher likelihood of recurrence after treatment and may be a marker for unfavorable outcome.

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

PubMed

Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

2011-01-01

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches

NASA Astrophysics Data System (ADS)

Amit, Guy; Ben-Ari, Rami; Hadad, Omer; Monovich, Einat; Granot, Noa; Hashoul, Sharbell

2017-03-01

Diagnostic interpretation of breast MRI studies requires meticulous work and a high level of expertise. Computerized algorithms can assist radiologists by automatically characterizing the detected lesions. Deep learning approaches have shown promising results in natural image classification, but their applicability to medical imaging is limited by the shortage of large annotated training sets. In this work, we address automatic classification of breast MRI lesions using two different deep learning approaches. We propose a novel image representation for dynamic contrast enhanced (DCE) breast MRI lesions, which combines the morphological and kinetics information in a single multi-channel image. We compare two classification approaches for discriminating between benign and malignant lesions: training a designated convolutional neural network and using a pre-trained deep network to extract features for a shallow classifier. The domain-specific trained network provided higher classification accuracy, compared to the pre-trained model, with an area under the ROC curve of 0.91 versus 0.81, and an accuracy of 0.83 versus 0.71. Similar accuracy was achieved in classifying benign lesions, malignant lesions, and normal tissue images. The trained network was able to improve accuracy by using the multi-channel image representation, and was more robust to reductions in the size of the training set. A small-size convolutional neural network can learn to accurately classify findings in medical images using only a few hundred images from a few dozen patients. With sufficient data augmentation, such a network can be trained to outperform a pre-trained out-of-domain classifier. Developing domain-specific deep-learning models for medical imaging can facilitate technological advancements in computer-aided diagnosis.

Classification of cardiac-related artifacts in dynamic contrast breast MRI

NASA Astrophysics Data System (ADS)

Stegbauer, Keith C.; Smith, Justin P.; Niemeyer, Tanya L.; Wood, Chris

2004-05-01

Dynamic contrast breast MRI is becoming an important adjunct in screening women at high risk for breast cancer, determining extent of disease (staging) and monitoring response to therapy. In dynamic contrast breast MRI, regions of rapid contrast uptake indicate increases in vascularity which can be associated with abnormal tissue, sometimes significant for malignant disease. To show these areas of enhancement, subtractions between the pre and post contrast images and maximum intensity projections (MIPs) are computed. Many projections are obscured by normally enhancing anatomy (heart, aorta, pulmonary vessels). Identification of these structures allows their removal from MIPs, which improves image quality, diagnostic utility and the conspicuity of the enhancing regions. In this study, a fully automated classifier is presented which uses the spatial location of enhancing regions to separate those that occur inside the chest wall from those occurring in the tissue of interest (breast, axilla, chest wall). The classifier was trained on 21 studies each acquired at a different institution (699 clusters of pixels), and tested on 7 studies (231 clusters of pixels) that were not part of the training set. Multiple cost functions for training were examined. The measurements for the peak performance of the classifier were sensitivity 97.0%, specificity 99.4%, PPV 99.9%, NPV 78.8%.

The association between histological, macroscopic and magnetic resonance imaging assessed synovitis in end-stage knee osteoarthritis: a cross-sectional study.

PubMed

Riis, R G C; Gudbergsen, H; Simonsen, O; Henriksen, M; Al-Mashkur, N; Eld, M; Petersen, K K; Kubassova, O; Bay Jensen, A C; Damm, J; Bliddal, H; Arendt-Nielsen, L; Boesen, M

2017-02-01

To investigate the association between magnetic resonance imaging (MRI), macroscopic and histological assessments of synovitis in end-stage knee osteoarthritis (KOA). Synovitis of end-stage osteoarthritic knees was assessed using non-contrast-enhanced (CE), contrast-enhanced magnetic resonance imaging (CE-MRI) and dynamic contrast-enhanced (DCE)-MRI prior to (TKR) and correlated with microscopic and macroscopic assessments of synovitis obtained intraoperatively. Multiple bivariate correlations were used with a pre-specified threshold of 0.70 for significance. Also, multiple regression analyses with different subsets of MRI-variables as explanatory variables and the histology score as outcome variable were performed with the intention to find MRI-variables that best explain the variance in histological synovitis (i.e., highest R 2 ). A stepped approach was taken starting with basic characteristics and non-CE MRI-variables (model 1), after which CE-MRI-variables were added (model 2) with the final model also including DCE-MRI-variables (model 3). 39 patients (56.4% women, mean age 68 years, Kellgren-Lawrence (KL) grade 4) had complete MRI and histological data. Only the DCE-MRI variable MExNvoxel (surrogate of the volume and degree of synovitis) and the macroscopic score showed correlations above the pre-specified threshold for acceptance with histological inflammation. The maximum R 2 -value obtained in Model 1 was R 2  = 0.39. In Model 2, where the CE-MRI-variables were added, the highest R 2  = 0.52. In Model 3, a four-variable model consisting of the gender, one CE-MRI and two DCE-MRI-variables yielded a R 2  = 0.71. DCE-MRI is correlated with histological synovitis in end-stage KOA and the combination of CE and DCE-MRI may be a useful, non-invasive tool in characterising synovitis in KOA. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Epidemiological findings and clinical and magnetic resonance presentations in subacute sclerosing panencephalitis.

PubMed

Cece, H; Tokay, L; Yildiz, S; Karakas, O; Karakas, E; Iscan, A

2011-01-01

Subacute sclerosing panencephalitis (SSPE) is a rare, progressive, inflammatory neurodegenerative disease. This study investigated the relationships of clinical stage with epidemiological and magnetic resonance imaging (MRI) findings in SSPE by retrospective review of 76 cases (57 male) diagnosed by typical periodic electroencephalographic features, clinical symptoms and elevated measles antibody titre in cerebrospinal fluid. Clinical stage at diagnosis was I or II in 48 patients, III in 25 and IV in three. Prominent findings at presentation were atonic/myoclonic seizures (57.9%) and mental deterioration with behaviour alteration (30.3%). Frequent MRI findings (13 - 32 patients) were subcortical, periventricular and cortical involvement and brain atrophy; the corpus callosum, basal ganglia, cerebellum and brainstem were less frequently involved. Five patients had pseudotumour cerebri. Cranial MRI at initial diagnosis was normal in 21 patients (19 stage I/II, two stage III/IV). Abnormal MRI findings were significantly more frequent in the later stages, thus a normal initial cranial MRI does not exclude SSPE, which should, therefore, be kept in mind in childhood demyelinating diseases even when the presentation is unusual.

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Comparison of 68Ga-PSMA PET/CT and multiparametric MRI for staging of high-risk prostate cancer68Ga-PSMA PET and MRI in prostate cancer.

PubMed

Tulsyan, Shruti; Das, Chandan J; Tripathi, Madhavi; Seth, Amlesh; Kumar, Rajeev; Bal, Chandrasekhar

2017-12-01

We carried out this study to compare Glu-NH-CO-NH-Lys-(Ahx) [Ga(HBED-CC)] [Ga prostate-specific membrane antigen-11 (PSMA-11)] PET with multiparametric MRI (mpMRI) for the staging of high-risk prostate cancer. This was a prospective study in which 36 patients with high-risk prostate cancer were included. The criteria for inclusion were biopsy-proven prostate cancer with a serum prostate specific antigen of at least 20 and/or Gleason's score of at least 8. Each patient then underwent both gallium-68 (Ga)-PSMA PET/computed tomography (CT) and mpMRI including diffusion-weighted whole-body imaging with background body signal suppression within an interval of 1 week and both modalities were compared for staging of primary disease, lymph node, and distant metastasis. The median age of the 36 patients included was 65 years (range: 44-80 years) and the median prostate specific antigen was 94.3 ng/ml (range: 20-19005  ng/ml). Concordance for localization of primary on Ga-PSMA PET/CT and MRI was observed in 19/36 (52.7%) patients. Concurrence for T staging on Ga-PSMA and MRI was observed in 58.3% of patients. Ga-PSMA PET/CT detected higher numbers of patients with regional (29) and nonregional (15) lymph nodes in comparison with MRI (20 and 5, respectively). Concurrence for regional and nonregional lymph node staging was observed in 72.2% of patients. Additional sites of metastatic disease reported on Ga-PSMA PET/CT were to the skeleton in one patient, the lung in two patients, and the liver in one patient. This study suggests that Ga-PSMA PET/CT is useful for lymph node and metastases staging in high-risk prostate cancers, whereas its utility for staging of disease in the prostate is limited.

Magnetic resonance imaging in prostate cancer detection and management: a systematic review.

PubMed

Monni, Fabio; Fontanella, Paolo; Grasso, Angelica; Wiklund, Peter; Ou, Yen-Chuan; Randazzo, Marco; Rocco, Bernardo; Montanari, Emanuele; Bianchi, Giampaolo

2017-12-01

The aim of our work was to evaluate the role of multi-parametric magnetic resonance imaging (mpMRI) in detection and management of prostate cancer (PC); specifically investigating the efficacy of mpMRI-based biopsy techniques in terms of diagnostic yield of significant prostate neoplasm and the improved management of patients who choose conservative treatments or active surveillance. A systematic and critical analysis through Medline, Embase, Scopus and Web of Science databases was carried out in March 2016, following the PRISMA ("Preferred Reporting Items for Systematic Reviews and Meta-Analyses") statement. The search was conducted using the following key words: "MRI/TRUS-fusion biopsy," "PIRADS," "prostate cancer," "magnetic resonance imaging (MRI)," "multiparametric MRI (mpMRI)," "systematic prostate biopsy (SB)," "targeted prostate biopsy (TPB)." English language articles were reviewed for inclusion ability. Sixty-six studies were selected in order to evaluate the characteristics and limitations of traditional sample biopsy, the role of mpMRI in detection of PC, specifically the increased degree of diagnostic accuracy of targeted prostate biopsy compared to systematic biopsy (12 cores), and to transperineal saturation biopsies with trans-rectal ultrasound (TRUS) only. MpMRI can detect index lesions in approximately 90% of cases when compared to prostatectomy specimen. The diagnostic performance of biparametric MRI (T2w + DWI) is not inferior to mpMRI, offering valid options to diminish cost- and time-consumption. Since approximately 10% of significant lesions are still MRI-invisible, systematic cores biopsy seem to still be necessary. The analysis of the different techniques shows that in-bore MRI-guided biopsy and MRI/TRUS-fusion-guided biopsy are superior in detection of significant PC compared to visual estimation alone. MpMRI proved to be very effective in active surveillance, as it prevents underdetection of significant PC and it assesses low-risk disease accurately. In higher-risk disease, presurgical MRI may change the clinically-based surgical plan in up to a third of cases. Targeted prostate biopsy, guided by mpMRI, is able to improve diagnostic accuracy and to reduce the detection of insignificant PC. Since the negative predictive value (NPV) of mpMRI is still imperfect, systematic cores biopsy should not be omitted for optimal staging of disease. A process of a progressive and periodic evolution in the detection and radiological classification of prostate lesions (such as PIRADS), is still needed in patients in active surveillance and in radical prostatectomy planning.

Tumor Size on Abdominal MRI Versus Pathologic Specimen in Resected Pancreatic Adenocarcinoma: Implications for Radiation Treatment Planning

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

Hall, William A., E-mail: whall4@emory.edu; Winship Cancer Institute, Emory University, Atlanta, Georgia; Mikell, John L.

2013-05-01

Purpose: We assessed the accuracy of abdominal magnetic resonance imaging (MRI) for determining tumor size by comparing the preoperative contrast-enhanced T1-weighted gradient echo (3-dimensional [3D] volumetric interpolated breath-hold [VIBE]) MRI tumor size with pathologic specimen size. Methods and Materials: The records of 92 patients who had both preoperative contrast-enhanced 3D VIBE MRI images and detailed pathologic specimen measurements were available for review. Primary tumor size from the MRI was independently measured by a single diagnostic radiologist (P.M.) who was blinded to the pathology reports. Pathologic tumor measurements from gross specimens were obtained from the pathology reports. The maximum dimensions ofmore » tumor measured in any plane on the MRI and the gross specimen were compared. The median difference between the pathology sample and the MRI measurements was calculated. A paired t test was conducted to test for differences between the MRI and pathology measurements. The Pearson correlation coefficient was used to measure the association of disparity between the MRI and pathology sizes with the pathology size. Disparities relative to pathology size were also examined and tested for significance using a 1-sample t test. Results: The median patient age was 64.5 years. The primary site was pancreatic head in 81 patients, body in 4, and tail in 7. Three patients were American Joint Commission on Cancer stage IA, 7 stage IB, 21 stage IIA, 58 stage IIB, and 3 stage III. The 3D VIBE MRI underestimated tumor size by a median difference of 4 mm (range, −34-22 mm). The median largest tumor dimensions on MRI and pathology specimen were 2.65 cm (range, 1.5-9.5 cm) and 3.2 cm (range, 1.3-10 cm), respectively. Conclusions: Contrast-enhanced 3D VIBE MRI underestimates tumor size by 4 mm when compared with pathologic specimen. Advanced abdominal MRI sequences warrant further investigation for radiation therapy planning in pancreatic adenocarcinoma before routine integration into the treatment planning process.« less

Multimodal endocavitary ultrasound versus MRI and clinical findings in pre- and post-treatment advanced cervical cancer. Preliminary report.

PubMed

Csutak, Csaba; Badea, Radu; Bolboaca, Sorana D; Ordeanu, Claudia; Nagy, Viorica M; Fekete, Zsolt; Chiorean, Liliana; Dudea, Sorin M

2016-03-01

The aim of this study was to evaluate the use of pre and post-therapy transrectal and transvaginal ultrasonography (TRUS, TVUS) with contrast enhancement and strain elastography compared with clinical examination and magnetic resonance imaging (MRI) in the assessment of advanced stage cervical cancer. This was a prospective study, carried out over a period of nine months on subjects with advanced-stage cervical cancer (stage >/= IIB). All included patients were examined clinically and underwent abdomino-pelvic contrast enhanced MRI and multimodal US examinations (TRUS with strain elastography and contrast enhanced TVUS) at the time of diagnosis and after radiochemotherapy. Tumor size and staging at TRUS and TVUS was compared with the same data obtained by clinical examination and MRI. Pathology was the golden standard. Eight patients accomplished the inclusion criteria. In five cases the tumor stage was identical on clinical and MRI examinations. In all cases parametrial infiltration was diagnosed by all pre-treatment examinations. No significant differences were observed in tumor size between clinical, US and MRI exams either at baseline or post-therapy, in native or post-contrast examinations. The size of the tumor evaluated pre-treatment proved to be significantly smaller post-contrast in both US and MRI examinations compared with the native images. Post-therapy, no significant differences were observed on US measured tumor dimensions when comparing native with post-contrast images. Oppositely, significant smaller dimensions were observed on post-contrast MRI compared with native scans. TRUS is accurate in the estimation of pre-therapy cervical cancer dimension. The post therapy tumor evaluation is better performed with MRI. The use of intravenous contrast agents on both examinations did not improved the accuracy of tumor evaluation pre or post-therapy.

Advanced Pediatric Brain Imaging Research and Training Program

DTIC Science & Technology

2014-10-01

death and disability in children. Recent advances in pediatric magnetic resonance imaging ( MRI ) techniques are revolutionizing our understanding of... MRI , brain injury. 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a...principles of pediatric brain injury and recovery following injury, as well as the clinical application of sophisticated MRI techniques that are

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

PubMed Central

Meszlényi, Regina J.; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network. PMID:29089883

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

PubMed

Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

Manifold Regularized Multitask Feature Learning for Multimodality Disease Classification

PubMed Central

Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

2015-01-01

Multimodality based methods have shown great advantages in classification of Alzheimer’s disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. PMID:25277605

Frequency and Clinical Significance of Extramammary Findings on Breast Magnetic Resonance Imaging.

PubMed

Phadke, Sneha; Thomas, Alexandra; Yang, Limin; Moore, Catherine; Xia, Chang; Schroeder, Mary C

2016-10-01

Use of breast magnetic resonance imaging (MRI) for screening and local staging of breast cancer has increased. With this, questions have emerged regarding the management and effect of extramammary findings (EMFs) reported on breast MRI. Breast MRI studies performed between January 1, 2007 and December 31, 2012 at the University of Iowa were analyzed. Data were collected regarding number and location of EMFs, characteristics of the patients who had a breast MRI, and time to first treatment among the patients who had a breast MRI for stage I-III breast cancer. During the study period, 1305 breast MRIs were obtained in 772 women. An EMF was found in 140 studies (10.7%) and 113 women (14.6%). EMFs were more likely in MRIs of older patients (50 vs. 54 years, P = .004) and postmenopausal women (P = .001). Anatomically, most EMFs were seen in the liver (89 of 140) or bone (21 of 140). Eight women (0.6%) had an EMF on breast MRI that led to upstaging to stage IV breast cancer. For patients with stage I-III breast cancer, the finding of an EMF on breast MRI did not affect time to initial cancer treatment (13 vs. 14 days; P = .586). EMFs on breast MRI are seen with some frequency and occur more commonly in older, postmenopausal women. In our study, most EMFs were benign and did not affect patient outcome with regard to upstaging to stage IV disease or time to cancer treatment. A very small portion of studies revealed subclinical advanced breast cancer. Copyright © 2015 Elsevier Inc. All rights reserved.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

PubMed

Wang, Gang; Wang, Yalin

2017-02-15

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.

PubMed

Chen, Xiaobo; Zhang, Han; Zhang, Lichi; Shen, Celina; Lee, Seong-Whan; Shen, Dinggang

2017-10-01

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

The brain MRI classification problem from wavelets perspective

NASA Astrophysics Data System (ADS)

Bendib, Mohamed M.; Merouani, Hayet F.; Diaba, Fatma

2015-02-01

Haar and Daubechies 4 (DB4) are the most used wavelets for brain MRI (Magnetic Resonance Imaging) classification. The former is simple and fast to compute while the latter is more complex and offers a better resolution. This paper explores the potential of both of them in performing Normal versus Pathological discrimination on the one hand, and Multiclassification on the other hand. The Whole Brain Atlas is used as a validation database, and the Random Forest (RF) algorithm is employed as a learning approach. The achieved results are discussed and statistically compared.

Multiparametric prostate MRI: technical conduct, standardized report and clinical use.

PubMed

Manfredi, Matteo; Mele, Fabrizio; Garrou, Diletta; Walz, Jochen; Fütterer, Jurgen J; Russo, Filippo; Vassallo, Lorenzo; Villers, Arnauld; Emberton, Mark; Valerio, Massimo

2018-02-01

Multiparametric prostate MRI (mp-MRI) is an emerging imaging modality for diagnosis, characterization, staging, and treatment planning of prostate cancer (PCa). The technique, results reporting, and its role in clinical practice have been the subject of significant development over the last decade. Although mp-MRI is not yet routinely used in the diagnostic pathway, almost all urological guidelines have emphasized the potential role of mp-MRI in several aspects of PCa management. Moreover, new MRI sequences and scanning techniques are currently under evaluation to improve the diagnostic accuracy of mp-MRI. This review presents an overview of mp-MRI, summarizing the technical applications, the standardized reporting systems used, and their current roles in various stages of PCa management. Finally, this critical review also reports the main limitations and future perspectives of the technique.

Label-aligned Multi-task Feature Learning for Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zu, Chen; Jie, Biao; Liu, Mingxia; Chen, Songcan

2015-01-01

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI. PMID:26572145

Magnetic resonance imaging classification of haemodialysis-related amyloidosis of the shoulder: risk factors and arthroscopic treatment.

PubMed

Ando, Akira; Hagiwara, Yoshihiro; Sekiguchi, Takuya; Koide, Masashi; Kanazawa, Kenji; Watanabe, Takashi; Itoi, Eiji

2017-07-01

This study proposed new magnetic resonance imaging (MRI) of haemodialysis shoulders (HDS) focusing on the changes of the rotator cuff, and rotator interval and risk factors for the development of HDS were examined. Eighty-five shoulders in 72 patients with a chief complaint of shoulder pain during haemodialysis and at least 10 years of haemodialysis were included. They were classified into 5 groups based on the thickness of the rotator cuff and conditions of rotator interval. Clinical and radiological findings in each grade were examined, and risk factors for the development of HDS were evaluated. Arthroscopic surgeries were performed on 22 shoulders in 20 patients, and arthroscopic findings were also evaluated. Positive correlations for the development of HDS were observed in duration of haemodialysis, positive hepatitis C virus (HCV) infection, and previous haemodialysis-related orthopaedic surgery (P < 0.001, respectively). Strong correlations were observed between positive HCV and the progression of HDS (odds ratio 24.8, 95 % confidence interval 5.7-107.6). Arthroscopically, progression of the surrounding soft tissue degeneration was observed, and operative times were lengthened depending on the progression of MRI grading. A new MRI classification of HDS which may be helpful when considering arthroscopic surgeries has been proposed. Positive HCV infection was strongly associated with the progression of HDS on MRI. Conditions of the rotator interval and the rotator cuff based on the MRI classification should be examined when treating HDS patients. III.

Validation of a partial coherence interferometry method for estimating retinal shape

PubMed Central

Verkicharla, Pavan K.; Suheimat, Marwan; Pope, James M.; Sepehrband, Farshid; Mathur, Ankit; Schmid, Katrina L.; Atchison, David A.

2015-01-01

To validate a simple partial coherence interferometry (PCI) based retinal shape method, estimates of retinal shape were determined in 60 young adults using off-axis PCI, with three stages of modeling using variants of the Le Grand model eye, and magnetic resonance imaging (MRI). Stage 1 and 2 involved a basic model eye without and with surface ray deviation, respectively and Stage 3 used model with individual ocular biometry and ray deviation at surfaces. Considering the theoretical uncertainty of MRI (12-14%), the results of the study indicate good agreement between MRI and all three stages of PCI modeling with <4% and <7% differences in retinal shapes along horizontal and vertical meridians, respectively. Stage 2 and Stage 3 gave slightly different retinal co-ordinates than Stage 1 and we recommend the intermediate Stage 2 as providing a simple and valid method of determining retinal shape from PCI data. PMID:26417496

Validation of a partial coherence interferometry method for estimating retinal shape.

PubMed

Verkicharla, Pavan K; Suheimat, Marwan; Pope, James M; Sepehrband, Farshid; Mathur, Ankit; Schmid, Katrina L; Atchison, David A

2015-09-01

To validate a simple partial coherence interferometry (PCI) based retinal shape method, estimates of retinal shape were determined in 60 young adults using off-axis PCI, with three stages of modeling using variants of the Le Grand model eye, and magnetic resonance imaging (MRI). Stage 1 and 2 involved a basic model eye without and with surface ray deviation, respectively and Stage 3 used model with individual ocular biometry and ray deviation at surfaces. Considering the theoretical uncertainty of MRI (12-14%), the results of the study indicate good agreement between MRI and all three stages of PCI modeling with <4% and <7% differences in retinal shapes along horizontal and vertical meridians, respectively. Stage 2 and Stage 3 gave slightly different retinal co-ordinates than Stage 1 and we recommend the intermediate Stage 2 as providing a simple and valid method of determining retinal shape from PCI data.

Added Value of Fetal MRI in the Evaluation of Fetal Anomalies of the Corpus Callosum: A Retrospective Analysis of 78 Cases.

PubMed

Manevich-Mazor, Mirra; Weissmann-Brenner, Alina; Bar Yosef, Omer; Hoffmann, Chen; Mazor, Roei David; Mosheva, Mariela; Achiron, Reuven Ryszard; Katorza, Eldad

2018-06-07

 To evaluate the added value of fetal MRI to ultrasound in detecting and specifying callosal anomalies, and its impact on clinical decision making.  Fetuses with a sonographic diagnosis of an anomalous corpus callosum (CC) who underwent a subsequent fetal brain MRI between 2010 and 2015 were retrospectively evaluated and classified according to the severity of the findings. The findings detected on ultrasound were compared to those detected on MRI. An analysis was performed to assess whether fetal MRI altered the group classification, and thus the management of these pregnancies.  78 women were recruited following sonographic diagnoses of either complete or partial callosal agenesis, short, thin or thick CC. Normal MRI studies were obtained inµ19 cases (24 %). Among these, all children available for follow-up received an adequate adaptive score in their Vineland II adaptive behavior scale assessment. Analysis of the concordance between US and MRI demonstrated a substantial level of agreement for complete callosal agenesis (kappa: 0.742), moderate agreement for thin CC (kappa: 0.418) and fair agreement for all other callosal anomalies. Comparison between US and MRI-based mild/severe findings classifications revealed that MRI contributed to a change in the management for 28 fetuses (35.9 %), mostly (25 fetuses, 32.1 %) in favor of pregnancy preservation.  Fetal MRI effectively detects callosal anomalies and enables satisfactory validation of the presence or absence of callosal anomalies identified by ultrasound and adds valuable data that improves clinical decision making. © Georg Thieme Verlag KG Stuttgart · New York.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

PubMed Central

Jang, Hojin; Plis, Sergey M.; Calhoun, Vince D.; Lee, Jong-Hwan

2016-01-01

Feedforward deep neural networks (DNN), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean ± standard deviation; %) of 6.9 (± 3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4 ± 4.6) and the two-layer network (7.4 ± 4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. PMID:27079534

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

PubMed

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

MR imaging of the traumatic triangular fibrocartilaginous complex tear

PubMed Central

Griffith, James F.; Fung, Cindy S. Y.; Lee, Ryan K. L.; Tong, Cina S. L.; Wong, Clara W. Y.; Tse, Wing Lim; Ho, Pak Cheong

2017-01-01

Triangular fibrocartilage complex is a major stabilizer of the distal radioulnar joint (DRUJ). However, triangular fibrocartilage complex (TFCC) tear is difficult to be diagnosed on MRI for its intrinsic small and thin structure with complex anatomy. The purpose of this article is to review the anatomy of TFCC, state of art MRI imaging technique, normal appearance and features of tear on MRI according to the Palmar’s classification. Atypical tear and limitations of MRI in diagnosis of TFCC tear are also discussed. PMID:28932701

Ultrasound of the coracoclavicular ligaments in the acute phase of an acromioclavicular disjonction: Comparison of radiographic, ultrasound and MRI findings.

PubMed

Faruch Bilfeld, Marie; Lapègue, Franck; Chiavassa Gandois, Hélène; Bayol, Marie Aurélie; Bonnevialle, Nicolas; Sans, Nicolas

2017-02-01

Acromioclavicular joint injuries are typically diagnosed by clinical and radiographic assessment with the Rockwood classification, which is crucial for treatment planning. The purpose of this study was to describe how the ultrasound findings of acromioclavicular joint injury compare with radiography and MRI findings. Forty-seven patients with suspected unilateral acromioclavicular joint injury after acute trauma were enrolled in this prospective study. All patients underwent digital radiography, ultrasound and 3T MRI. A modified Rockwood classification was used to evaluate the coracoclavicular ligaments. The classifications of acromioclavicular joint injuries diagnosed with radiography, ultrasound and MRI were compared. MRI was used as the gold standard. The agreement between the ultrasound and MRI findings was very good, with a correlation coefficient of 0.83 (95 % CI: 0.72-0.90; p < 0.0001). Ultrasound detected coracoclavicular ligament injuries with a sensitivity of 88.9 %, specificity of 90.0 %, positive predictive value of 92.3 % and negative predictive value of 85.7 %. The agreement between the ultrasound and radiography findings was poor, with a correlation coefficient of 0.69 (95 % CI: 0.51-0.82; p < 0.0001). Ultrasound is an effective examination for the diagnostic work-up of lesions of the coracoclavicular ligaments in the acute phase of an acromioclavicular injury. • Ultrasound is appropriate for acute acromioclavicular trauma due to its accessibility. • Ultrasound contributes to the diagnostic work-up of acute lesions of the coracoclavicular ligaments. • Ultrasound is appropriate in patients likely to benefit from surgical treatment. • Ultrasound could be a supplement to standard radiography in acute acromioclavicular trauma.

Cross-classification of musical and vocal emotions in the auditory cortex.

PubMed

Paquette, Sébastien; Takerkart, Sylvain; Saget, Shinji; Peretz, Isabelle; Belin, Pascal

2018-05-09

Whether emotions carried by voice and music are processed by the brain using similar mechanisms has long been investigated. Yet neuroimaging studies do not provide a clear picture, mainly due to lack of control over stimuli. Here, we report a functional magnetic resonance imaging (fMRI) study using comparable stimulus material in the voice and music domains-the Montreal Affective Voices and the Musical Emotional Bursts-which include nonverbal short bursts of happiness, fear, sadness, and neutral expressions. We use a multivariate emotion-classification fMRI analysis involving cross-timbre classification as a means of comparing the neural mechanisms involved in processing emotional information in the two domains. We find, for affective stimuli in the violin, clarinet, or voice timbres, that local fMRI patterns in the bilateral auditory cortex and upper premotor regions support above-chance emotion classification when training and testing sets are performed within the same timbre category. More importantly, classifier performance generalized well across timbre in cross-classifying schemes, albeit with a slight accuracy drop when crossing the voice-music boundary, providing evidence for a shared neural code for processing musical and vocal emotions, with possibly a cost for the voice due to its evolutionary significance. © 2018 New York Academy of Sciences.

Inter-reader reproducibility of dynamic contrast-enhanced magnetic resonance imaging in patients with non-small cell lung cancer treated with bevacizumab and erlotinib.

PubMed

van den Boogaart, Vivian E M; de Lussanet, Quido G; Houben, Ruud M A; de Ruysscher, Dirk; Groen, Harry J M; Marcus, J Tim; Smit, Egbert F; Dingemans, Anne-Marie C; Backes, Walter H

2016-03-01

Objectives When evaluating anti-tumor treatment response by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) it is necessary to assure its validity and reproducibility. This has not been well addressed in lung tumors. Therefore we have evaluated the inter-reader reproducibility of response classification by DCE-MRI in patients with non-small cell lung cancer (NSCLC) treated with bevacizumab and erlotinib enrolled in a multicenter trial. Twenty-one patients were scanned before and 3 weeks after start of treatment with DCE-MRI in a multicenter trial. The scans were evaluated by two independent readers. The primary lung tumor was used for response assessment. Responses were assessed in terms of relative changes in tumor mean trans endothelial transfer rate (K(trans)) and its heterogeneity in terms of the spatial standard deviation. Reproducibility was expressed by the inter-reader variability, intra-class correlation coefficient (ICC) and dichotomous response classification. The inter-reader variability and ICC for the relative K(trans) were 5.8% and 0.930, respectively. For tumor heterogeneity the inter-reader variability and ICC were 0.017 and 0.656, respectively. For the two readers the response classification for relative K(trans) was concordant in 20 of 21 patients (k=0.90, p<0.0001) and for tumor heterogeneity in 19 of 21 patients (k=0.80, p<0.0001). Strong agreement was seen with regard to the inter-reader variability and reproducibility of response classification by the two readers of lung cancer DCE-MRI scans. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Role of endorectal coil magnetic resonance imaging in treatment of patients with prostate cancer and in determining radical prostatectomy surgical margin status: report of a single surgeon's practice.

PubMed

Zhang, Jian Qing; Loughlin, Kevin R; Zou, Kelly H; Haker, Steven; Tempany, Clare M C

2007-06-01

To evaluate the role of the combination of endorectal coil and external multicoil array magnetic resonance imaging (MRI) in the management of prostate cancer and predicting the surgical margin status in a single-surgeon practice. We reviewed all patients referred by a single surgeon from January 1993 to May 2002 for staging prostate MRI before selecting treatment. All MRI examinations were performed using 1.5T (Signa, GE Medical Systems) with a combination of endorectal and pelvic multicoil array. The tumor size, stage, and total gland volume on MRI, prostate-specific antigen (PSA) level, and Gleason score were all compared with the pathologic stage and diagnosis of positive surgical margins (PSMs). A total of 232 patients were evaluated, of whom 110 underwent radical prostatectomy, all performed by one surgeon (group 1), and 122 did not (group 2). The results showed that MRI stage, PSA level, and age were all significantly different (P <0.001). In group 1, the results showed a high specificity (99%) and accuracy (91%) for MRI staging of T3 cancer. The postoperative follow-up (median 4.5 years) revealed that 90% of men had PSA levels of less than 0.1 ng/mL. The PSM rate was 16%. No significant difference was found on MRI between the PSM group and non-PSM group. A single tumor length greater than 1.8 cm was the cutpoint above which PSMs were found (P = 0.002). The results of our study have shown that the combined use of clinical data and endorectal MRI can help optimize patient treatment and selection for surgery and, in a single surgeon's practice, lead to successful outcomes.

[Feedback of ultrasound and RMI in the staging of endometrial carcinoma in early stage].

PubMed

Buhler, J; Routiot, T; Polet-Lefebvre, K; Morel, O

2015-04-01

Endometrial cancer is the most common gynecological cancer in France. The therapeutic management is based on preoperative staging. The recommended imaging examination remains the MRI. This is to evaluate ultrasound and MRI in the staging for localized cancers. This is a retrospective observational study, conducted from July 2012 to July 2014, at the University Hospital of Nancy, on all patients care for endometrial cancer stage I, who underwent a pelvic ultrasound and MRI for the assessment of myometrial infiltration. Twenty-nine patients were included with a mean age of 69 years and a BMI of 30 kg/m(2). Using ultrasound, we have a sensitivity of 58%, a specificity of 100%, a positive predictive value (PPV) of 100%, a negative predictive value (NPV) of 70% and an accuracy of 75%. Using MRI, we have a sensitivity of 83%, a specificity of 100%, a PPV of 83%, a VPN of 88%, and an accuracy of 86%. Transvaginal sonography should be performed before post-menopausal bleeding. It remains possible in the staging of localized cancers. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Ultrahigh field MRI in clinical neuroimmunology: a potential contribution to improved diagnostics and personalised disease management.

PubMed

Sinnecker, Tim; Kuchling, Joseph; Dusek, Petr; Dörr, Jan; Niendorf, Thoralf; Paul, Friedemann; Wuerfel, Jens

2015-01-01

Conventional magnetic resonance imaging (MRI) at 1.5 Tesla (T) is limited by modest spatial resolution and signal-to-noise ratio (SNR), impeding the identification and classification of inflammatory central nervous system changes in current clinical practice. Gaining from enhanced susceptibility effects and improved SNR, ultrahigh field MRI at 7 T depicts inflammatory brain lesions in great detail. This review summarises recent reports on 7 T MRI in neuroinflammatory diseases and addresses the question as to whether ultrahigh field MRI may eventually improve clinical decision-making and personalised disease management.

Multiparametric Magnetic Resonance Imaging of the Prostate for Tumour Detection and Local Staging: Imaging in 1.5T and Histopathologic Correlation.

PubMed

Loggitsi, Dimitra; Gyftopoulos, Anastasios; Economopoulos, Nikolaos; Apostolaki, Aikaterini; Kalogeropoulos, Theodoros; Thanos, Anastasios; Alexopoulou, Efthimia; Kelekis, Nikolaos L

2017-11-01

The study sought to prospectively evaluate which technique among T2-weighted images, dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted (DW) MRI, or a combination of the 2, is best suited for prostate cancer detection and local staging. Twenty-seven consecutive patients with biopsy-proven adenocarcinoma of the prostate underwent MRI on a 1.5T scanner with a surface phased-array coil prior radical prostatectomy. Combined anatomical and functional imaging was performed with the use of T2-weighted sequences, DCE MRI, and DW MRI. We compared the imaging results with whole mount histopathology. For the multiparametric approach, significantly higher sensitivity values, that is, 53% (95% confidence interval [CI]: 41.0-64.1) were obtained as compared with each modality alone or any combination of the 3 modalities (P < .05). The specificity for this multiparametric approach, being 90.3% (95% CI: 86.3-93.3) was not significantly higher (P < .05) as compared with the values of the combination of T2+DCE MRI, DW+DCE MRI, or DCE MRI alone. Among the 3 techniques, DCE had the best performance for tumour detection in both the peripheral and the transition zone. High negative predictive value rates (>86%) were obtained for both tumour detection and local staging. The combination of T2-weighted sequences, DCE MRI, and DW MRI yields higher diagnostic performance for tumour detection and local staging than can any of these techniques alone or even any combination of them. Copyright © 2017 Canadian Association of Radiologists. Published by Elsevier Inc. All rights reserved.

A fuzzy integral method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification across multiple subjects.

PubMed

Cacha, L A; Parida, S; Dehuri, S; Cho, S-B; Poznanski, R R

2016-12-01

The huge number of voxels in fMRI over time poses a major challenge to for effective analysis. Fast, accurate, and reliable classifiers are required for estimating the decoding accuracy of brain activities. Although machine-learning classifiers seem promising, individual classifiers have their own limitations. To address this limitation, the present paper proposes a method based on the ensemble of neural networks to analyze fMRI data for cognitive state classification for application across multiple subjects. Similarly, the fuzzy integral (FI) approach has been employed as an efficient tool for combining different classifiers. The FI approach led to the development of a classifiers ensemble technique that performs better than any of the single classifier by reducing the misclassification, the bias, and the variance. The proposed method successfully classified the different cognitive states for multiple subjects with high accuracy of classification. Comparison of the performance improvement, while applying ensemble neural networks method, vs. that of the individual neural network strongly points toward the usefulness of the proposed method.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

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.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Usefulness of combining gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging and contrast-enhanced ultrasound for diagnosing the macroscopic classification of small hepatocellular carcinoma.

PubMed

Kobayashi, Tomoki; Aikata, Hiroshi; Hatooka, Masahiro; Morio, Kei; Morio, Reona; Kan, Hiromi; Fujino, Hatsue; Fukuhara, Takayuki; Masaki, Keiichi; Ohno, Atsushi; Naeshiro, Noriaki; Nakahara, Takashi; Honda, Yohji; Murakami, Eisuke; Kawaoka, Tomokazu; Tsuge, Masataka; Hiramatsu, Akira; Imamura, Michio; Kawakami, Yoshiiku; Hyogo, Hideyuki; Takahashi, Shoichi; Chayama, Kazuaki

2015-11-01

Non-simple nodules in hepatocellular carcinoma (HCC) correlate with poor prognosis. Therefore, we examined the diagnostic ability of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (EOB-MRI) and contrast-enhanced ultrasound (CEUS) for diagnosing the macroscopic classification of small HCCs. A total of 85 surgically resected nodules (≤30 mm) were analyzed. HCCs were pathologically classified as simple nodular (SN) and non-SN. By evaluating hepatobiliary phase (HBP) of EOB-MRI and Kupffer phase of CEUS, the diagnostic abilities of both modalities to correctly distinguish between SN and non-SN were compared. Forty-six nodules were diagnosed as SN and the remaining 39 nodules as non-SN. The area under the ROC curve (AUROCs, 95% confidence interval) for the diagnosis of non-SN were EOB-MRI, 0.786 (0.682-0.890): CEUS, 0.784 (0.679-0.889), in combination, 0.876 (0.792-0.959). The sensitivity, specificity, and accuracy were 64.1%, 95.7%, and 81.2% in EOB-MRI, 56.4%, 97.8%, and 78.8% in CEUS, and 84.6%, 95.7%, and 90.6% in combination, respectively. High diagnostic ability was obtained when diagnosed in both modalities combined. The sensitivity was especially statistically significant compared to CEUS. Combined diagnosis by EOB-MRI and CEUS can provide high-quality imaging assessment for determining non-SN in small HCCs. • Non-SN has a higher frequency of MVI and intrahepatic metastasis than SN. • Macroscopic classification is useful to choose the treatment strategy for small HCCs. • Diagnostic ability for macroscopic findings of EOB-MRI and CEUS were statistically equal. • The diagnosis of macroscopic findings by individual modality has limitations. • Combined diagnosis of EOB-MRI and CEUS provides high diagnostic ability.

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P; Amrhein, Timothy J; Befera, Nicholas T; Lungren, Matthew P

2017-04-01

The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

[Potentialities of low-field magnetic resonance tomography in the diagnosis and treatment of invasive cancer of cervix uteri].

PubMed

Shatov, A V

2003-01-01

The aim of the study was to evaluate the efficiency of low-field (0.14 T) magnetic resonance imaging (MRI) in the diagnosis and treatment of cancer of the cervix uteri. Low-field MRI was performed in 39 patients with cancer of the cervix uteri to define the stage of the tumor and to follow up the outcomes of their treatment. Particular emphasis was laid on the determination of the size of the tumor and the presence of parametral invasion and on metastatic lesions of lymph nodes. MRI data were compared with clinical, morphological, and surgical staging results. In detecting the stage of cancer of the cervix uteri, the accuracy of MRI was 72% whereas that of clinical study was 51%. In determining parametral invasion, the accuracy of clinical study and low-field MRI was 71 and 90%, respectively. The sensitivity and specificity of MRI were 83 and 92%, respectively. The anterioposterior tumor size was an important prognostic factor in following up the outcomes of treatment as there was its close association and the incidence of tumor recurrences. The present study has indicated that the high efficiency of low-field MRI in detecting the stage of invasive cancer of cervix uteri makes it the method of choice in planning treatment and monitoring the outcomes of combined radiation therapy.

Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.

PubMed

Bickelhaupt, Sebastian; Paech, Daniel; Kickingereder, Philipp; Steudle, Franziska; Lederer, Wolfgang; Daniel, Heidi; Götz, Michael; Gählert, Nils; Tichy, Diana; Wiesenfarth, Manuel; Laun, Frederik B; Maier-Hein, Klaus H; Schlemmer, Heinz-Peter; Bonekamp, David

2017-08-01

To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T 2 -weighted sequences. From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T 2 -weighted, (T 2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC. The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI. In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique. 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616. © 2017 International Society for Magnetic Resonance in Medicine.

Differentiating between bipolar and unipolar depression in functional and structural MRI studies.

PubMed

Han, Kyu-Man; De Berardis, Domenico; Fornaro, Michele; Kim, Yong-Ku

2018-03-28

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification. Copyright © 2018 Elsevier Inc. All rights reserved.

Decoding of Ankle Flexion and Extension from Cortical Current Sources Estimated from Non-invasive Brain Activity Recording Methods.

PubMed

Mejia Tobar, Alejandra; Hyoudou, Rikiya; Kita, Kahori; Nakamura, Tatsuhiro; Kambara, Hiroyuki; Ogata, Yousuke; Hanakawa, Takashi; Koike, Yasuharu; Yoshimura, Natsue

2017-01-01

The classification of ankle movements from non-invasive brain recordings can be applied to a brain-computer interface (BCI) to control exoskeletons, prosthesis, and functional electrical stimulators for the benefit of patients with walking impairments. In this research, ankle flexion and extension tasks at two force levels in both legs, were classified from cortical current sources estimated by a hierarchical variational Bayesian method, using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The hierarchical prior for the current source estimation from EEG was obtained from activated brain areas and their intensities from an fMRI group (second-level) analysis. The fMRI group analysis was performed on regions of interest defined over the primary motor cortex, the supplementary motor area, and the somatosensory area, which are well-known to contribute to movement control. A sparse logistic regression method was applied for a nine-class classification (eight active tasks and a resting control task) obtaining a mean accuracy of 65.64% for time series of current sources, estimated from the EEG and the fMRI signals using a variational Bayesian method, and a mean accuracy of 22.19% for the classification of the pre-processed of EEG sensor signals, with a chance level of 11.11%. The higher classification accuracy of current sources, when compared to EEG classification accuracy, was attributed to the high number of sources and the different signal patterns obtained in the same vertex for different motor tasks. Since the inverse filter estimation for current sources can be done offline with the present method, the present method is applicable to real-time BCIs. Finally, due to the highly enhanced spatial distribution of current sources over the brain cortex, this method has the potential to identify activation patterns to design BCIs for the control of an affected limb in patients with stroke, or BCIs from motor imagery in patients with spinal cord injury.

Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning.

PubMed

Zhou, Yongxia; Yu, Fang; Duong, Timothy

2014-01-01

This study employed graph theory and machine learning analysis of multiparametric MRI data to improve characterization and prediction in autism spectrum disorders (ASD). Data from 127 children with ASD (13.5±6.0 years) and 153 age- and gender-matched typically developing children (14.5±5.7 years) were selected from the multi-center Functional Connectome Project. Regional gray matter volume and cortical thickness increased, whereas white matter volume decreased in ASD compared to controls. Small-world network analysis of quantitative MRI data demonstrated decreased global efficiency based on gray matter cortical thickness but not with functional connectivity MRI (fcMRI) or volumetry. An integrative model of 22 quantitative imaging features was used for classification and prediction of phenotypic features that included the autism diagnostic observation schedule, the revised autism diagnostic interview, and intelligence quotient scores. Among the 22 imaging features, four (caudate volume, caudate-cortical functional connectivity and inferior frontal gyrus functional connectivity) were found to be highly informative, markedly improving classification and prediction accuracy when compared with the single imaging features. This approach could potentially serve as a biomarker in prognosis, diagnosis, and monitoring disease progression.

Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm.

PubMed

Belilovsky, Eugene; Gkirtzou, Katerina; Misyrlis, Michail; Konova, Anna B; Honorio, Jean; Alia-Klein, Nelly; Goldstein, Rita Z; Samaras, Dimitris; Blaschko, Matthew B

2015-12-01

We explore various sparse regularization techniques for analyzing fMRI data, such as the ℓ1 norm (often called LASSO in the context of a squared loss function), elastic net, and the recently introduced k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects. We show that in many cases, use of the k-support norm leads to better predictive performance, solution stability, and interpretability as compared to other standard approaches. We additionally analyze the advantages of using the absolute loss function versus the standard squared loss which leads to significantly better predictive performance for the regularization methods tested in almost all cases. Our results support the use of the k-support norm for fMRI analysis and on the clinical side, the generalizability of the I-RISA model of cocaine addiction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

NASA Astrophysics Data System (ADS)

Alfano, R.; Soetemans, D.; Bauman, G. S.; Gibson, E.; Gaed, M.; Moussa, M.; Gomez, J. A.; Chin, J. L.; Pautler, S.; Ward, A. D.

2018-02-01

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score <4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.

TU-G-201-02: An MRI Simulator From Proposal to Operation

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

Cao, Y.

2015-06-15

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

Finding regional models of the Alzheimer disease by fusing information from neuropsychological tests and structural MR images

NASA Astrophysics Data System (ADS)

Giraldo, Diana L.; García-Arteaga, Juan D.; Romero, Eduardo

2016-03-01

Initial diagnosis of Alzheimer's disease (AD) is based on the patient's clinical history and a battery of neuropsy-chological tests. This work presents an automatic strategy that uses Structural Magnetic Resonance Imaging (MRI) to learn brain models for different stages of the disease using information from clinical assessments. Then, a comparison of the discriminant power of the models in different anatomical areas is made by using the brain region of the models as a reference frame for the classification problem, by using the projection into the AD model a Receiver Operating Characteristic (ROC) curve is constructed. Validation was performed using a leave- one-out scheme with 86 subjects (20 AD and 60 NC) from the Open Access Series of Imaging Studies (OASIS) database. The region with the best classification performance was the left amygdala where it is possible to achieve a sensibility and specificity of 85% at the same time. The regions with the best performance, in terms of the AUC, are in strong agreement with those described as important for the diagnosis of AD in clinical practice.

MR Imaging Based Treatment Planning for Radiotherapy of Prostate Cancer

DTIC Science & Technology

2008-02-01

Radiotherapy, MR-based treatment planning, dosimetry, Monte Carlo dose verification, Prostate Cancer, MRI -based DRRs 16. SECURITY CLASSIFICATION...AcQPlan system Version 5 was used for the study , which is capable of performing dose calculation on both CT and MRI . A four field 3D conformal planning...prostate motion studies for 3DCRT and IMRT of prostate cancer; (2) to investigate and improve the accuracy of MRI -based treatment planning dose calculation

Comparative study of the detection of joint injury in early-stage rheumatoid arthritis by magnetic resonance imaging of the wrist and finger joints and physical examination.

PubMed

Tamai, Mami; Kawakami, Atsushi; Iwamoto, Naoki; Kawashiri, Shin-Ya; Fujikawa, Keita; Aramaki, Toshiyuki; Kita, Junko; Okada, Akitomo; Koga, Tomohiro; Arima, Kazuhiko; Kamachi, Makoto; Yamasaki, Satoshi; Nakamura, Hideki; Ida, Hiroaki; Origuchi, Tomoki; Takao, Shoichiro; Aoyagi, Kiyoshi; Uetani, Masataka; Eguchi, Katsumi

2011-03-01

To verify whether magnetic resonance imaging (MRI)-proven joint injury is sensitive as compared with joint injury determined by physical examination. MRI of the wrist and finger joints of both hands was examined in 51 early-stage rheumatoid arthritis (RA) patients by both plain and gadolinium diethylenetriaminepentaacetic acid-enhanced MRI. Synovitis, bone edema, and bone erosion (the latter two included as bone lesions at the wrist joints); metacarpophalangeal joints; and proximal interphalangeal joints were considered as MRI-proven joint injury. Japan College of Rheumatology-certified rheumatologists had given a physical examination just before the MRI study. The presence of tender and/or swollen joints in the same fields as MRI was considered as joint injury on physical examination. The association of MRI-proven joint injury with physical examination-proven joint injury was examined. A total of 1,110 sites were available to be examined. MRI-proven joint injury was found in 521 sites, whereas the other 589 sites were normal. Physical examination-proven joint injury was found in 305 sites, which was significantly low as compared with MRI-proven joint injury (P = 1.1 × 10(-12) versus MRI). Joint injury on physical examination was not found in 81.5% of the sites where MRI findings were normal. Furthermore, an association of the severity of MRI-proven joint injury with that of joint injury on physical examination was clearly demonstrated (P = 1.6 × 10(-15), r(s) = 0.469). Our present data suggest that MRI is not only sensitive but accurately reflects the joint injury in patients with early-stage RA. Copyright © 2011 by the American College of Rheumatology.

Urinary bladder cancer T-staging from T2-weighted MR images using an optimal biomarker approach

NASA Astrophysics Data System (ADS)

Wang, Chuang; Udupa, Jayaram K.; Tong, Yubing; Chen, Jerry; Venigalla, Sriram; Odhner, Dewey; Guzzo, Thomas J.; Christodouleas, John; Torigian, Drew A.

2018-02-01

Magnetic resonance imaging (MRI) is often used in clinical practice to stage patients with bladder cancer to help plan treatment. However, qualitative assessment of MR images is prone to inaccuracies, adversely affecting patient outcomes. In this paper, T2-weighted MR image-based quantitative features were extracted from the bladder wall in 65 patients with bladder cancer to classify them into two primary tumor (T) stage groups: group 1 - T stage < T2, with primary tumor locally confined to the bladder, and group 2 - T stage < T2, with primary tumor locally extending beyond the bladder. The bladder was divided into 8 sectors in the axial plane, where each sector has a corresponding reference standard T stage that is based on expert radiology qualitative MR image review and histopathologic results. The performance of the classification for correct assignment of T stage grouping was then evaluated at both the patient level and the sector level. Each bladder sector was divided into 3 shells (inner, middle, and outer), and 15,834 features including intensity features and texture features from local binary pattern and gray-level co-occurrence matrix were extracted from the 3 shells of each sector. An optimal feature set was selected from all features using an optimal biomarker approach. Nine optimal biomarker features were derived based on texture properties from the middle shell, with an area under the ROC curve of AUC value at the sector and patient level of 0.813 and 0.806, respectively.

Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions.

PubMed

Dijkstra, Hildebrand; Dorrius, Monique D; Wielema, Mirjam; Pijnappel, Ruud M; Oudkerk, Matthijs; Sijens, Paul E

2016-12-01

To assess if specificity can be increased when semiautomated breast lesion analysis of quantitative diffusion-weighted imaging (DWI) is implemented after dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) in the workup of BI-RADS 3 and 4 breast lesions larger than 1 cm. In all, 120 consecutive patients (mean-age, 48 years; age range, 23-75 years) with 139 breast lesions (≥1 cm) were examined (2010-2014) with 1.5T DCE-MRI and DWI (b = 0, 50, 200, 500, 800, 1000 s/mm 2 ) and the BI-RADS classification and histopathology were obtained. For each lesion malignancy was excluded using voxelwise semiautomated breast lesion analysis based on previously defined thresholds for the apparent diffusion coefficient (ADC) and the three intravoxel incoherent motion (IVIM) parameters: molecular diffusion (D slow ), microperfusion (D fast ), and the fraction of D fast (f fast ). The sensitivity (Se), specificity (Sp), and negative predictive value (NPV) based on only IVIM parameters combined in parallel (D slow , D fast , and f fast ), or the ADC or the BI-RADS classification by DCE-MRI were compared. Subsequently, the Se, Sp, and NPV of the combination of the BI-RADS classification by DCE-MRI followed by the IVIM parameters in parallel (or the ADC) were compared. In all, 23 of 139 breast lesions were benign. Se and Sp of DCE-MRI was 100% and 30.4% (NPV = 100%). Se and Sp of IVIM parameters in parallel were 92.2% and 52.2% (NPV = 57.1%) and for the ADC 95.7% and 17.4%, respectively (NPV = 44.4%). In all, 26 of 139 lesions were classified as BI-RADS 3 (n = 7) or BI-RADS 4 (n = 19). DCE-MRI combined with ADC (Se = 99.1%, Sp = 34.8%) or IVIM (Se = 99.1%, Sp = 56.5%) did significantly improve (P = 0.016) Sp of DCE-MRI alone for workup of BI-RADS 3 and 4 lesions (NPV = 92.9%). Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649. © 2016 International Society for Magnetic Resonance in Medicine.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

PubMed

Chong, Catherine D; Gaw, Nathan; Fu, Yinlin; Li, Jing; Wu, Teresa; Schwedt, Todd J

2017-08-01

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

Reproducibility of neuroimaging analyses across operating systems

PubMed Central

Glatard, Tristan; Lewis, Lindsay B.; Ferreira da Silva, Rafael; Adalat, Reza; Beck, Natacha; Lepage, Claude; Rioux, Pierre; Rousseau, Marc-Etienne; Sherif, Tarek; Deelman, Ewa; Khalili-Mahani, Najmeh; Evans, Alan C.

2015-01-01

Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed. PMID:25964757

Reproducibility of neuroimaging analyses across operating systems.

PubMed

Glatard, Tristan; Lewis, Lindsay B; Ferreira da Silva, Rafael; Adalat, Reza; Beck, Natacha; Lepage, Claude; Rioux, Pierre; Rousseau, Marc-Etienne; Sherif, Tarek; Deelman, Ewa; Khalili-Mahani, Najmeh; Evans, Alan C

2015-01-01

Neuroimaging pipelines are known to generate different results depending on the computing platform where they are compiled and executed. We quantify these differences for brain tissue classification, fMRI analysis, and cortical thickness (CT) extraction, using three of the main neuroimaging packages (FSL, Freesurfer and CIVET) and different versions of GNU/Linux. We also identify some causes of these differences using library and system call interception. We find that these packages use mathematical functions based on single-precision floating-point arithmetic whose implementations in operating systems continue to evolve. While these differences have little or no impact on simple analysis pipelines such as brain extraction and cortical tissue classification, their accumulation creates important differences in longer pipelines such as subcortical tissue classification, fMRI analysis, and cortical thickness extraction. With FSL, most Dice coefficients between subcortical classifications obtained on different operating systems remain above 0.9, but values as low as 0.59 are observed. Independent component analyses (ICA) of fMRI data differ between operating systems in one third of the tested subjects, due to differences in motion correction. With Freesurfer and CIVET, in some brain regions we find an effect of build or operating system on cortical thickness. A first step to correct these reproducibility issues would be to use more precise representations of floating-point numbers in the critical sections of the pipelines. The numerical stability of pipelines should also be reviewed.

Development and implementation of a synoptic MRI report for preoperative staging of rectal cancer on a population-based level.

PubMed

Kennedy, Erin D; Milot, Laurent; Fruitman, Mark; Al-Sukhni, Eisar; Heine, Gabrielle; Schmocker, Selina; Brown, Gina; McLeod, Robin S

2014-06-01

Colorectal cancer physician champions across the province of Ontario, Canada, reported significant concern about appropriate selection of patients for preoperative chemoradiotherapy because of perceived variation in the completeness and consistency of MRI reports. The purpose of this work was to develop, pilot test, and implement a synoptic MRI report for preoperative staging of rectal cancer. This was an integrated knowledge translation project. This study was conducted in Ontario, Canada. Surgeons, radiologists, radiation oncologists, medical oncologists, and pathologists treating patients with rectal cancer were included in this study. A multifaceted knowledge translation strategy was used to develop, pilot test, and implement a synoptic MRI report. This strategy included physician champions, audit and feedback, assessment of barriers, and tailoring to the local context. A radiology webinar was conducted to pilot test the synoptic MRI report. Seventy-three (66%) of 111 Ontario radiologists participated in the radiology webinar and evaluated the synoptic MRI report. A total of 78% and 90% radiologists expressed that the synoptic MRI report was easy to use and included all of the appropriate items; 82% noted that the synoptic MRI report improved the overall quality of their information, and 83% indicated they would consider using this report in their clinical practice. An MRI report audit after implementation of the synoptic MRI report showed a 39% improvement in the completeness of MRI reports and a 37% uptake of the synoptic MRI report format across the province. Radiologists evaluating the synoptic MRI report and participating in the radiology webinar may not be representative of gastroenterologic radiologists in other geographic jurisdictions. The evaluation of completeness and uptake of the synoptic MRI reports is limited because of unmeasured differences that may occur before and after the MRI. A synoptic MRI report for preoperative staging of rectal cancer was successfully developed and pilot tested in the province of Ontario, Canada.

A comparison of the diagnostic accuracy of magnetic resonance imaging to axillary ultrasound in the detection of axillary nodal metastases in newly diagnosed breast cancer.

PubMed

Assing, Matthew A; Patel, Bhavika K; Karamsadkar, Neel; Weinfurtner, Jared; Usmani, Omar; Kiluk, John V; Drukteinis, Jennifer S

2017-11-01

Patients with a diagnosis of invasive breast cancer are increasingly undergoing breast magnetic resonance imaging (MRI) for preoperative staging including evaluation of axillary lymph node metastases (ALNM). This retrospective study aims to evaluate the utility of adding axillary ultrasound (AUS) in the preoperative setting when an MRI is planned or has already been performed. This IRB approved, HIPAA compliant study reviewed a total of 271 patients with a new diagnosis of invasive breast cancer at a single institution, between June 1, 2010 and June 30, 2013. The study included patients who received both AUS and MRI for preoperative staging. Data were divided into two cohorts, patients who underwent MRI prior to AUS and those who underwent AUS prior to MRI. AUS and MRI reports were categorized according to BI-RADS criteria as "suspicious" or "not suspicious" for ALNM. In the setting of a negative MRI and subsequent positive AUS, only one out of 25 cases (4%) were positive for metastases after correlating with histologic pathology. MRI detected metastatic disease in four out of 27 (15%) patients who had false-negative AUS performed prior to MRI. Our results indicate the addition of AUS after preoperative MRI does not contribute significantly to increased detection of missed disease. MRI could serve as the initial staging imaging method of the axilla in the setting that AUS is not initially performed and may be valuable in identification of lymph nodes not identified on AUS. © 2017 Wiley Periodicals, Inc.

Haptic fMRI: using classification to quantify task-correlated noise during goal-directed reaching motions.

PubMed

Menon, Samir; Quigley, Paul; Yu, Michelle; Khatib, Oussama

2014-01-01

Neuroimaging artifacts in haptic functional magnetic resonance imaging (Haptic fMRI) experiments have the potential to induce spurious fMRI activation where there is none, or to make neural activation measurements appear correlated across brain regions when they are actually not. Here, we demonstrate that performing three-dimensional goal-directed reaching motions while operating Haptic fMRI Interface (HFI) does not create confounding motion artifacts. To test for artifacts, we simultaneously scanned a subject's brain with a customized soft phantom placed a few centimeters away from the subject's left motor cortex. The phantom captured task-related motion and haptic noise, but did not contain associated neural activation measurements. We quantified the task-related information present in fMRI measurements taken from the brain and the phantom by using a linear max-margin classifier to predict whether raw time series data could differentiate between motion planning or reaching. fMRI measurements in the phantom were uninformative (2σ, 45-73%; chance=50%), while those in primary motor, visual, and somatosensory cortex accurately classified task-conditions (2σ, 90-96%). We also localized artifacts due to the haptic interface alone by scanning a stand-alone fBIRN phantom, while an operator performed haptic tasks outside the scanner's bore with the interface at the same location. The stand-alone phantom had lower temporal noise and had similar mean classification but a tighter distribution (bootstrap Gaussian fit) than the brain phantom. Our results suggest that any fMRI measurement artifacts for Haptic fMRI reaching experiments are dominated by actual neural responses.

Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features

NASA Astrophysics Data System (ADS)

Andreasen, Daniel; Edmund, Jens M.; Zografos, Vasileios; Menze, Bjoern H.; Van Leemput, Koen

2016-03-01

In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

What does magnetic resonance imaging add to the prenatal ultrasound diagnosis of facial clefts?

PubMed

Mailáth-Pokorny, M; Worda, C; Krampl-Bettelheim, E; Watzinger, F; Brugger, P C; Prayer, D

2010-10-01

Ultrasound is the modality of choice for prenatal detection of cleft lip and palate. Because its accuracy in detecting facial clefts, especially isolated clefts of the secondary palate, can be limited, magnetic resonance imaging (MRI) is used as an additional method for assessing the fetus. The aim of this study was to investigate the role of fetal MRI in the prenatal diagnosis of facial clefts. Thirty-four pregnant women with a mean gestational age of 26 (range, 19-34) weeks underwent in utero MRI, after ultrasound examination had identified either a facial cleft (n = 29) or another suspected malformation (micrognathia (n = 1), cardiac defect (n = 1), brain anomaly (n = 2) or diaphragmatic hernia (n = 1)). The facial cleft was classified postnatally and the diagnoses were compared with the previous ultrasound findings. There were 11 (32.4%) cases with cleft of the primary palate alone, 20 (58.8%) clefts of the primary and secondary palate and three (8.8%) isolated clefts of the secondary palate. In all cases the primary and secondary palate were visualized successfully with MRI. Ultrasound imaging could not detect five (14.7%) facial clefts and misclassified 15 (44.1%) facial clefts. The MRI classification correlated with the postnatal/postmortem diagnosis. In our hands MRI allows detailed prenatal evaluation of the primary and secondary palate. By demonstrating involvement of the palate, MRI provides better detection and classification of facial clefts than does ultrasound alone. Copyright © 2010 ISUOG. Published by John Wiley & Sons, Ltd.

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

Kowalchik, Kristin V.; Vallow, Laura A., E-mail: vallow.laura@mayo.edu; McDonough, Michelle

Purpose: To study the utility of preoperative breast MRI for partial breast irradiation (PBI) patient selection, using multivariable analysis of significant risk factors to create a classification rule. Methods and Materials: Between 2002 and 2009, 712 women with newly diagnosed breast cancer underwent preoperative bilateral breast MRI at Mayo Clinic Florida. Of this cohort, 566 were retrospectively deemed eligible for PBI according to the National Surgical Adjuvant Breast and Bowel Project Protocol B-39 inclusion criteria using physical examination, mammogram, and/or ultrasound. Magnetic resonance images were then reviewed to determine their impact on patient eligibility. The patient and tumor characteristics weremore » evaluated to determine risk factors for altered PBI eligibility after MRI and to create a classification rule. Results: Of the 566 patients initially eligible for PBI, 141 (25%) were found ineligible because of pathologically proven MRI findings. Magnetic resonance imaging detected additional ipsilateral breast cancer in 118 (21%). Of these, 62 (11%) had more extensive disease than originally noted before MRI, and 64 (11%) had multicentric disease. Contralateral breast cancer was detected in 28 (5%). Four characteristics were found to be significantly associated with PBI ineligibility after MRI on multivariable analysis: premenopausal status (P=.021), detection by palpation (P<.001), first-degree relative with a history of breast cancer (P=.033), and lobular histology (P=.002). Risk factors were assigned a score of 0-2. The risk of altered PBI eligibility from MRI based on number of risk factors was 0:18%; 1:22%; 2:42%; 3:65%. Conclusions: Preoperative bilateral breast MRI altered the PBI recommendations for 25% of women. Women who may undergo PBI should be considered for breast MRI, especially those with lobular histology or with 2 or more of the following risk factors: premenopausal, detection by palpation, and first-degree relative with a history of breast cancer.« less

Sci-Thur PM - Colourful Interactions: Highlights 04: A Fast Quantitative MRI Acquisition and Processing Pipeline for Radiation Treatment Planning and Simulation

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

Jutras, Jean-David

MRI-only Radiation Treatment Planning (RTP) is becoming increasingly popular because of a simplified work-flow, and less inconvenience to the patient who avoids multiple scans. The advantages of MRI-based RTP over traditional CT-based RTP lie in its superior soft-tissue contrast, and absence of ionizing radiation dose. The lack of electron-density information in MRI can be addressed by automatic tissue classification. To distinguish bone from air, which both appear dark in MRI, an ultra-short echo time (UTE) pulse sequence may be used. Quantitative MRI parametric maps can provide improved tissue segmentation/classification and better sensitivity in monitoring disease progression and treatment outcome thanmore » standard weighted images. Superior tumor contrast can be achieved on pure T{sub 1} images compared to conventional T{sub 1}-weighted images acquired in the same scan duration and voxel resolution. In this study, we have developed a robust and fast quantitative MRI acquisition and post-processing work-flow that integrates these latest advances into the MRI-based RTP of brain lesions. Using 3D multi-echo FLASH images at two different optimized flip angles (both acquired in under 9 min, and 1mm isotropic resolution), parametric maps of T{sub 1}, proton-density (M{sub 0}), and T{sub 2}{sup *} are obtained with high contrast-to-noise ratio, and negligible geometrical distortions, water-fat shifts and susceptibility effects. An additional 3D UTE MRI dataset is acquired (in under 4 min) and post-processed to classify tissues for dose simulation. The pipeline was tested on four healthy volunteers and a clinical trial on brain cancer patients is underway.« less

Automated tracking, segmentation and trajectory classification of pelvic organs on dynamic MRI.

PubMed

Nekooeimehr, Iman; Lai-Yuen, Susana; Bao, Paul; Weitzenfeld, Alfredo; Hart, Stuart

2016-08-01

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

PubMed Central

Lee, Seong-Whan

2014-01-01

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis. PMID:24363140

Intra- and inter-observer agreement in MRI assessment of rotator cuff healing using the Sugaya classification 10years after surgery.

PubMed

Niglis, L; Collin, P; Dosch, J-C; Meyer, N; Kempf, J-F

2017-10-01

The long-term outcomes of rotator cuff repair are unclear. Recurrent tears are common, although their reported frequency varies depending on the type and interpretation challenges of the imaging method used. The primary objective of this study was to assess the intra- and inter-observer reproducibility of the MRI assessment of rotator cuff repair using the Sugaya classification 10years after surgery. The secondary objective was to determine whether poor reproducibility, if found, could be improved by using a simplified yet clinically relevant classification. Our hypothesis was that reproducibility was limited but could be improved by simplifying the classification. In a retrospective study, we assessed intra- and inter-observer agreement in interpreting 49 magnetic resonance imaging (MRI) scans performed 10years after rotator cuff repair. These 49 scans were taken at random among 609 cases that underwent re-evaluation, with imaging, for the 2015 SoFCOT symposium on 10-year and 20-year clinical and anatomical outcomes of rotator cuff repair for full-thickness tears. Each of three observers read each of the 49 scans on two separate occasions. At each reading, they assessed the supra-spinatus tendon according to the Sugaya classification in five types. Intra-observer agreement for the Sugaya type was substantial (κ=0.64) but inter-observer agreement was only fair (κ=0.39). Agreement improved when the five Sugaya types were collapsed into two categories (1-2-3 and 4-5) (intra-observer κ=0.74 and inter-observer κ=0.68). Using the Sugaya classification to assess post-operative rotator cuff healing was associated with substantial intra-observer and fair inter-observer agreement. A simpler classification into two categories improved agreement while remaining clinically relevant. II, prospective randomised low-power study. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

[Imaging in rheumatoid arthritis of the elbow].

PubMed

Lerch, K; Herold, T; Borisch, N; Grifka, J

2003-08-01

Early specific radiologic changes of rheumatoid arthritis can usually be detected in the hands and feet. Later stages of the disease process show a typical centripetal spread of the affected joints, i.e., shoulder, elbow, and knee. For prognostic assessment of cubital rheumatoid arthritis, conventional radiography still remains the gold standard. X-rays allow objective scoring and thus classification into standardized stages. A concentric destruction of the rheumatic joint as compared to deformity in the degenerative joint is the typical radiologic symptom to look for. For soft tissue assessment, ultrasound (US) should be the diagnostic tool of choice. Due to the thin surrounding soft tissue layer, as well as the advanced high-resolution technology, bony structures can also be well demonstrated in any plane. In the early arthritic stages, particularly the small changes, e.g., minimal erosions of the cortical area, are very well detectable by US. The use of "color" allows good evaluation of the synovial inflammatory status. Modern imaging methods such as computer- assisted tomography (CAT) scan and magnetic resonance imaging (MRI) are restricted to a few set indications and should not be chosen for routine examination. More invasive methods such as arthrography are no longer indicated for assessment of cubital rheumatoid arthritis.

Comparison of 68Ga-PSMA-11 PET-CT with mpMRI for preoperative lymph node staging in patients with intermediate to high-risk prostate cancer.

PubMed

Zhang, Qing; Zang, Shiming; Zhang, Chengwei; Fu, Yao; Lv, Xiaoyu; Zhang, Qinglei; Deng, Yongming; Zhang, Chuan; Luo, Rui; Zhao, Xiaozhi; Wang, Wei; Wang, Feng; Guo, Hongqian

2017-11-07

To evaluate the diagnostic value of 68 Ga-PSMA-11 PET-CT with multiparametric magnetic resonance imaging (mpMRI) for lymph node (LN) staging in patients with intermediate- to high-risk prostate cancer (PCa) undergoing radical prostatectomy (RP) with pelvic lymph node dissection (PLND). We retrospectively identified 42 consecutive patients with intermediate- to high-risk PCa according to D'Amico and without concomitant cancer. Preoperative 68 Ga-PSMA-11 PET-CT, pelvic mpMRI and subsequent robot assisted laparoscopic RP with PLND were performed in all patients. Among 42 patients assessed, the preoperative PSA value, Gleason score, pT stage and intraprostatic PCa volume of patients with LN metastases were all significantly higher than those without metastases (P = 0.029, 0.028, 0.004, respectively). The average maximum standardized uptake value (SUV) of 68 Ga-PSMA-11 PET-CT positive PCa of patients with or without LN metastases were 13.10 (range 6.12-51.75) and 7.22 (range 5.4-11.2), respectively (P < 0.001). 68 Ga-PSMA-11 PET-CT and pelvic mpMRI had the ability of succeed on preoperative definite accurate diagnosis and accurate localization of primary PCa in all 42 patients. Fifteen patients (35.71%) had a pN1 stage. 51 positive LN were found. Both 68 Ga-PSMA-11 PET-CT and pelvic mpMRI displayed brillient patient-based and region-based sensitivity, specificity, negative predictive value and positive predictive value. There was no statistical difference for the detection of LNMs according to the diameter of the LNMs between 68 Ga-PSMA-11 PET-CT and mpMRI in this study. Both 68 Ga-PSMA-11 PET-CT and mpMRI performed great value for LN staging in patients with intermediate- to high-risk PCa undergoing RP with PLND. However, despite excellent performance of 68 Ga-PSMA-11 PET-CT, it cannot replace mpMRI that remains excellent for lymph node staging.

Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.

PubMed

Liu, Xiaonan; Chen, Kewei; Wu, Teresa; Weidman, David; Lure, Fleming; Li, Jing

2018-04-01

Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus on early stages of the disease, that is, the mild cognitive impairment (MCI) and preclinical stages. Making a diagnosis of AD and offering a prognosis (likelihood of converting to AD) at these early stages are challenging tasks but possible with the help of multimodality imaging, such as magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG)-positron emission topography (PET), amyloid-PET, and recently introduced tau-PET, which provides different but complementary information. This article is a focused review of existing research in the recent decade that used statistical machine learning and artificial intelligence methods to perform quantitative analysis of multimodality image data for diagnosis and prognosis of AD at the MCI or preclinical stages. We review the existing work in 3 subareas: diagnosis, prognosis, and methods for handling modality-wise missing data-a commonly encountered problem when using multimodality imaging for prediction or classification. Factors contributing to missing data include lack of imaging equipment, cost, difficulty of obtaining patient consent, and patient drop-off (in longitudinal studies). Finally, we summarize our major findings and provide some recommendations for potential future research directions. Copyright © 2018 Elsevier Inc. All rights reserved.

Magnetic resonance imaging and computed tomography characteristics of renal cell carcinoma associated with Xp11.2 translocation/TFE3 gene fusion.

PubMed

Wang, Wei; Ding, Jianhui; Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence.

Magnetic Resonance Imaging and Computed Tomography Characteristics of Renal Cell Carcinoma Associated with Xp11.2 Translocation/TFE3 Gene Fusion

PubMed Central

Li, Yuan; Wang, Chaofu; Zhou, Liangping; Zhu, Hui; Peng, Weijun

2014-01-01

Purpose To characterize Xp11.2 translocation renal cell carcinoma (RCC) using magnetic resonance imaging (MRI) and computed tomography (CT). Methods This study retrospectively collected the MRI and CT data of twelve patients with Xp11.2 translocation RCC confirmed by pathology. Nine cases underwent dynamic contrast-enhanced MRI (DCE-MRI) and 6 cases underwent CT, of which 3 cases underwent MRI and CT simultaneously. The MRI and CT findings were analyzed in regard to tumor position, size, hemorrhagic, cystic or necrotic components, calcification, tumor density, signal intensity and enhancement features. Results The age of the 12 patients ranged from 13 to 46 years (mean age: 23 years). T2WI revealed heterogeneous intensity, hyper-intensity, and slight hypo-intensity in 6 cases, 2 cases, and 1 case, respectively. On DCE-MR images, mild, moderate, and marked rim enhancement of the tumor in the corticomedullary phase (CMP) were observed in 1, 6, and 2 cases, respectively. The tumor parenchyma showed iso-attenuation (n = 4) or slight hyper-attenuation (n = 1) compared to the normal renal cortex on non-contrast CT images. Imaging findings were suggestive of hemorrhage (n = 4) or necrosis (n = 8) in the tumors, and there was evidence of calcification in 8 cases by CT (n = 3) and pathology (n = 8). On dynamic contrast-enhanced CT images, 3 cases and 1 case manifested moderate and strong CMP enhancement, respectively. Nine tumors by MRI and 4 tumors by CT showed prolonged enhancement. Three neoplasms presented at stage I, 2 at stage II, 3 at stage III, and 4 at stage IV according the 2010 AJCC staging criteria. Conclusions XP11.2 translocation RCC should be considered when a child or young adult patient presents with a renal tumor with heterogeneous features such as hemorrhage, necrosis, cystic changes, and calcification on CT and MRI and/or is accompanied by metastatic evidence. PMID:24926688

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

PubMed Central

Guo, Hao; Liu, Lei; Chen, Junjie; Xu, Yong; Jie, Xiang

2017-01-01

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease. PMID:29249926

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

PubMed Central

Guan, Hao; Liu, Tao; Jiang, Jiyang; Tao, Dacheng; Zhang, Jicong; Niu, Haijun; Zhu, Wanlin; Wang, Yilong; Cheng, Jian; Kochan, Nicole A.; Brodaty, Henry; Sachdev, Perminder; Wen, Wei

2017-01-01

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment. PMID:29085292

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

PubMed

Zhang, Junming; Wu, Yan

2018-03-28

Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

[New ASAS criteria for the diagnosis of spondyloarthritis: diagnosing sacroiliitis by magnetic resonance imaging].

PubMed

Banegas Illescas, M E; López Menéndez, C; Rozas Rodríguez, M L; Fernández Quintero, R M

2014-01-01

Radiographic sacroiliitis has been included in the diagnostic criteria for spondyloarthropathies since the Rome criteria were defined in 1961. However, in the last ten years, magnetic resonance imaging (MRI) has proven more sensitive in the evaluation of the sacroiliac joints in patients with suspected spondyloarthritis and symptoms of sacroiliitis; MRI has proven its usefulness not only for diagnosis of this disease, but also for the follow-up of the disease and response to treatment in these patients. In 2009, The Assessment of SpondyloArthritis international Society (ASAS) developed a new set of criteria for classifying and diagnosing patients with spondyloarthritis; one important development with respect to previous classifications is the inclusion of MRI positive for sacroiliitis as a major diagnostic criterion. This article focuses on the radiologic part of the new classification. We describe and illustrate the different alterations that can be seen on MRI in patients with sacroiliitis, pointing out the limitations of the technique and diagnostic pitfalls. Copyright © 2013 SERAM. Published by Elsevier Espana. All rights reserved.

Sleep staging with movement-related signals.

PubMed

Jansen, B H; Shankar, K

1993-05-01

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.

Assessment of the Hong Kong Liver Cancer Staging System in Europe.

PubMed

Kolly, Philippe; Reeves, Helen; Sangro, Bruno; Knöpfli, Marina; Candinas, Daniel; Dufour, Jean-François

2016-06-01

European and American guidelines have endorsed the Barcelona Clinic Liver Cancer (BCLC) staging system. The aim of this study was to assess the performance of the recently developed Hong Kong Liver Cancer (HKLC) classification as a staging system for hepatocellular carcinoma (HCC) in Europe. We used a pooled set of 1693 HCC patients combining three prospective European cohorts. Discrimination ability between the nine substages and five stages of the HKLC classification system was assessed. To evaluate the predictive power of the HKLC and BCLC staging systems on overall survival, Nagelkerke pseudo R2, Bayesian Information Criterion and Harrell's concordance index were calculated. The number of patients who would benefit from a curative therapy was assessed for both staging systems. The HKLC classification in nine substages shows suboptimal discrimination between the staging groups. The classification in five stages shows better discrimination between groups. However, the BCLC classification performs better than the HKLC classification in the ability to predict overall survival (OS). The HKLC treatment algorithm tags significantly more patients to curative therapy than the BCLC. The BCLC staging system performs better for European patients than the HKLC staging system in predicting OS. Twice more patients are eligible for a curative therapy with the HKLC algorithm; whether this translates in survival benefit remains to be investigated. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A systematic approach to the interpretation of preoperative staging MRI for rectal cancer.

PubMed

Taylor, Fiona G M; Swift, Robert I; Blomqvist, Lennart; Brown, Gina

2008-12-01

The purpose of this article is to provide an aid to the systematic evaluation of MRI in staging rectal cancer. MRI has been shown to be an effective tool for the accurate preoperative staging of rectal cancer. In the Magnetic Resonance Imaging and Rectal Cancer European Equivalence Study (MERCURY), imaging workshops were held for participating radiologists to ensure standardization of scan acquisition techniques and interpretation of the images. In this article, we report how the information was obtained and give examples of the images and how they are interpreted, with the aim of providing a systematic approach to the reporting process.

The postoperative COFAS end-stage ankle arthritis classification system: interobserver and intraobserver reliability.

PubMed

Krause, Fabian G; Di Silvestro, Matthew; Penner, Murray J; Wing, Kevin J; Glazebrook, Mark A; Daniels, Timothy R; Lau, Johnny T C; Younger, Alastair S E

2012-02-01

End-stage ankle arthritis is operatively treated with numerous designs of total ankle replacement and different techniques for ankle fusion. For superior comparison of these procedures, outcome research requires a classification system to stratify patients appropriately. A postoperative 4-type classification system was designed by 6 fellowship-trained foot and ankle surgeons. Four surgeons reviewed blinded patient profiles and radiographs on 2 occasions to determine the interobserver and intraobserver reliability of the classification. Excellent interobserver reliability (κ = .89) and intraobserver reproducibility (κ = .87) were demonstrated for the postoperative classification system. In conclusion, the postoperative Canadian Orthopaedic Foot and Ankle Society (COFAS) end-stage ankle arthritis classification system appears to be a valid tool to evaluate the outcome of patients operated for end-stage ankle arthritis.

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

PubMed

Ramasubbu, Rajamannar; Brown, Matthew R G; Cortese, Filmeno; Gaxiola, Ismael; Goodyear, Bradley; Greenshaw, Andrew J; Dursun, Serdar M; Greiner, Russell

2016-01-01

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers. Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ≥ 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls. The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups. Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Comprehensive Clinical Staging for Resectable Lung Cancer: Clinicopathological Correlations and the Role of Brain MRI.

PubMed

Vernon, Jordyn; Andruszkiewicz, Nicole; Schneider, Laura; Schieman, Colin; Finley, Christian J; Shargall, Yaron; Fahim, Christine; Farrokhyar, Forough; Hanna, Waël C

2016-11-01

In our model of comprehensive clinical staging (CCS) for lung cancer, patients with a computerized tomography scan of the chest and upper abdomen not showing distant metastases will then routinely undergo whole body positron emission tomography/computerized tomography and magnetic resonance imaging (MRI) of the brain before any therapeutic decision. Our aim was to determine the accuracy of CCS and the value of brain MRI in this population. A retrospective analysis of a prospectively entered database was performed for all patients who underwent lung cancer resection from January 2012 to June 2014. Demographics, clinical and pathological stage (seventh edition of the American Joint Committee on Cancer/Union for International Cancer Control tumor, node, and metastasis staging manual), and costs of staging were collected. Correlation between clinical and pathological stage was determined. Of 315 patients with primary lung cancer, 55.6% were female and the mean age was 70 ± 9.6 years. When correlation was analyzed without consideration for substages A and B, 49.8% of patients (158 of 315) were staged accurately, 39.7% (125 of 315) were overstaged, and 10.5% (32 of 315) were understaged. Only 4.7% of patients (15 of 315) underwent surgery without appropriate neoadjuvant treatment. Preoperative brain MRI detected asymptomatic metastases in four of 315 patients (1.3%). At a median postoperative follow-up of 19 months (range 6-43), symptomatic brain metastases developed in seven additional patients. The total cost of CCS in Canadian dollars was $367,292 over the study period, with $117,272 (31.9%) going toward brain MRI. CCS is effective for patients with resectable lung cancer, with less than 5% of patients being denied appropriate systemic treatment before surgery. Brain MRI is a low-yield and high-cost intervention in this population, and its routine use should be questioned. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Preoperative local MRI-staging of patients with a suspected pancreatic mass.

PubMed

Fischer, U; Vosshenrich, R; Horstmann, O; Becker, H; Salamat, B; Baum, F; Grabbe, E

2002-02-01

The aim of this study was to define the value of MRI of the pancreas for preoperative local staging of patients with a suspected pancreatic mass. Ninety-four patients (41 women, 53 men; age range 32-87 years) with a suspected pancreatic tumor underwent preoperative staging with MRI on a 1.5-T system. The MRI protocol included breath-hold MR cholangiopancreatography in turbo spin-echo technique, biphasic contrast-enhanced 3D MR angiography, and MRI of the upper abdomen with breath-hold T2-weighted half-Fourier acquired single-shot turbo spin-echo and T1-weighted fast-low-angle-shot (pre- and postcontrast) sequences. Data were collected prospectively and analyzed by two radiologists in agreement modality. Evaluation criteria were vascular involvement, resectability, and a characterization benign vs malignant. Results were compared to histopathology in 78 patients. Sixteen patients were followed-up. In 74 of 94 patients a solid tumor or an inflammation of the pancreas ( n=62) or the papilla ( n=12) was detected. In this group, MRI had a sensitivity of 98%, a specificity of 92%, and an accuracy of 96% in the characterization of malignant tumors. Regarding only the solid tumors, the positive predictive value of MRI was 87% with respect to resectability. Other pathologic findings included adenoma or inflammation of the duodenum ( n=5), carcinoma or benign stenosis of the choledochus duct ( n=7) and carcinoma of the gall bladder ( n=2). In 6 patients MRI did not depict any pathologic findings, and follow-up confirmed this interpretation. Magnetic resonance imaging allows a local preoperative staging in patients with suspected pancreatic tumor. Limitations, however, concern to the diagnostics of peritoneal and/or liver metastases.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging as a Predictor of Outcome in Head-and-Neck Squamous Cell Carcinoma Patients With Nodal Metastases

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

Shukla-Dave, Amita, E-mail: davea@mskcc.org; Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY; Lee, Nancy Y.

2012-04-01

Purpose: Dynamic contrast-enhanced MRI (DCE-MRI) can provide information regarding tumor perfusion and permeability and has shown prognostic value in certain tumors types. The goal of this study was to assess the prognostic value of pretreatment DCE-MRI in head and neck squamous cell carcinoma (HNSCC) patients with nodal disease undergoing chemoradiation therapy or surgery. Methods and Materials: Seventy-four patients with histologically proven squamous cell carcinoma and neck nodal metastases were eligible for the study. Pretreatment DCE-MRI was performed on a 1.5T MRI. Clinical follow-up was a minimum of 12 months. DCE-MRI data were analyzed using the Tofts model. DCE-MRI parameters weremore » related to treatment outcome (progression-free survival [PFS] and overall survival [OS]). Patients were grouped as no evidence of disease (NED), alive with disease (AWD), dead with disease (DOD), or dead of other causes (DOC). Prognostic significance was assessed using the log-rank test for single variables and Cox proportional hazards regression for combinations of variables. Results: At last clinical follow-up, for Stage III, all 12 patients were NED. For Stage IV, 43 patients were NED, 4 were AWD, 11 were DOD, and 4 were DOC. K{sup trans} is volume transfer constant. In a stepwise Cox regression, skewness of K{sup trans} (volume transfer constant) was the strongest predictor for Stage IV patients (PFS and OS: p <0.001). Conclusion: Our study shows that skewness of K{sup trans} was the strongest predictor of PFS and OS in Stage IV HNSCC patients with nodal disease. This study suggests an important role for pretreatment DCE-MRI parameter K{sup trans} as a predictor of outcome in these patients.« less

Gd-EOB-DTPA-enhanced MRI is better than MDCT in decision making of curative treatment for hepatocellular carcinoma.

PubMed

Yoo, Sun Hong; Choi, Jong Young; Jang, Jeong Won; Bae, Si Hyun; Yoon, Seung Kew; Kim, Dong Goo; Yoo, Young Kyoung; Rha, Sung Eun; Lee, Young Joon; Jung, Eun Sun

2013-09-01

We assessed the change in the therapeutic decision among curative treatments after adding Gd-EOB-DTPA-enhanced MRI to triple-phase MDCT for patients with early-stage HCC. This study retrospectively investigated two groups: 33 pathologically confirmed HCC patients after liver transplantation in group 1; 34 HCC patients without pathology in group 2. In group 1, we simulated the therapeutic decision-making process by pretransplant MDCT and Gd-EOB-DTPA-enhanced MRI. In group 2, including the 34 early-stage HCC patients consecutively enrolled, we investigated the change of therapeutic decision after adding Gd-EOB-DTPA-enhanced MRI to MDCT. In the simulation from group 1, after adding Gd-EOB-DTPA-enhanced MRI, 33.3% (11/33 patients) of treatment decisions were changed from the decision based on MDCT alone. Among 22 patients considered eligible for resection and 33 patients for radiofrequency ablation, the therapeutic decision was changed for 10 patients in the surgical group and 4 patients for the RFA group (45.5 and 12.1%). In group 2, the rate of change in the therapeutic decision after adding Gd-EOB-DTPA-enhanced MRI to MDCT was 41.2% (14/34 patients). In group 1 with explants pathology, the median diameter of HCCs not detected by MDCT but detected by Gd-EOB-DTPA-enhanced MRI was 1.15 cm (0.3-3.0 cm). The median diameter of HCCs seen only in the explanted liver was 1.0 cm (0.3-1.7 cm), and 60.7% of them were well-differentiated HCCs. This study suggests that performing Gd-EOB-DTPA-enhanced MRI before deciding on curative treatment for early-stage HCC may improve the accuracy of treatment decision for early-stage HCC.

[Contribution of MRI to the preoperative evaluation of rotator cuff tears].

PubMed

Gagey, N; Desmoineaux, P; Gagey, O; Idy-Peretti, I; Mazas, F

1991-01-01

The authors report a series of 38 patients who had been examined by MRI and then operated for a rotator cuff syndrome. The correlation between the description of the cuff lesions after MRI and the surgical observations were excellent for 37 patients. In one case MRI showed a false image of tear of the supra spinatus m. on its anterior edge. This was due to a bad knowledge of the anatomy of the muscle and tendon and to a poor orientation of the frontal cut plane. This study was completed with MRI and anatomic study of 12 non embalmed cadaveric shoulders. The results showed that MRI was very sensitive (0.93) and specific (0.94) for the diagnosis of rotator cuff tears. MRI allowed also to show partial tears of the tendons of the rotator cuff. The authors propose a MRI classification of cuff lesions which permits to establish a good surgical planning.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach

NASA Astrophysics Data System (ADS)

Litjens, G. J. S.; Barentsz, J. O.; Karssemeijer, N.; Huisman, H. J.

2012-03-01

MRI has shown to have great potential in prostate cancer localization and grading, but interpreting those exams requires expertise that is not widely available. Therefore, CAD applications are being developed to aid radiologists in detecting prostate cancer. Existing CAD applications focus on the prostate as a whole. However, in clinical practice transition zone cancer and peripheral zone cancer are considered to have different appearances. In this paper we present zone-specific CAD, in addition to an atlas based segmentation technique which includes zonal segmentation. Our CAD system consists of a detection and a classification stage. Prior to the detection stage the prostate is segmented into two zones. After segmentation features are extracted. Subsequently a likelihood map is generated on which local maxima detection is performed. For each local maximum a region is segmented. In the classification stage additional shape features are calculated, after which the regions are classified. Validation was performed on 288 data sets with MR-guided biopsy results as ground truth. Freeresponse Receiver Operating Characteristic (FROC) analysis was used for statistical evaluation. The difference between whole-prostate and zone-specific CAD was assessed using the difference between the FROCs. Our results show that evaluating the two zones separately results in an increase in performance compared to whole-prostate CAD. The FROC curves at .1, 1 and 3 false positives have a sensitivity of 0.0, 0.55 and 0.72 for whole-prostate and 0.08, 0.57 and 0.80 for zone-specific CAD. The FROC curve of the zone-specific CAD also showed significantly better performance overall (p < 0.05).

Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation

PubMed Central

Rubia, Katya

2018-01-01

This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent clinically relevant applications such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation. Meta-analyses of fMRI studies of executive functions (EFs) show that ADHD patients have cognitive-domain dissociated complex multisystem impairments in several right and left hemispheric dorsal, ventral and medial fronto-cingulo-striato-thalamic and fronto-parieto-cerebellar networks that mediate cognitive control, attention, timing and working memory (WM). There is furthermore emerging evidence for abnormalities in orbital and ventromedial prefrontal and limbic areas that mediate motivation and emotion control. In addition, poor deactivation of the default mode network (DMN) suggests an abnormal interrelationship between hypo-engaged task-positive and poorly “switched off” hyper-engaged task-negative networks, both of which are related to impaired cognition. Translational cognitive neuroscience in ADHD is still in its infancy. Pattern recognition analyses have attempted to provide diagnostic classification of ADHD using fMRI data with respectable classification accuracies of over 80%. Necessary replication studies, however, are still outstanding. Brain stimulation has been tested in heterogeneously designed, small numbered proof of concept studies targeting key frontal functional impairments in ADHD. Transcranial direct current stimulation (tDCS) appears to be promising to improve ADHD symptoms and cognitive functions based on some studies, but larger clinical trials of repeated stimulation with and without cognitive training are needed to test clinical efficacy and potential costs on non-targeted functions. Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which, however, were not superior to the active control conditions, suggesting potential placebo effects. Neurotherapeutics seems attractive for ADHD due to their safety and potential longer-term neuroplastic effects, which drugs cannot offer. However, they need to be thoroughly tested for short- and longer-term clinical and cognitive efficacy and their potential for individualized treatment. PMID:29651240

Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation.

PubMed

Rubia, Katya

2018-01-01

This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent clinically relevant applications such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation. Meta-analyses of fMRI studies of executive functions (EFs) show that ADHD patients have cognitive-domain dissociated complex multisystem impairments in several right and left hemispheric dorsal, ventral and medial fronto-cingulo-striato-thalamic and fronto-parieto-cerebellar networks that mediate cognitive control, attention, timing and working memory (WM). There is furthermore emerging evidence for abnormalities in orbital and ventromedial prefrontal and limbic areas that mediate motivation and emotion control. In addition, poor deactivation of the default mode network (DMN) suggests an abnormal interrelationship between hypo-engaged task-positive and poorly "switched off" hyper-engaged task-negative networks, both of which are related to impaired cognition. Translational cognitive neuroscience in ADHD is still in its infancy. Pattern recognition analyses have attempted to provide diagnostic classification of ADHD using fMRI data with respectable classification accuracies of over 80%. Necessary replication studies, however, are still outstanding. Brain stimulation has been tested in heterogeneously designed, small numbered proof of concept studies targeting key frontal functional impairments in ADHD. Transcranial direct current stimulation (tDCS) appears to be promising to improve ADHD symptoms and cognitive functions based on some studies, but larger clinical trials of repeated stimulation with and without cognitive training are needed to test clinical efficacy and potential costs on non-targeted functions. Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which, however, were not superior to the active control conditions, suggesting potential placebo effects. Neurotherapeutics seems attractive for ADHD due to their safety and potential longer-term neuroplastic effects, which drugs cannot offer. However, they need to be thoroughly tested for short- and longer-term clinical and cognitive efficacy and their potential for individualized treatment.

Classification of High Intensity Zones of the Lumbar Spine and Their Association with Other Spinal MRI Phenotypes: The Wakayama Spine Study.

PubMed

Teraguchi, Masatoshi; Samartzis, Dino; Hashizume, Hiroshi; Yamada, Hiroshi; Muraki, Shigeyuki; Oka, Hiroyuki; Cheung, Jason Pui Yin; Kagotani, Ryohei; Iwahashi, Hiroki; Tanaka, Sakae; Kawaguchi, Hiroshi; Nakamura, Kozo; Akune, Toru; Cheung, Kenneth Man-Chee; Yoshimura, Noriko; Yoshida, Munehito

2016-01-01

High intensity zones (HIZ) of the lumbar spine are a phenotype of the intervertebral disc noted on MRI whose clinical relevance has been debated. Traditionally, T2-weighted (T2W) magnetic resonance imaging (MRI) has been utilized to identify HIZ of lumbar discs. However, controversy exists with regards to HIZ morphology, topography, and association with other MRI spinal phenotypes. Moreover, classification of HIZ has not been thoroughly defined in the past and the use of additional imaging parameters (e.g. T1W MRI) to assist in defining this phenotype has not been addressed. A cross-sectional study of 814 (69.8% females) subjects with mean age of 63.6 years from a homogenous Japanese population was performed. T2W and T1W sagittal 1.5T MRI was obtained on all subjects to assess HIZ from L1-S1. We created a morphological and topographical HIZ classification based on disc level, shape type (round, fissure, vertical, rim, and enlarged), location within the disc (posterior, anterior), and signal type on T1W MRI (low, high and iso intensity) in comparison to the typical high intensity on T2W MRI. HIZ was noted in 38.0% of subjects. Of these, the prevalence of posterior, anterior, and both posterior/anterior HIZ in the overall lumbar spine were 47.3%, 42.4%, and 10.4%, respectively. Posterior HIZ was most common, occurring at L4/5 (32.5%) and L5/S1 (47.0%), whereas anterior HIZ was most common at L3/4 (41.8%). T1W iso-intensity type of HIZ was most prevalent (71.8%), followed by T1W high-intensity (21.4%) and T1W low-intensity (6.8%). Of all discs, round types were most prevalent (anterior: 3.6%, posterior: 3.7%) followed by vertical type (posterior: 1.6%). At all affected levels, there was a significant association between HIZ and disc degeneration, disc bulge/protrusion and Modic type II (p<0.01). Posterior HIZ and T1W high-intensity type of HIZ were significantly associated with disc bulge/protrusion and disc degeneration (p<0.01). In addition, posterior HIZ was significantly associated with Modic type II and III. T1W low-intensity type of HIZ was significantly associated with Modic type II. This is the first large-scale study reporting a novel classification scheme of HIZ of the lumbar spine. This study is the first that has utilized T2W and T1W MRIs in differentiating HIZ sub-phenotypes. Specific HIZ sub-phenotypes were found to be more associated with specific MRI degenerative changes. With a more detailed description of the HIZ phenotype, this scheme can be standardized for future clinical and research initiatives.

TFM classification and staging of oral submucous fibrosis: A new proposal.

PubMed

Arakeri, Gururaj; Thomas, Deepak; Aljabab, Abdulsalam S; Hunasgi, Santosh; Rai, Kirthi Kumar; Hale, Beverley; Fonseca, Felipe Paiva; Gomez, Ricardo Santiago; Rahimi, Siavash; Merkx, Matthias A W; Brennan, Peter A

2018-04-01

We have evaluated the rationale of existing grading and staging schemes of oral submucous fibrosis (OSMF) based on how they are categorized. A novel classification and staging scheme is proposed. A total of 300 OSMF patients were evaluated for agreement between functional, clinical, and histopathological staging. Bilateral biopsies were assessed in 25 patients to evaluate for any differences in histopathological staging of OSMF in the same mouth. Extent of clinician agreement for categorized staging data was evaluated using Cohen's weighted kappa analysis. Cross-tabulation was performed on categorical grading data to understand the intercorrelation, and the unweighted kappa analysis was used to assess the bilateral grade agreement. Probabilities of less than 0.05 were considered significant. Data were analyzed using SPSS Statistics (version 25.0, IBM, USA). A low agreement was found between all the stages depicting the independent nature of trismus, clinical features, and histopathological components (K = 0.312, 0.167, 0.152) in OSMF. Following analysis, a three-component classification scheme (TFM classification) was developed that describes the severity of each independently, grouping them using a novel three-tier staging scheme as a guide to the treatment plan. The proposed classification and staging could be useful for effective communication, categorization, and for recording data and prognosis, and for guiding treatment plans. Furthermore, the classification considers OSMF malignant transformation in detail. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

PubMed

Duan, Xiaohui; Ban, Xiaohua; Zhang, Xiang; Hu, Huijun; Li, Guozhao; Wang, Dongye; Wang, Charles Qian; Zhang, Fang; Shen, Jun

2016-12-01

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10 -3  mm 2 /s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

Clinical values of (18) F-FDG PET/CT in oral cavity cancer with dental artifacts on CT or MRI.

PubMed

Hong, Hye Ran; Jin, Soyoung; Koo, Hyun Jung; Roh, Jong-Lyel; Kim, Jae Seung; Cho, Kyung-Ja; Choi, Seung-Ho; Nam, Soon Yuhl; Kim, Sang Yoon

2014-11-01

2a To investigate the role of (18) F-FDG PET/CT in tumor staging, extent, and volume measurements in oral cavity squamous cell carcinoma (OSCC) patients with/without dental artifacts on CT or MRI. This study was conducted in 63 consecutive patients with OSCC who received initial workups including (18) F-FDG PET/CT and MRI. The results of the imaging modalities were compared to those of pathology, using McNemar's test and the paired t-test. Thirty-seven patients (59%) had dental or metallic artifacts obscuring primary tumors. (18) F-FDG PET/CT scanning was superior to MRI in tumor staging (weighted κ = 0.870 vs. 0.518, P = 0.004) in patients with dental artifacts. In addition, (18) F-FDG PET/CT scans were more specific than MRI in detecting sublingual gland (P = 0.014) and mouth floor (P = 0.011) involvement. In patients with dental artifacts, there was a significant discrepancy between primary tumor volume (PTV) measured by pathology and MRI (P = 0.018), but not between PTV measured from pathology and (18) F-FDG PET/CT at SUV2.5 (P = 0.245), which showed the highest intraclass correlation coefficient value (0.860). (18) F-FDG PET/CT scans provide accurate tumor staging and volume measurements in OSCC patients with CR/MRI dental artifacts, leading to improved preoperative planning. 2b CONDENSED ABSTRACT This study evaluated the clinical value of (18) F-FDG PET/CT in 63 patients with oral cavity cancers. In 37 (59%) patients with dental artifacts on CT/MRI, (18) F-FDG PET/CT showed superior results compared to MRI in tumor staging and represented the highest intraclass correlation coefficient value to tumor volume determined by pathology. © 2014 Wiley Periodicals, Inc.

Simultaneous whole-body 18F-PSMA-1007-PET/MRI with integrated high-resolution multiparametric imaging of the prostatic fossa for comprehensive oncological staging of patients with prostate cancer: a pilot study.

PubMed

Freitag, Martin T; Kesch, Claudia; Cardinale, Jens; Flechsig, Paul; Floca, Ralf; Eiber, Matthias; Bonekamp, David; Radtke, Jan P; Kratochwil, Clemens; Kopka, Klaus; Hohenfellner, Markus; Stenzinger, Albrecht; Schlemmer, Heinz-Peter; Haberkorn, Uwe; Giesel, Frederik

2018-03-01

The aim of the present study was to explore the clinical feasibility and reproducibility of a comprehensive whole-body 18 F-PSMA-1007-PET/MRI protocol for imaging prostate cancer (PC) patients. Eight patients with high-risk biopsy-proven PC underwent a whole-body PET/MRI (3 h p.i.) including a multi-parametric prostate MRI after 18 F-PSMA-1007-PET/CT (1 h p.i.) which served as reference. Seven patients presented with non-treated PC, whereas one patient presented with biochemical recurrence. SUV mean -quantification was performed using a 3D-isocontour volume-of-interest. Imaging data was consulted for TNM-staging and compared with histopathology. PC was confirmed in 4/7 patients additionally by histopathology after surgery. PET-artifacts, co-registration of pelvic PET/MRI and MRI-data were assessed (PI-RADS 2.0). The examinations were well accepted by patients and comprised 1 h. SUV mean -values between PET/CT (1 h p.i.) and PET/MRI (3 h p.i.) were significantly correlated (p < 0.0001, respectively) and similar to literature of 18 F-PSMA-1007-PET/CT 1 h vs 3 h p.i. The dominant intraprostatic lesion could be detected in all seven patients in both PET and MRI. T2c, T3a, T3b and T4 features were detected complimentarily by PET and MRI in five patients. PET/MRI demonstrated moderate photopenic PET-artifacts surrounding liver and kidneys representing high-contrast areas, no PET-artifacts were observed for PET/CT. Simultaneous PET-readout during prostate MRI achieved optimal co-registration results. The presented 18 F-PSMA-1007-PET/MRI protocol combines efficient whole-body assessment with high-resolution co-registered PET/MRI of the prostatic fossa for comprehensive oncological staging of patients with PC.

Preoperative magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study.

PubMed

Taylor, Fiona G M; Quirke, Philip; Heald, Richard J; Moran, Brendan J; Blomqvist, Lennart; Swift, Ian R; Sebag-Montefiore, David; Tekkis, Paris; Brown, Gina

2014-01-01

The prognostic relevance of preoperative high-resolution magnetic resonance imaging (MRI) assessment of circumferential resection margin (CRM) involvement is unknown. This follow-up study of 374 patients with rectal cancer reports the relationship between preoperative MRI assessment of CRM staging, American Joint Committee on Cancer (AJCC) TNM stage, and clinical variables with overall survival (OS), disease-free survival (DFS), and time to local recurrence (LR). Patients underwent protocol high-resolution pelvic MRI. Tumor distance to the mesorectal fascia of ≤ 1 mm was recorded as an MRI-involved CRM. A Cox proportional hazards model was used in multivariate analysis to determine the relationship of MRI assessment of CRM to survivorship after adjusting for preoperative covariates. Surviving patients were followed for a median of 62 months. The 5-year OS was 62.2% in patients with MRI-clear CRM compared with 42.2% in patients with MRI-involved CRM with a hazard ratio (HR) of 1.97 (95% CI, 1.27 to 3.04; P < .01). The 5-year DFS was 67.2% (95% CI, 61.4% to 73%) for MRI-clear CRM compared with 47.3% (95% CI, 33.7% to 60.9%) for MRI-involved CRM with an HR of 1.65 (95% CI, 1.01 to 2.69; P < .05). Local recurrence HR for MRI-involved CRM was 3.50 (95% CI, 1.53 to 8.00; P < .05). MRI-involved CRM was the only preoperative staging parameter that remained significant for OS, DFS, and LR on multivariate analysis. High-resolution MRI preoperative assessment of CRM status is superior to AJCC TNM-based criteria for assessing risk of LR, DFS, and OS. Furthermore, MRI CRM involvement is significantly associated with distant metastatic disease; therefore, colorectal cancer teams could intensify treatment and follow-up accordingly to improve survival outcomes.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-11-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-01-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)1, a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. PMID:25042445

PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

PubMed Central

Zhou, Zhen; Wang, Jian-Bao; Zang, Yu-Feng; Pan, Gang

2018-01-01

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies. PMID:29375288

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

The Role of Endorectal Coil MRI in the management of patients with prostate cancer and in determining radical prostatectomy surgical margin status: A report of a single surgeon's practice

PubMed Central

Zhang, Jianqing; Loughlin, Kevin R.; Zou, Kelly H.; Haker, Steven; Tempany, Clare M.C.

2009-01-01

Objective To evaluate the role of combination of endorectal coil and external multicoil array MRI in the management of prostate cancer and predicting the surgical margin status in a single surgical practice. Materials and Methods We reviewed all patients referred by a single surgeon from January 1993 to May 2002 for staging prostate MRI prior to selecting treatment. All MRI examinations were performed using 1.5T (Signa; GE Medical Systems) with a combination of endorectal and pelvic multi-coil array. The tumor size, stage and total gland volume on MR, PSA and Gleason grade were all compared with the pathological stage and diagnosis of positive surgical margin (PSM). Result A total of 232 patients were evaluated, of which 110 underwent radical prostatectomy all performed by one surgeon (Group 1), and 122 did not (Group 2). The results showed MRI stage, PSA and age, all significantly different (P<0.001). In Group 1, the results showed a high specificity (99%) and accuracy (91%) of the MRI staging T3. Post-surgical follow up (median 4.5 years) showed 90% of men had PSA levels below 0.1ng/ml. The positive surgical margin (PSM) rate was 16%. There was no significant difference found on MR imaging between PSM group and non-PSM group. A single tumor length above 1.8cm was the cut point above which there was PSM (P=0.002). Conclusion In conclusion, the combined use of clinical data and endorectal MR imaging can help optimize patient management and selection for surgery, and in a single surgeon's practice lead to successful outcomes. PMID:17572201

Planning magnetic resonance imaging for prostate cancer intensity-modulated radiation therapy: Impact on target volumes, radiotherapy dose and androgen deprivation administration.

PubMed

Horsley, Patrick J; Aherne, Noel J; Edwards, Grace V; Benjamin, Linus C; Wilcox, Shea W; McLachlan, Craig S; Assareh, Hassan; Welshman, Richard; McKay, Michael J; Shakespeare, Thomas P

2015-03-01

Magnetic resonance imaging (MRI) scans are increasingly utilized for radiotherapy planning to contour the primary tumors of patients undergoing intensity-modulated radiation therapy (IMRT). These scans may also demonstrate cancer extent and may affect the treatment plan. We assessed the impact of planning MRI detection of extracapsular extension, seminal vesicle invasion, or adjacent organ invasion on the staging, target volume delineation, doses, and hormonal therapy of patients with prostate cancer undergoing IMRT. The records of 509 consecutive patients with planning MRI scans being treated with IMRT for prostate cancer between January 2010 and July 2012 were retrospectively reviewed. Tumor staging and treatment plans before and after MRI were compared. Of the 509 patients, 103 (20%) were upstaged and 44 (9%) were migrated to a higher risk category as a result of findings at MRI. In 94 of 509 patients (18%), the MRI findings altered management. Ninety-four of 509 patients (18%) had a change to their clinical target volume (CTV) or treatment technique, and in 41 of 509 patients (8%) the duration of hormone therapy was changed because of MRI findings. The use of radiotherapy planning MRI altered CTV design, dose and/or duration of androgen deprivation in 18% of patients in this large, single institution series of men planned for dose-escalated prostate IMRT. This has substantial implications for radiotherapy target volumes and doses, as well as duration of androgen deprivation. Further research is required to investigate whether newer MRI techniques can simultaneously fulfill staging and radiotherapy contouring roles. © 2014 Wiley Publishing Asia Pty Ltd.

[The clinical classification of acute otitis media with special reference to tympanometry].

PubMed

Subbotina, M V

We have developed a new clinical classification of acute otitis media (AOM) based on the previously proposed classifications of V.T. Palchun with co-workers (1997) and J. Jeger (1970) in which the letter near the stage of the pathological process roughly corresponds to the type of the tympanogram as follows: stage I (acute tubootitis): A, B, C; stage II (acute catarrhal otitis media): A, B, C; stage III (acute purulent otitis media, perforation stage); stage IV (acute purulent otitis media, post-perforation stage); stage V (resolution of otitis media): A - convalescence or recovery, B1 - exudate present in the tympanic cavity; B2 - persisting perforation; C - block of the auditory tube, O - the development of complications. This classification implies the necessity of tympanometry at the stage of diagnostics of AOM although it is not mandatory because the detection of exudate as a result of paracentesis at any of the stages of otitis media will allow to designate the stage of otitis either by letter A, B or C. The application of the new classification described in this article permits to more accurately than before determine the character of the pathological process in the middle ear during the course of acute otitis media which is of special importance in the clinical pediatric practice for the timely and adequate treatment of the children.

Classification of clinical significance of MRI prostate findings using 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Sedghi, Alireza; Ghafoorian, Mohsen; Taghipour, Mehdi; Tempany, Clare M.; Wells, William M.; Kapur, Tina; Mousavi, Parvin; Abolmaesumi, Purang; Fedorov, Andriy

2017-03-01

Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.

Using Computer-extracted Image Phenotypes from Tumors on Breast MRI to Predict Breast Cancer Pathologic Stage

PubMed Central

Burnside, Elizabeth S.; Drukker, Karen; Li, Hui; Bonaccio, Ermelinda; Zuley, Margarita; Ganott, Marie; Net, Jose M.; Sutton, Elizabeth; Brandt, Kathleen R.; Whitman, Gary; Conzen, Suzanne; Lan, Li; Ji, Yuan; Zhu, Yitan; Jaffe, Carl; Huang, Erich; Freymann, John; Kirby, Justin; Morris, Elizabeth; Giger, Maryellen

2015-01-01

Background To demonstrate that computer-extracted image phenotypes (CEIPs) of biopsy-proven breast cancer on MRI can accurately predict pathologic stage. Methods We used a dataset of de-identified breast MRIs organized by the National Cancer Institute in The Cancer Imaging Archive. We analyzed 91 biopsy-proven breast cancer cases with pathologic stage (stage I = 22; stage II = 58; stage III = 11) and surgically proven nodal status (negative nodes = 46, ≥ 1 positive node = 44, no nodes examined = 1). We characterized tumors by (a) radiologist measured size, and (b) CEIP. We built models combining two CEIPs to predict tumor pathologic stage and lymph node involvement, evaluated them in leave-one-out cross-validation with area under the ROC curve (AUC) as figure of merit. Results Tumor size was the most powerful predictor of pathologic stage but CEIPs capturing biologic behavior also emerged as predictive (e.g. stage I+II vs. III demonstrated AUC = 0.83). No size measure was successful in the prediction of positive lymph nodes but adding a CEIP describing tumor “homogeneity,” significantly improved this discrimination (AUC = 0.62, p=.003) over chance. Conclusions Our results indicate that MRI phenotypes show promise for predicting breast cancer pathologic stage and lymph node status. PMID:26619259

Lymphadenitis associated with cat-scratch disease simulating a neoplasm: Imaging findings with histopathological associations.

PubMed

Chen, Ying; Fu, Yan-Biao; Xu, Xiu-Fang; Pan, Yao; Lu, Chen-Ying; Zhu, Xiu-Liang; Li, Qing-Hai; Yu, Ri-Sheng

2018-01-01

The lymphadenitis associated with cat-scratch disease (CSD) is often confused with neoplasms by a number of radiologists and clinicians, and consequently, unnecessary invasive procedures or surgeries are performed. In the present study, the contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) findings of 10 patients (6 men and 4 women) with clinically and pathologically confirmed lymphadenitis associated with CSD were retrospectively analyzed (CT in 3 patients, MRI in 6 patients, and CT and MRI in 1 patient) at The Second Affiliated Hospital of Zhejiang University School of Medicine (Hangzhou, China) between January 2007 and November 2014. As a result, 17 enlarged lymph nodes were identified in 10 cases. The 5 nodes identified by CT scan exhibited relatively inhomogeneous isodensity to muscle, with patchy low density in the center. All 14 nodes identified by MRI scan exhibited homogeneous or heterogeneous isointensity to muscle or slightly increased intensity compared with that of muscle on T1-weighted images (T1WI), and homogeneous or heterogeneous hyperintensity on fat-suppressed T2WI. Following enhancement, all 17 enlarged lymph nodes associated with CSD demonstrated the following 3 different enhancement patterns: Moderate homogeneous enhancement (n=8), which was associated with histologically identified early disease stage; marked heterogeneous enhancement with no enhancement of the necrotic areas (n=4), and heterogeneous enhancement with progressively 'spoke-wheel-like' (defined as radiating enhancement from the center) enhancement of the patchy low-density area (n=1), which was associated with histologically identified intermediate disease stage; and astral low-density/hypointensity with marked enhancement (n=2) or a 'rose flower' sign (n=2), which was associated with histologically identified late disease stage. We hypothesized that the CT and MRI results of lymphadenitis in CSD may be associated with the pathological features. It may be suggested that the diagnosis of CSD may be formed when considering the characteristic CT and MRI features of astral low-density/hypointensity with marked enhancement or a 'rose flower' sign (defined as marginal petaloid enhancement) in the late disease stage, or the MRI results of homogeneous, moderate enhancement in the early disease stage, or the CT/MRI data of heterogeneous enhancement with non-enhancing area in the center in the intermediate disease stage, in solitary or multiple enlarged lymph nodes associated with general subcutaneous edema in the vicinity of the nodes on CT/MRI and with a history of cat exposure.

TU-G-201-01: What Therapy Physicists Need to Know About CT and PET/CT: Terminology and Latest Developments

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

Hua, C.

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

TU-G-201-00: Imaging Equipment Specification and Selection in Radiation Oncology Departments

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

NONE

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment. The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, andmore » potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts. The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4-zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed. In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment. Learning Objectives: Learn the latest innovations of major imaging systems relevant to radiation therapy Be able to describe important technical specifications of CT, MRI, and PET/CT Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development.« less

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Use of Magnetic Resonance Imaging for N-Staging in Patients with Non-Small Cell Lung Cancer. A Systematic Review.

PubMed

Brea, Tara Pereiro; Raviña, Alberto Ruano; Villamor, José Martín Carreira; Gómez, Antonio Golpe; de Alegría, Anxo Martínez; Valdés, Luís

2018-05-23

The aim of this study is to assess the diagnostic value of the magnetic resonance imaging (MRI) in differentiating metastasic from non-metastatic lymph nodes in NSCLC patients compared with computed tomography (CT) and fluorodeoxyglucose (FDG) - positron emission tomography (PET) or both combined. Twenty-three studies (19 studies and 4 meta-analysis) with sample size ranging between 22 and 250 patients were included in this analysis. MRI, regardless of the sequence obtained, where used for the evaluation of N-staging of NSCLC. Histopathology results and clinical or imaging follow-up were used as the reference standard. Studies were excluded if the sample size was less than 20 cases, if less than 10 lymph nodes assessment were presented or studies where standard reference was not used. Papers not reporting sufficient data were also excluded. As compared to CT and PET, MRI demonstrated a higher sensitivity, specificity and diagnostic accuracy in the diagnosis of metastatic or non-metastatic lymph nodes in N-staging in NSCLC patients. No study considered MRI inferior than conventional techniques (CT, PET or PET/CT). Other outstanding results of this review are fewer false positives with MRI in comparison with PET, their superiority over PET/CT to detect non-resectable lung cancer, to diagnosing infiltration of adjacent structures or brain metastasis and detecting small nodules. MRI has shown at least similar or better results in diagnostic accuracy to differentiate metastatic from non-metastatic mediastinal lymph nodes. This suggests that MRI could play a significant role in mediastinal NSCLC staging. Copyright © 2018 SEPAR. Publicado por Elsevier España, S.L.U. All rights reserved.

Shoulder-Mounted Robot for MRI-guided arthrography: Accuracy and mounting study.

PubMed

Monfaredi, R; Wilson, E; Sze, R; Sharma, K; Azizi, B; Iordachita, I; Cleary, K

2015-08-01

A new version of our compact and lightweight patient-mounted MRI-compatible 4 degree-of-freedom (DOF) robot for MRI-guided arthrography procedures is introduced. This robot could convert the traditional two-stage arthrography procedure (fluoroscopy-guided needle insertion followed by a diagnostic MRI scan) to a one-stage procedure, all in the MRI suite. The results of a recent accuracy study are reported. A new mounting technique is proposed and the mounting stability is investigated using optical and electromagnetic tracking on an anthropomorphic phantom. Five volunteer subjects including 2 radiologists were asked to conduct needle insertion in 4 different random positions and orientations within the robot's workspace and the displacement of the base of the robot was investigated during robot motion and needle insertion. Experimental results show that the proposed mounting method is stable and promising for clinical application.

Hybrid PET/MR imaging in two sarcoma patients - clinical benefits and implications for future trials.

PubMed

Partovi, Sasan; Kohan, Andres A; Zipp, Lisa; Faulhaber, Peter; Kosmas, Christos; Ros, Pablo R; Robbin, Mark R

2014-01-01

PET/MRI is an evolving hybrid imaging modality which combines the inherent strengths of MRIs soft-tissue and contrast resolution and PETs functional metabolic capabilities. Bone and soft-tissue sarcoma are a relatively rare tumor entity, relying on MRI for local staging and often on PET/CT for lymph node involvement and metastatic spread evaluation. The purpose of this article is to demonstrate the successful use of PET/MRI in two sarcoma patients. We also use these patients as a starting point to discuss how PET/MRI might be of value in sarcoma. Among its potential benefits are: superior TNM staging than either modality alone, decreased radiation dose, more sensitive and specific follow-up and better assessment of treatment response. These potentials need to be investigated in future PET/MRI soft-tissue sarcoma trials.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

PubMed

Liu, Manhua; Cheng, Danni; Wang, Kundong; Wang, Yaping

2018-03-23

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93 AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.

SU-E-J-226: Efficient Use of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for Cervical-Cancer Brachytherapy

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

Damato, A; Bhagwat, M; Buzurovic, I

Purpose: To investigate image modality selection in an environment with limited access to interventional MRI for image-guided high-dose-rate cervical-cancer brachytherapy. Methods: Records of all cervical-cancer patients treated with brachytherapy between 1/2013 and 8/2014 were analyzed. Insertions were performed under CT guidance (CT group) or with >1 fraction under 3T MR guidance (MRI group; subMRI includes only patients who also had a CT-guided insertion). Differences between groups in clinical target volume (CTV), disease stage (I/II or III/IV), number of patients with or without interstitial needles, and CTV D90 were investigated. Statistical significance was evaluated with the Student T test and Fishermore » test (p <0.05). Results: 46 cervical-cancer patients were included (16 MRI [3 subMRI], 30 CT). CTV: overall, 55±53 cm3; MRI, 81±61 cm3; CT, 42±44 cm3 (p = 0.017). Stage: overall, 24 I/II and 22 III/IV; MRI, 3 I/II and 13 III/IV; CT, 21 I/II and 9 III/IV (p = 0.002). Use of needles: overall, 26 without and 20 with; MRI, 5 without and 11 with; CT, 21 without and 9 with (p = 0.015). CTV D90: overall, 82±5 Gy; MRI, 81±6 Gy; CT, 82±5 Gy (p = 0.78). SubMRI: CTV and D90 (as % of nominal fraction dose) were 23±6 cm3 and 124±3% for MRI-guided insertions and 21±5 cm3 (p = 0.83) and 106±12% (p = 0.15) for CT-guided insertions. Conclusion: Statistically significant differences in patient population indicate preferential use of MRI for patients with high-stage disease and large residual CTVs requiring the use of interstitial needles. CTV D90 was similar between groups, despite the difference in patient selection. For patients who underwent both CT and MRI insertions, a larger MR CTV D90 and similar CTVs between insertions were observed. While MRI is generally preferable to CT, MRI selection can be optimized in environments without a dedicated MRI brachytherapy suite. This work was partially funded by the NIH R21 CA167800 (PI: Viswanathan; aviswanathan@partners.org)« less

Estadiaje local del carcinoma rectal: imágenes de ecografía versus resonancia magnética. Revisión sistemática de la literatura y metaanálisis.

PubMed

Guenaga, Katia F; Otoch, Jose P; Artifon, Everson L A

2016-01-01

New surgical techniques in the treatment of rectal cancer have improved survival mainly by reducing local recurrences. A preoperative staging method is required to accurately identify tumor stage and planning the appropriate treatment. MRI and ERUS are currently being used for the local staging (T stage). In this review, the accuracy of MRI and ERUS with rigid probe was compared against the gold standard of the pathological findings in the resection specimens. Five studies met the inclusion criteria and were included in this meta-analysis. The accuracy was 91.0% to ERUS and 86.8% to MRI (p=0.27). The result has no statistical significance but with pronounced heterogeneity between the included trials as well as other published reviews. We can conclude that there is a clear need for good quality, larger scale and prospective studies.

Risk factors contributing to a poor prognosis of papillary thyroid carcinoma: validity of UICC/AJCC TNM classification and stage grouping.

PubMed

Ito, Yasuhiro; Miyauchi, Akira; Jikuzono, Tomoo; Higashiyama, Takuya; Takamura, Yuuki; Miya, Akihiro; Kobayashi, Kaoru; Matsuzuka, Fumio; Ichihara, Kiyoshi; Kuma, Kanji

2007-04-01

In 2002, the UICC/AJCC TNM classification for papillary thyroid carcinoma was revised. In this study, we examined the validity of this classification system by investigating the predictors of disease-free survival (DFS) and cause-specific survival (CSS) in patients. We examined various clinicopathological features, including the component of the TNM classification, for 1,740 patients who underwent initial and curative surgery for papillary carcinoma between 1987 and 1995. Clinical and pathological T4a, clinical N1b in the TNM classification, and patient age were recognized as independent predictors of not only DFS, but also CSS of patients. Tumor size, male gender, and central node metastasis independently affected DFS only. There were 1,005 pathological N1b patients, but pathological N1b did not independently affect either DFS or CSS. Regarding the stage grouping, clinical stage IVA including clinical N1b more clearly affected DFS and CSS than pathological stage IVA including pathological N1b. Clinical stage grouping was more useful than pathological stage grouping for predicting the prognosis of papillary carcinoma patients possibly because pathological stage overestimates the biological characteristics of many pathological N1b tumors.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

MRI assessment of local acute radiation syndrome.

PubMed

Weber-Donat, G; Amabile, J-C; Lahutte-Auboin, M; Potet, J; Baccialone, J; Bey, E; Teriitehau, C; Laroche, P

2012-12-01

To describe local acute radiation syndrome and its radiological imaging characteristics. We performed a retrospective study of patients who had suffered skin and deeper radiation damage who were investigated by magnetic resonance imaging (MRI). We compared the clinical findings, C-reactive protein (CRP) levels and MRI results. A total of 22 MRI examinations were performed between 2005 and 2010 in 7 patients; 6 patients had increased CRP levels and MRI abnormalities. They were treated by surgery and local cellular therapy. One patient had no CRP or MRI abnormalities, and had a spontaneous good outcome. Eighteen abnormal MR examinations demonstrated high STIR signal and/or abnormal enhancement in the dermis and muscle tissues. Three MRI examinations demonstrated skeletal abnormalities, consistent with radionecrosis. The four normal MRI examinations were associated only with minor clinical manifestations such as pain and pigmentation disorders. MRI seems to be a useful and promising imaging investigation in radiation burns management i.e. initial lesion evaluation, treatment evaluation and complication diagnosis. MRI findings correlated perfectly with clinical stage and no false negative examinations were obtained. In particular, the association between normal MRI and low CRP level seems to be related to good outcome without specific treatment. Local acute radiation syndrome (radioepidermitis) mainly affects the skin and superficial tissues. MRI findings correspond with clinical stage (with a strong negative predictive value). MRI outperformed X-ray examination for the diagnosis of bone radionecrosis. Diffusion-weighted imaging shows low ADC in bone and soft tissue necrosis. Perfusion sequence allows assessment of tissue microcirculation impairment.

Prophylactic Cranial Irradiation (PCI) versus Active MRI Surveillance for Small Cell Lung Cancer: The Case for Equipoise.

PubMed

Rusthoven, Chad G; Kavanagh, Brian D

2017-12-01

Prophylactic cranial irradiation (PCI) for SCLC offers a consistent reduction in the incidence of brain metastases at the cost of measurable toxicity to neurocognitive function and quality of life, in the setting of characteristic pathologic changes to the brain. The sequelae of PCI have historically been justified by the perception of an overall survival advantage specific to SCLC. This rationale has now been challenged by a randomized trial in extensive-stage SCLC demonstrating equivalent progression-free survival and a trend toward improved overall survival with PCI omission in the context of modern magnetic resonance imaging (MRI) staging and surveillance. In this article, we critically examine the randomized trials of PCI in extensive-stage SCLC and discuss their implications on the historical data supporting PCI for limited-stage SCLC from the pre-MRI era. Further, we review the toxicity of moderate doses of radiation to the entire brain that underlie the growing interest in active MRI surveillance and PCI omission. Finally, the evidence supporting prospective investigation of radiosurgery for limited brain metastases in SCLC is reviewed. Overall, our aim is to provide an evidence-based assessment of the debate over PCI versus active MRI surveillance and to highlight the need for contemporary trials evaluating optimal central nervous system management in SCLC. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Eyes on MEGDEL: distinctive basal ganglia involvement in dystonia deafness syndrome.

PubMed

Wortmann, Saskia B; van Hasselt, Peter M; Barić, Ivo; Burlina, Alberto; Darin, Niklas; Hörster, Friederike; Coker, Mahmut; Ucar, Sema Kalkan; Krumina, Zita; Naess, Karin; Ngu, Lock H; Pronicka, Ewa; Riordan, Gilian; Santer, Rene; Wassmer, Evangeline; Zschocke, Johannes; Schiff, Manuel; de Meirleir, Linda; Alowain, Mohammed A; Smeitink, Jan A M; Morava, Eva; Kozicz, Tamas; Wevers, Ron A; Wolf, Nicole I; Willemsen, Michel A

2015-04-01

Pediatric movement disorders are still a diagnostic challenge, as many patients remain without a (genetic) diagnosis. Magnetic resonance imaging (MRI) pattern recognition can lead to the diagnosis. MEGDEL syndrome (3-MethylGlutaconic aciduria, Deafness, Encephalopathy, Leigh-like syndrome MIM #614739) is a clinically and biochemically highly distinctive dystonia deafness syndrome accompanied by 3-methylglutaconic aciduria, severe developmental delay, and progressive spasticity. Mutations are found in SERAC1, encoding a phosphatidylglycerol remodeling enzyme essential for both mitochondrial function and intracellular cholesterol trafficking. Based on the homogenous phenotype, we hypothesized an accordingly characteristic MRI pattern. A total of 43 complete MRI studies of 30 patients were systematically reevaluated. All patients presented a distinctive brain MRI pattern with five characteristic disease stages affecting the basal ganglia, especially the putamen. In stage 1, T2 signal changes of the pallidum are present. In stage 2, swelling of the putamen and caudate nucleus is seen. The dorsal putamen contains an "eye" that shows no signal alteration and (thus) seems to be spared during this stage of the disease. It later increases, reflecting progressive putaminal involvement. This "eye" was found in all patients with MEGDEL syndrome during a specific age range, and has not been reported in other disorders, making it pathognomonic for MEDGEL and allowing diagnosis based on MRI findings. Georg Thieme Verlag KG Stuttgart · New York.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

The 7th lung cancer TNM classification and staging system: Review of the changes and implications.

PubMed

Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin

2012-04-28

Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

MRI histogram analysis enables objective and continuous classification of intervertebral disc degeneration.

PubMed

Waldenberg, Christian; Hebelka, Hanna; Brisby, Helena; Lagerstrand, Kerstin Magdalena

2018-05-01

Magnetic resonance imaging (MRI) is the best diagnostic imaging method for low back pain. However, the technique is currently not utilized in its full capacity, often failing to depict painful intervertebral discs (IVDs), potentially due to the rough degeneration classification system used clinically today. MR image histograms, which reflect the IVD heterogeneity, may offer sensitive imaging biomarkers for IVD degeneration classification. This study investigates the feasibility of using histogram analysis as means of objective and continuous grading of IVD degeneration. Forty-nine IVDs in ten low back pain patients (six males, 25-69 years) were examined with MRI (T2-weighted images and T2-maps). Each IVD was semi-automatically segmented on three mid-sagittal slices. Histogram features of the IVD were extracted from the defined regions of interest and correlated to Pfirrmann grade. Both T2-weighted images and T2-maps displayed similar histogram features. Histograms of well-hydrated IVDs displayed two separate peaks, representing annulus fibrosus and nucleus pulposus. Degenerated IVDs displayed decreased peak separation, where the separation was shown to correlate strongly with Pfirrmann grade (P < 0.05). In addition, some degenerated IVDs within the same Pfirrmann grade displayed diametrically different histogram appearances. Histogram features correlated well with IVD degeneration, suggesting that IVD histogram analysis is a suitable tool for objective and continuous IVD degeneration classification. As histogram analysis revealed IVD heterogeneity, it may be a clinical tool for characterization of regional IVD degeneration effects. To elucidate the usefulness of histogram analysis in patient management, IVD histogram features between asymptomatic and symptomatic individuals needs to be compared.

Amyloid positron emission tomography with (18)F-flutemetamol and structural magnetic resonance imaging in the classification of mild cognitive impairment and Alzheimer's disease.

PubMed

Duara, Ranjan; Loewenstein, David A; Shen, Qian; Barker, Warren; Potter, Elizabeth; Varon, Daniel; Heurlin, Kristen; Vandenberghe, Rik; Buckley, Christopher

2013-05-01

To evaluate the contributions of amyloid-positive (Am+) and medial temporal atrophy-positive (MTA+) scans to the diagnostic classification of prodromal and probable Alzheimer's disease (AD). (18)F-flutemetamol-labeled amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) were used to classify 10 young normal, 15 elderly normal, 20 amnestic mild cognitive impairment (aMCI), and 27 AD subjects. MTA+ status was determined using a cut point derived from a previous study, and Am+ status was determined using a conservative and liberal cut point. The rates of MRI scans with positive results among young normal, elderly normal, aMCI, and AD subjects were 0%, 20%, 75%, and 82%, respectively. Using conservative cut points, the rates of Am+ scans for these same groups of subjects were 0%, 7%, 50%, and 93%, respectively, with the aMCI group showing the largest discrepancy between Am+ and MTA+ scans. Among aMCI cases, 80% of Am+ subjects were also MTA+, and 70% of amyloid-negative (Am-) subjects were MTA+. The combination of amyloid PET and MTA data was additive, with an overall correct classification rate for aMCI of 86%, when a liberal cut point (standard uptake value ratio = 1.4) was used for amyloid positivity. (18)F-flutemetamol PET and structural MRI provided additive information in the diagnostic classification of aMCI subjects, suggesting an amyloid-independent neurodegenerative component among aMCI subjects in this sample. Copyright © 2013 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Reliability of a novel, semi-quantitative scale for classification of structural brain magnetic resonance imaging in children with cerebral palsy.

PubMed

Fiori, Simona; Cioni, Giovanni; Klingels, Katrjin; Ortibus, Els; Van Gestel, Leen; Rose, Stephen; Boyd, Roslyn N; Feys, Hilde; Guzzetta, Andrea

2014-09-01

To describe the development of a novel rating scale for classification of brain structural magnetic resonance imaging (MRI) in children with cerebral palsy (CP) and to assess its interrater and intrarater reliability. The scale consists of three sections. Section 1 contains descriptive information about the patient and MRI. Section 2 contains the graphical template of brain hemispheres onto which the lesion is transposed. Section 3 contains the scoring system for the quantitative analysis of the lesion characteristics, grouped into different global scores and subscores that assess separately side, regions, and depth. A larger interrater and intrarater reliability study was performed in 34 children with CP (22 males, 12 females; mean age at scan of 9 y 5 mo [SD 3 y 3 mo], range 4 y-16 y 11 mo; Gross Motor Function Classification System level I, [n=22], II [n=10], and level III [n=2]). Very high interrater and intrarater reliability of the total score was found with indices above 0.87. Reliability coefficients of the lobar and hemispheric subscores ranged between 0.53 and 0.95. Global scores for hemispheres, basal ganglia, brain stem, and corpus callosum showed reliability coefficients above 0.65. This study presents the first visual, semi-quantitative scale for classification of brain structural MRI in children with CP. The high degree of reliability of the scale supports its potential application for investigating the relationship between brain structure and function and examining treatment response according to brain lesion severity in children with CP. © 2014 Mac Keith Press.

Multivariate pattern analysis of fMRI data reveals deficits in distributed representations in schizophrenia

PubMed Central

Yoon, Jong H.; Tamir, Diana; Minzenberg, Michael J.; Ragland, J. Daniel; Ursu, Stefan; Carter, Cameron S.

2009-01-01

Background Multivariate pattern analysis is an alternative method of analyzing fMRI data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional GLM-based univariate analysis. Methods 19 schizophrenia and 15 control subjects viewed two runs of stimuli--exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multi-voxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxel-wise activity across runs evaluated variance over time in activity patterns. Results Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared to controls, 59% and 72% respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between inter-run correlations and classification accuracy. Conclusions Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of non-specific factors driving these results. PMID:18822407

Forensic age estimation based on development of third molars: a staging technique for magnetic resonance imaging.

PubMed

De Tobel, J; Phlypo, I; Fieuws, S; Politis, C; Verstraete, K L; Thevissen, P W

2017-12-01

The development of third molars can be evaluated with medical imaging to estimate age in subadults. The appearance of third molars on magnetic resonance imaging (MRI) differs greatly from that on radiographs. Therefore a specific staging technique is necessary to classify third molar development on MRI and to apply it for age estimation. To develop a specific staging technique to register third molar development on MRI and to evaluate its performance for age estimation in subadults. Using 3T MRI in three planes, all third molars were evaluated in 309 healthy Caucasian participants from 14 to 26 years old. According to the appearance of the developing third molars on MRI, descriptive criteria and schematic representations were established to define a specific staging technique. Two observers, with different levels of experience, staged all third molars independently with the developed technique. Intra- and inter-observer agreement were calculated. The data were imported in a Bayesian model for age estimation as described by Fieuws et al. (2016). This approach adequately handles correlation between age indicators and missing age indicators. It was used to calculate a point estimate and a prediction interval of the estimated age. Observed age minus predicted age was calculated, reflecting the error of the estimate. One-hundred and sixty-six third molars were agenetic. Five percent (51/1096) of upper third molars and 7% (70/1044) of lower third molars were not assessable. Kappa for inter-observer agreement ranged from 0.76 to 0.80. For intra-observer agreement kappa ranged from 0.80 to 0.89. However, two stage differences between observers or between staging sessions occurred in up to 2.2% (20/899) of assessments, probably due to a learning effect. Using the Bayesian model for age estimation, a mean absolute error of 2.0 years in females and 1.7 years in males was obtained. Root mean squared error equalled 2.38 years and 2.06 years respectively. The performance to discern minors from adults was better for males than for females, with specificities of 96% and 73% respectively. Age estimations based on the proposed staging method for third molars on MRI showed comparable reproducibility and performance as the established methods based on radiographs.

Local staging and assessment of colon cancer with 1.5-T magnetic resonance imaging

PubMed Central

Blake, Helena; Jeyadevan, Nelesh; Abulafi, Muti; Swift, Ian; Toomey, Paul; Brown, Gina

2016-01-01

Objective: The aim of this study was to assess the accuracy of 1.5-T MRI in the pre-operative local T and N staging of colon cancer and identification of extramural vascular invasion (EMVI). Methods: Between 2010 and 2012, 60 patients with adenocarcinoma of the colon were prospectively recruited at 2 centres. 55 patients were included for final analysis. Patients received pre-operative 1.5-T MRI with high-resolution T2 weighted, gadolinium-enhanced T1 weighted and diffusion-weighted images. These were blindly assessed by two expert radiologists. Accuracy of the T-stage, N-stage and EMVI assessment was evaluated using post-operative histology as the gold standard. Results: Results are reported for two readers. Identification of T3 disease demonstrated an accuracy of 71% and 51%, sensitivity of 74% and 42% and specificity of 74% and 83%. Identification of N1 disease demonstrated an accuracy of 57% for both readers, sensitivity of 26% and 35% and specificity of 81% and 74%. Identification of EMVI demonstrated an accuracy of 74% and 69%, sensitivity 63% and 26% and specificity 80% and 91%. Conclusion: 1.5-T MRI achieved a moderate accuracy in the local evaluation of colon cancer, but cannot be recommended to replace CT on the basis of this study. Advances in knowledge: This study confirms that MRI is a viable alternative to CT for the local assessment of colon cancer, but this study does not reproduce the very high accuracy reported in the only other study to assess the accuracy of MRI in colon cancer staging. PMID:27226219

Prognostic value of the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification in stage IB lung adenocarcinoma.

PubMed

Xu, C-h; Wang, W; Wei, Y; Hu, H-d; Zou, J; Yan, J; Yu, L-k; Yang, R-s; Wang, Y

2015-10-01

Patients with pathological stage IB lung adenocarcinoma have a variable prognosis, even if received the same treatment. This study investigated the prognostic value of the new International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) lung adenocarcinoma classification in resected stage IB lung adenocarcinoma. We identified 276 patients with pathological stage IB adenocarcinoma who had undergone surgical resection at the Nanjing Chest Hospital between 2005 and 2010. The histological subtypes of all patients were classified according to the 2011 IASLC/ATS/ERS international multidisciplinary lung adenocarcinoma classification. Kaplan-Meier and Cox regression analyses were used to analyze the correlation between the IASLC/ATS/ERS classification and patients' prognosis. Two hundred and seventy-six patients with pathological stage IB adenocarcinoma had an 86.2% 5-year overall survival (OS) and 80.4% 5-year disease-free survival (DFS). Patients with micropapillary and solid predominant tumors had a significantly worse OS and DFS as compared to those with other subtypes predominant tumors (p = 0.003 and 0.001). Multivariate analysis revealed that the new classification was an independent prognostic factor for both OS and DFS of pathological stage IB adenocarcinoma (p = 0.009 and 0.003). Our study revealed that the new IASLC/ATS/ERS classification was an independent prognostic factor of pathological stage IB adenocarcinoma. This new classification is valuable of screening out high risk patients to receive postoperative adjuvant therapy. Copyright © 2015. Published by Elsevier Ltd.

Sequential Dictionary Learning From Correlated Data: Application to fMRI Data Analysis.

PubMed

Seghouane, Abd-Krim; Iqbal, Asif

2017-03-22

Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information. The proposed algorithms differ from the K-SVD in their sparse coding and dictionary update stages. The first two algorithms account for the known correlation structure in the fMRI data by using the squared Q, R-norm instead of the Frobenius norm for matrix approximation. The third and last algorithm account for both the known correlation structure in the fMRI data and the temporal smoothness. The temporal smoothness is incorporated in the dictionary update stage via regularization of the dictionary atoms obtained with penalization. The performance of the proposed dictionary learning algorithms are illustrated through simulations and applications on real fMRI data.

Staging Liver Fibrosis with Statistical Observers

NASA Astrophysics Data System (ADS)

Brand, Jonathan Frieman

Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically on order of 1mm, which close to the resolution limit of in vivo Gd-enhanced MRI. In this work the methods to collect training and testing images for a Hotelling observer are covered. An observer based on local texture analysis is trained and tested using wet-tissue phantoms. The technique is used to optimize the MRI sequence based on task performance. The final method developed is a two stage model observer to classify fibrotic and healthy tissue in both phantoms and in vivo MRI images. The first stage observer tests for the presence of local texture. Test statistics from the first observer are used to train the second stage observer to globally sample the local observer results. A decision of the disease class is made for an entire MRI image slice using test statistics collected from the second observer. The techniques are tested on wet-tissue phantoms and in vivo clinical patient data.

Seral stage classification and monitoring model for big sagebrush/western wheatgrass/blue grama habitat

Treesearch

Lakhdar Benkobi; Daniel W. Uresk

1996-01-01

An ecological classification model for seral stages was developed for big sagebrush (Artemisia tridentata) shrub steppe habitat in Thunder Basin National Grassland, Wyoming. Four seral stages (early to late succession) were defined in this habitat type. Ecological seral stages were quantitatively identified with an estimated 92% level of accuracy...

The Use of Breast Magnetic Resonance Imaging Parameters to Identify Possible Signaling Pathways of a Serum Biomarker, HE4.

PubMed

Durur-Karakaya, Afak; Durur-Subasi, Irmak; Karaman, Adem; Akcay, Mufide Nuran; Palabiyik, Saziye Sezin; Erdemci, Burak; Alper, Fatih; Acemoglu, Hamit

2016-01-01

This study aimed to investigate the relationship between breast magnetic resonance imaging (MRI) parameters; clinical features such as age, tumor diameter, N, T, and TNM stages; and serum human epididymis protein 4 (HE4) levels in patients with breast carcinoma and use this as a means of estimating possible signaling pathways of the biomarker, HE4. Thirty-seven patients with breast cancer were evaluated by breast MRI and serum HE4 levels before therapy. Correlations between parameters including age, tumor diameter T and N, dynamic curve type, enhancement ratio (ER), slope washin (S-WI), time to peak (TTP), slope washout (S-WO), and the serum level of HE4 were investigated statistically. Human epididymis protein 4 levels of early and advanced stage of disease were also compared statistically. Breast MRI parameters showed correlation to serum HE4 levels and correlations were statistically significant. Of these MRI parameters, S-WI had higher correlation coefficient than the others. Human epididymis protein 4 levels were not statistically different in early and advanced stage of disease. High correlation with MRI parameters related to neoangiogenesis may indicate signaling pathway of HE4.

Role of imaging techniques in the diagnosis and follow-up of muscle-invasive bladder carcinoma.

PubMed

Mesa, A; Nava, E; Fernández Del Valle, A; Argüelles, B; Menéndez-Del Llano, R; Sal de Rellán, S

2017-10-10

Muscle-invasive bladder malignancies represent 20-30% of all bladder cancers. These patients require imaging tests to determine the regional and distant staging. To describe the role of various imaging tests in the diagnosis, staging and follow-up of muscle-invasive bladder cancer. To assess recent developments in radiology aimed at improving the sensitivity and specificity of local staging and treatment response. We conducted an updated literature review. Computed tomography and magnetic resonance imaging (MRI) are the tests of choice for performing proper staging prior to surgery. Computed tomography urography is currently the most widely used technique, although it has limitations in local staging. Ultrasonography still has a limited role. Recent developments in MRI have improved its capacity for local staging. MRI has been suggested as the test of choice for the follow-up, with promising results in assessing treatment response. Positron emission tomography could improve the detection of adenopathies and extrapelvic metastatic disease. Imaging tests are essential for the diagnosis, staging and follow-up of muscle-invasive bladder cancer. Recent technical developments represent important improvements in local staging and have opened the possibility of assessing treatment response. Copyright © 2017 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

Fusion of multi-parametric MRI and temporal ultrasound for characterization of prostate cancer: in vivo feasibility study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Ghavidel, Sahar; Abolmaesumi, Purang; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Romagnoli, Cesare; Cool, Derek W.; Bastian-Jordan, Matthew; Kassam, Zahra; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Mousavi, Parvin

2016-03-01

Recently, multi-parametric Magnetic Resonance Imaging (mp-MRI) has been used to improve the sensitivity of detecting high-risk prostate cancer (PCa). Prior to biopsy, primary and secondary cancer lesions are identified on mp-MRI. The lesions are then targeted using TRUS guidance. In this paper, for the first time, we present a fused mp-MRI-temporal-ultrasound framework for characterization of PCa, in vivo. Cancer classification results obtained using temporal ultrasound are fused with those achieved using consolidated mp-MRI maps determined by multiple observers. We verify the outcome of our study using histopathology following deformable registration of ultrasound and histology images. Fusion of temporal ultrasound and mp-MRI for characterization of the PCa results in an area under the receiver operating characteristic curve (AUC) of 0.86 for cancerous regions with Gleason scores (GSs)>=3+3, and AUC of 0.89 for those with GSs>=3+4.

Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI.

PubMed

Adler, Sophie; Lorio, Sara; Jacques, Thomas S; Benova, Barbora; Gunny, Roxana; Cross, J Helen; Baldeweg, Torsten; Carmichael, David W

2017-01-01

Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.

Deep learning for brain tumor classification

NASA Astrophysics Data System (ADS)

Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

2017-03-01

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

Ionising radiation-free whole-body MRI versus (18)F-fluorodeoxyglucose PET/CT scans for children and young adults with cancer: a prospective, non-randomised, single-centre study.

PubMed

Klenk, Christopher; Gawande, Rakhee; Uslu, Lebriz; Khurana, Aman; Qiu, Deqiang; Quon, Andrew; Donig, Jessica; Rosenberg, Jarrett; Luna-Fineman, Sandra; Moseley, Michael; Daldrup-Link, Heike E

2014-03-01

Imaging tests are essential for staging of children with cancer. However, CT and radiotracer-based imaging procedures are associated with substantial exposure to ionising radiation and risk of secondary cancer development later in life. Our aim was to create a highly effective, clinically feasible, ionising radiation-free staging method based on whole-body diffusion-weighted MRI and the iron supplement ferumoxytol, used off-label as a contrast agent. We compared whole-body diffusion-weighted MRI with standard clinical (18)F-fluorodeoxyglucose ((18)F-FDG) PET/CT scans in children and young adults with malignant lymphomas and sarcomas. Whole-body diffusion-weighted magnetic resonance images were generated by coregistration of colour-encoded ferumoxytol-enhanced whole-body diffusion-weighted MRI scans for tumour detection with ferumoxytol-enhanced T1-weighted MRI scans for anatomical orientation, similar to the concept of integrated (18)F-FDG PET/CT scans. Tumour staging results were compared using Cohen's κ statistics. Histopathology and follow-up imaging served as the standard of reference. Data was assessed in the per-protocol population. This study is registered with ClinicalTrials.gov, number NCT01542879. 22 of 23 recruited patients were analysed because one patient discontinued before completion of the whole-body scan. Mean exposure to ionising radiation was 12·5 mSv (SD 4·1) for (18)F-FDG PET/CT compared with zero for whole-body diffusion-weighted MRI. (18)F-FDG PET/CT detected 163 of 174 malignant lesions at 1325 anatomical regions and whole-body diffusion-weighted MRI detected 158. Comparing (18)F-FDG PET/CT to whole-body diffusion-weighted MRI, sensitivities were 93·7% (95% CI 89·0-96·8) versus 90·8% (85·5-94·7); specificities 97·7% (95% CI 96·7-98·5) versus 99·5% (98·9-99·8); and diagnostic accuracies 97·2% (93·6-99·4) versus 98·3% (97·4-99·2). Tumour staging results showed very good agreement between both imaging modalities with a κ of 0·93 (0·81-1·00). No adverse events after administration of ferumoxytol were recorded. Ferumoxytol-enhanced whole-body diffusion-weighted MRI could be an alternative to (18)F-FDG PET/CT for staging of children and young adults with cancer that is free of ionising radiation. This new imaging test might help to prevent long-term side-effects from radiographic staging procedures. Thrasher Research Fund and Clinical Health Research Institute at Stanford University. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation of a Web-Based App Demonstrating an Exclusionary Algorithmic Approach to TNM Cancer Staging

PubMed Central

2015-01-01

Background TNM staging plays a critical role in the evaluation and management of a range of different types of cancers. The conventional combinatorial approach to the determination of an anatomic stage relies on the identification of distinct tumor (T), node (N), and metastasis (M) classifications to generate a TNM grouping. This process is inherently inefficient due to the need for scrupulous review of the criteria specified for each classification to ensure accurate assignment. An exclusionary approach to TNM staging based on sequential constraint of options may serve to minimize the number of classifications that need to be reviewed to accurately determine an anatomic stage. Objective Our aim was to evaluate the usability and utility of a Web-based app configured to demonstrate an exclusionary approach to TNM staging. Methods Internal medicine residents, surgery residents, and oncology fellows engaged in clinical training were asked to evaluate a Web-based app developed as an instructional aid incorporating (1) an exclusionary algorithm that polls tabulated classifications and sorts them into ranked order based on frequency counts, (2) reconfiguration of classification criteria to generate disambiguated yes/no questions that function as selection and exclusion prompts, and (3) a selectable grid of TNM groupings that provides dynamic graphic demonstration of the effects of sequentially selecting or excluding specific classifications. Subjects were asked to evaluate the performance of this app after completing exercises simulating the staging of different types of cancers encountered during training. Results Survey responses indicated high levels of agreement with statements supporting the usability and utility of this app. Subjects reported that its user interface provided a clear display with intuitive controls and that the exclusionary approach to TNM staging it demonstrated represented an efficient process of assignment that helped to clarify distinctions between tumor, node, and metastasis classifications. High overall usefulness ratings were bolstered by supplementary comments suggesting that this app might be readily adopted for use in clinical practice. Conclusions A Web-based app that utilizes an exclusionary algorithm to prompt the assignment of tumor, node, and metastasis classifications may serve as an effective instructional aid demonstrating an efficient and informative approach to TNM staging. PMID:28410163

Disparities in staging prostate magnetic resonance imaging utilization for nonmetastatic prostate cancer patients undergoing definitive radiation therapy.

PubMed

Ajayi, Ayobami; Hwang, Wei-Ting; Vapiwala, Neha; Rosen, Mark; Chapman, Christina H; Both, Stefan; Shah, Meera; Wang, Xingmei; Agawu, Atu; Gabriel, Peter; Christodouleas, John; Tochner, Zelig; Deville, Curtiland

2016-01-01

There is growing evidence supporting incorporating multiparametric (mp) magnetic resonance imaging (MRI) scans into risk stratification, active surveillance, and treatment paradigms for prostate cancer. The purpose of our study was to determine whether demographic disparities exist in staging MRI utilization for prostate cancer patients. An institutional database of 705 nonmetastatic prostate cancer patients treated with radiation therapy from 2005 through 2013 was used to identify patients undergoing versus not undergoing pretreatment diagnostic prostate mpMRI. Uni- and multivariable logistic regression evaluated the relationship of clinical and demographic characteristics with MRI utilization. All demographic variables assessed, except the other race category, were significantly associated with MRI utilization (all P < .05), including age (odds ratio [OR], 0.92), black race (OR, 0.51), poverty (OR, 0.53), closer distance to radiation facility (OR, 1.79), and nonprivate primary insurance (OR, 0.57) on univariable analysis, while clinical stage T3 (OR, 3.37) was the only clinical characteristic. On multivariable analysis stratified by D'Amico risk group, age remained significant across all risk groups, whereas the black versus white racial (OR, 0.21; 95% confidence interval, 0.08-0.55) and nonprivate versus private insurance type (OR, 0.37; 95% confidence interval, 0.16-0.86) disparities persisted in the low-risk group. Clinical stage T3 remained associated in the high-risk group. For race specifically, the percentages of whites, blacks, and others undergoing MRI in the overall cohort and by risk group were, respectively: overall, 80% (343/427), 68% (156/231), and 85% (40/47); low risk, 86%, 56%, and 63%; intermediate risk, 79%, 72%, and 95%; and high risk, 72%, 72%, and 100%. In this urban, academic center cohort, older patients across all risk groups and black or nonprivate insurance patients in the low risk group were less likely to undergo staging prostate MRI scans. Further research should investigate these differences to ensure equitable utilization across all demographic groups considering the burden of prostate cancer disparities.

High-resolution T2-weighted cervical cancer imaging: a feasibility study on ultra-high-field 7.0-T MRI with an endorectal monopole antenna.

PubMed

Hoogendam, Jacob P; Kalleveen, Irene M L; de Castro, Catalina S Arteaga; Raaijmakers, Alexander J E; Verheijen, René H M; van den Bosch, Maurice A A J; Klomp, Dennis W J; Zweemer, Ronald P; Veldhuis, Wouter B

2017-03-01

We studied the feasibility of high-resolution T 2 -weighted cervical cancer imaging on an ultra-high-field 7.0-T magnetic resonance imaging (MRI) system using an endorectal antenna of 4.7-mm thickness. A feasibility study on 20 stage IB1-IIB cervical cancer patients was conducted. All underwent pre-treatment 1.5-T MRI. At 7.0-T MRI, an external transmit/receive array with seven dipole antennae and a single endorectal monopole receive antenna were used. Discomfort levels were assessed. Following individualised phase-based B 1 + shimming, T 2 -weighted turbo spin echo sequences were completed. Patients had stage IB1 (n = 9), IB2 (n = 4), IIA1 (n = 1) or IIB (n = 6) cervical cancer. Discomfort (ten-point scale) was minimal at placement and removal of the endorectal antenna with a median score of 1 (range, 0-5) and 0 (range, 0-2) respectively. Its use did not result in adverse events or pre-term session discontinuation. To demonstrate feasibility, T 2 -weighted acquisitions from 7.0-T MRI are presented in comparison to 1.5-T MRI. Artefacts on 7.0-T MRI were due to motion, locally destructive B 1 interference, excessive B 1 under the external antennae and SENSE reconstruction. High-resolution T 2 -weighted 7.0-T MRI of stage IB1-IIB cervical cancer is feasible. The addition of an endorectal antenna is well tolerated by patients. • High resolution T 2 -weighted 7.0-T MRI of the inner female pelvis is challenging • We demonstrate a feasible approach for T 2 -weighted 7.0-T MRI of cervical cancer • An endorectal monopole receive antenna is well tolerated by participants • The endorectal antenna did not lead to adverse events or session discontinuation.

Circumferential resection margin (CRM) positivity after MRI assessment and adjuvant treatment in 189 patients undergoing rectal cancer resection.

PubMed

Simpson, G S; Eardley, N; McNicol, F; Healey, P; Hughes, M; Rooney, P S

2014-05-01

The management of rectal cancer relies on accurate MRI staging. Multi-modal treatments can downstage rectal cancer prior to surgery and may have an effect on MRI accuracy. We aim to correlate the findings of MRI staging of rectal cancer with histological analysis, the effect of neoadjuvant therapy on this and the implications of circumferential resection margin (CRM) positivity following neoadjuvant therapy. An analysis of histological data and radiological staging of all cases of rectal cancer in a single centre between 2006 and 2011 were conducted. Two hundred forty-one patients had histologically proved rectal cancer during the study period. One hundred eighty-two patients underwent resection. Median age was 66.6 years, and male to female ratio was 13:5. R1 resection rate was 11.1%. MRI assessments of the circumferential resection margin in patients without neoadjuvant radiotherapy were 93.6 and 88.1% in patients who underwent neoadjuvant radiotherapy. Eighteen patients had predicted positive margins following chemoradiotherapy, of which 38.9% had an involved CRM on histological analysis. MRI assessment of the circumferential resection margin in rectal cancer is associated with high accuracy. Neoadjuvant chemoradiotherapy has a detrimental effect on this accuracy, although accuracy remains high. In the presence of persistently predicted positive margins, complete resection remains achievable but may necessitate a more radical approach to resection.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

Finite Difference Time Domain Modeling at USA Instruments, Inc.

NASA Astrophysics Data System (ADS)

Curtis, Richard

2003-10-01

Due to the competitive nature of the commercial MRI industry, it is essential for the financial health of a participating company to innovate new coil designs and bring product to market rapidly in response to ever-changing market conditions. However, the technology of MRI coil design is still early in its stage of development and its principles are yet evolving. As a result, it is not always possible to know the relevant electromagnetic effects of a given design since the interaction of coil elements is complex and often counter-intuitive. Even if the effects are known qualitatively, the quantitative results are difficult to obtain. At USA Instruments, Inc., the acquisition of the XFDTDâ electromagnetic simulation tool from REMCOM, Inc., has been helpful in determining the electromagnetic performance characteristics of existing coil designs in the prototype stage before the coils are released for production. In the ideal case, a coil design would be modeled earlier at the conceptual stage, so that only good designs will make it to the prototyping stage and the electromagnetic characteristics better understood very early in the design process and before the testing stage has begun. This paper is a brief overview of using FDTD modeling for MRI coil design at USA Instruments, Inc., and shows some of the highlights of recent FDTD modeling efforts on Birdcage coils, a staple of the MRI coil design portfolio.

Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters.

PubMed

Rive, Maria M; Redlich, Ronny; Schmaal, Lianne; Marquand, André F; Dannlowski, Udo; Grotegerd, Dominik; Veltman, Dick J; Schene, Aart H; Ruhé, Henricus G

2016-11-01

Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging (fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder (MDD) and bipolar disorder (BD). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication-free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike. We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting-state fMRI (resting-state networks implicated in mood disorders: default mode network [DMN], salience network [SN], and lateralized frontoparietal networks [FPNs]) in depressed (n=42) and remitted (n=49) medication-free subjects with MDD and BD. Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPNs and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD. For the first time, we showed that medication-free subjects with MDD and BD can be differentiated based on structural MRI as well as resting-state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication-naïve or first-episode subjects. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2016-09-01

The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.

Magnetic resonance imaging of sacroiliitis in patients with spondyloarthritis: correlation with anatomy and histology.

PubMed

Hermann, K-G A; Bollow, M

2014-03-01

Magnetic resonance imaging (MRI) of the sacroiliac joints (SIJs) has become established as a valuable modality for the early diagnosis of sacroiliitis in patients with inconclusive radiographic findings. Positive MRI findings have the same significance as a positive test for HLA-B27. Sacroiliitis is one of the key features of axial spondyloarthritis (SpA) in the classification proposed by the Assessments in Ankylosing Spondylitis (ASAS) group. Early signs of sacroiliitis include enthesitis of articular fibrocartilage, capsulitis, and osteitis. In more advanced disease, structural (chronic) lesions will be visible, including periarticular fatty deposition, erosions, subchondral sclerosis, and transarticular bone buds and bridges. In this article we describe magnetic resonance (MR) findings and provide histologic biopsy specimens of the respective disease stages. The predominant histologic feature of early and active sacroiliitis is the destruction of cartilage and bone by proliferations consisting of fibroblasts and fibrocytes, T-cells, and macrophages. Advanced sacroiliitis is characterized by new bone formation with enclosed cartilaginous islands and residual cellular infiltrations, which may ultimately lead to complete ankylosis. Knowledge of the morphologic appearance of the sacroiliac joints and their abnormal microscopic and gross anatomy is helpful in correctly interpreting MR findings. © Georg Thieme Verlag KG Stuttgart · New York.

Clinically Practical Magnetic Resonance Protocol for Improved Specifically in Breast Cancer Diagnosis

DTIC Science & Technology

2006-06-01

MRI, MRS, DCE, Choline , Perfusion, Breast Cancer, Diagnosis, Specificity 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18...data analysis, patient recruitment and consent, and can now perform these tasks independently. 3. Through interactions with the DOD representative (Dr...out at 4 cc/sec during perfusion MRI acquisition. The detection of an apparent choline compounds (Cho) peak (S/N > 2) at 3.23 ppm was defined as

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

PubMed

Sato, João R; Moll, Jorge; Green, Sophie; Deakin, John F W; Thomaz, Carlos E; Zahn, Roland

2015-08-30

Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to address this question. Using a recently identified neural signature of guilt-selective functional disconnection, the classification algorithm was able to distinguish remitted MD from control participants with 78.3% accuracy. This demonstrates the high potential of our fMRI signature as a biomarker of MD vulnerability. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.

Breast MRI in nonpalpable breast lesions: a randomized trial with diagnostic and therapeutic outcome - MONET - study.

PubMed

Peters, Nicky H G M; Borel Rinkes, Inne H M; Mali, Willem P T M; van den Bosch, Maurice A A J; Storm, Remmert K; Plaisier, Peter W; de Boer, Erwin; van Overbeeke, Adriaan J; Peeters, Petra H M

2007-11-28

In recent years there has been an increasing interest in MRI as a non-invasive diagnostic modality for the work-up of suspicious breast lesions. The additional value of Breast MRI lies mainly in its capacity to detect multicentric and multifocal disease, to detect invasive components in ductal carcinoma in situ lesions and to depict the tumor in a 3-dimensional image. Breast MRI therefore has the potential to improve the diagnosis and provide better preoperative staging and possibly surgical care in patients with breast cancer. The aim of our study is to assess whether performing contrast enhanced Breast MRI can reduce the number of surgical procedures due to better preoperative staging and whether a subgroup of women with suspicious nonpalpable breast lesions can be identified in which the combination of mammography, ultrasound and state-of-the-art contrast-enhanced Breast MRI can provide a definite diagnosis. The MONET - study (MR mammography Of Nonpalpable BrEast Tumors) is a randomized controlled trial with diagnostic and therapeutic endpoints. We aim to include 500 patients with nonpalpable suspicious breast lesions who are referred for biopsy. With this number of patients, the expected 12% reduction in surgical procedures due to more accurate preoperative staging with Breast MRI can be detected with a high power (90%). The secondary outcome is the positive and negative predictive value of contrast enhanced Breast MRI. If the predictive values are deemed sufficiently close to those for large core biopsy then the latter, invasive, procedure could possibly be avoided in some women. The rationale, study design and the baseline characteristics of the first 100 included patients are described. Study protocol number NCT00302120.

Breast MRI in nonpalpable breast lesions: a randomized trial with diagnostic and therapeutic outcome – MONET – study

PubMed Central

Peters, Nicky HGM; Borel Rinkes, Inne HM; Mali, Willem PTM; van den Bosch, Maurice AAJ; Storm, Remmert K; Plaisier, Peter W; de Boer, Erwin; van Overbeeke, Adriaan J; Peeters, Petra HM

2007-01-01

Background In recent years there has been an increasing interest in MRI as a non-invasive diagnostic modality for the work-up of suspicious breast lesions. The additional value of Breast MRI lies mainly in its capacity to detect multicentric and multifocal disease, to detect invasive components in ductal carcinoma in situ lesions and to depict the tumor in a 3-dimensional image. Breast MRI therefore has the potential to improve the diagnosis and provide better preoperative staging and possibly surgical care in patients with breast cancer. The aim of our study is to assess whether performing contrast enhanced Breast MRI can reduce the number of surgical procedures due to better preoperative staging and whether a subgroup of women with suspicious nonpalpable breast lesions can be identified in which the combination of mammography, ultrasound and state-of-the-art contrast-enhanced Breast MRI can provide a definite diagnosis. Methods/Design The MONET – study (MR mammography Of Nonpalpable BrEast Tumors) is a randomized controlled trial with diagnostic and therapeutic endpoints. We aim to include 500 patients with nonpalpable suspicious breast lesions who are referred for biopsy. With this number of patients, the expected 12% reduction in surgical procedures due to more accurate preoperative staging with Breast MRI can be detected with a high power (90%). The secondary outcome is the positive and negative predictive value of contrast enhanced Breast MRI. If the predictive values are deemed sufficiently close to those for large core biopsy then the latter, invasive, procedure could possibly be avoided in some women. The rationale, study design and the baseline characteristics of the first 100 included patients are described. Trial registration Study protocol number NCT00302120 PMID:18045470

Diagnostic performance of magnetic resonance imaging and 3D endoanal ultrasound in detection, staging and assessment post treatment, in anal cancer.

PubMed

Reginelli, Alfonso; Granata, Vincenza; Fusco, Roberta; Granata, Francesco; Rega, Daniela; Roberto, Luca; Pellino, Gianluca; Rotondo, Antonio; Selvaggi, Francesco; Izzo, Francesco; Petrillo, Antonella; Grassi, Roberto

2017-04-04

We compared Magnetic Resonance Imaging (MRI) and 3D Endoanal Ultrasound (EAUS) imaging performance to confirm anal carcinoma and to monitor treatment response.58 patients with anal cancer were retrospectively enrolled. All patients underwent clinical examination, anoscopic examination; EAUS and contrast-enhanced MRI study before and after treatment. Four radiologists evaluated the presence of lesions, using a 4-point confidence scale, features of the lesion and nodes on EAUS images, T1-weighted (T1-W), T2-weighted (T2-W) and diffusion-weighted images (DWI) signal intensity (SI), the apparent diffusion coefficient (ADC) map for nodes and lesion, as well as enhancement pattern during dynamic MRI were assessed.All lesions were detected by EAUS while MRI detected 93.1% of anal cancer. MRI showed a good correlation with EAUS, anoscopy and clinical examination. The residual tissue not showed significant difference in EAUS assessment and T2-W SI in pre and post treatment. We found significant difference in dynamic study, in SI of DWI, in ADC map and values among responder's patients in pre and post treatment. The neoplastic nodes were hypoecoic on EAUS, with hyperintense signal on T2-W sequences and hypointense signal on T1-W. The neoplastic nodes showed SI on DWI sequences and ADC value similar to anal cancer. We found significant difference in nodes status in pre and post therapy on DWI data.3D EAUS and MRI are accurate techniques in anal cancer staging, although EAUS is more accurate than MRI for T1 stage. MRI allows correct detection of neoplastic nodes and can properly stratify patients into responders or non responders.

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

PubMed

Aktaruzzaman, M; Migliorini, M; Tenhunen, M; Himanen, S L; Bianchi, A M; Sassi, R

2015-05-01

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

Urogenital tuberculosis: definition and classification.

PubMed

Kulchavenya, Ekaterina

2014-10-01

To improve the approach to the diagnosis and management of urogenital tuberculosis (UGTB), we need clear and unique classification. UGTB remains an important problem, especially in developing countries, but it is often an overlooked disease. As with any other infection, UGTB should be cured by antibacterial therapy, but because of late diagnosis it may often require surgery. Scientific literature dedicated to this problem was critically analyzed and juxtaposed with the author's own more than 30 years' experience in tuberculosis urology. The conception, terms and definition were consolidated into one system; classification stage by stage as well as complications are presented. Classification of any disease includes dispersion on forms and stages and exact definitions for each stage. Clinical features and symptoms significantly vary between different forms and stages of UGTB. A simple diagnostic algorithm was constructed. UGTB is multivariant disease and a standard unified approach to it is impossible. Clear definition as well as unique classification are necessary for real estimation of epidemiology and the optimization of therapy. The term 'UGTB' has insufficient information in order to estimate therapy, surgery and prognosis, or to evaluate the epidemiology.

Predictors of clinical-pathologic stage discrepancy in oral cavity squamous cell carcinoma: A National Cancer Database study.

PubMed

Kılıç, Sarah S; Kılıç, Suat; Crippen, Meghan M; Varughese, Denny; Eloy, Jean Anderson; Baredes, Soly; Mahmoud, Omar M; Park, Richard Chan Woo

2018-04-01

Few studies have examined the frequency and survival implications of clinicopathologic stage discrepancy in oral cavity squamous cell carcinoma (SCC). Oral cavity SCC cases with full pathologic staging information were identified in the National Cancer Database (NCDB). Clinical and pathologic stages were compared. Multivariate logistic regressions were performed to identify factors associated with stage discrepancy. There were 9110 cases identified, of which 67.3% of the cases were stage concordant, 19.9% were upstaged, and 12.8% were downstaged. The N classification discordance (28.5%) was more common than T classification discordance (27.6%). In cases of T classification discordance, downstaging is more common than upstaging (15.4% vs 12.1% of cases), but in cases of N classification discordance, the reverse is true; upstaging is much more common than downstaging (20.1 vs 8.4% of cases). Clinicopathologic stage discrepancy in oral cavity SCC is a common phenomenon that is associated with a number of clinical factors and has survival implications. © 2018 Wiley Periodicals, Inc.

MRI and MRS alterations in the preclinical phase of murine prion disease: association with neuropathological and behavioural changes.

PubMed

Broom, Kerry A; Anthony, Daniel C; Lowe, John P; Griffin, Julian L; Scott, Helen; Blamire, Andrew M; Styles, Peter; Perry, V Hugh; Sibson, Nicola R

2007-06-01

Prion diseases are fatal chronic neurodegenerative diseases. Previous qualitative magnetic resonance imaging (MRI) and spectroscopy (MRS) studies report conflicting results in the symptomatic stages of the disease, but little work has been carried out during the earlier stages of the disease. Here we have used the murine ME7 model of prion disease to quantitatively investigate MRI and MRS changes during the period prior to the onset of overt clinical signs (20+ weeks) and have correlated these with pathological and behavioural abnormalities. Using in vivo MRI, at the later stages of the preclinical period (18 weeks) the diffusion of tissue water was significantly reduced, coinciding with significant microglial activation and behavioural hyperactivity. Using in vivo MRS, we found early (12 weeks) decreases in the ratio of N-acetyl aspartate to both choline (NAA/Cho) and creatine (NAA/Cr) in the thalamus and hippocampus, which were associated with early behavioural deficits. Ex vivo MRS of brain extracts confirmed and extended these findings, showing early (8-12 weeks) decreases in both the neuronal metabolites NAA and glutamate, and the metabolic metabolites lactate and glucose. Increases in the glial metabolite myo-inositol were observed at later stages when microglial and astrocyte activation is substantial. These changes in MRI and MRS signals, which precede overt clinical signs of disease, could provide insights into the pathogenesis of this disease and may enable early detection of pathology.

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

PubMed

de Pierrefeu, Amicie; Fovet, Thomas; Hadj-Selem, Fouad; Löfstedt, Tommy; Ciuciu, Philippe; Lefebvre, Stephanie; Thomas, Pierre; Lopes, Renaud; Jardri, Renaud; Duchesnay, Edouard

2018-04-01

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback. © 2018 Wiley Periodicals, Inc.

Magnetic Resonance Imaging Findings Predict the Recurrence of Chronic Subdural Hematoma

PubMed Central

GOTO, Haruo; ISHIKAWA, Osamu; NOMURA, Masashi; TANAKA, Kentaro; NOMURA, Seiji; MAEDA, Keiichiro

2015-01-01

The exact predictive factors for postoperative recurrence of chronic subdural hematoma (CSDH) are still unknown. Based on the preoperative magnetic resonance imaging (MRI), low recurrence rate of T1-hyperintensity hematoma was previously reported. We investigated the other types of radiological findings which are related to the recurrence rate of CSDH in large number of patients analyzed by multivariate logistic regression model. Preoperative MRI and postoperative computed tomography (CT) were performed and the influence of the preoperative use of antiplatelet or anticoagulant drugs was also studied. The overall recurrence rate was 9.3% (47 of 505 hematomas). The MRI T1-iso/hypointensity group showed a significantly higher recurrence rate (18.2%, 29 of 159) compared to the other groups (5.2%, 18 of 346; p < 0.001). Multivariate logistic regression analysis showed T1 classification was the solo significant prognostic predictor among various factors such as bilateral hematoma, antiplatelet or anticoagulant drug usage, residual hematoma on postoperative CT, and MRI classification (p < 0.001): adjusted odds ratio for the recurrence in T1-iso/hypointensity group relative to the T1-hyperintensity group was 5.58 [95% confidence interval (CI), 2.09–14.86] (p = 0.001). Postoperative residual hematoma and antiplatelet or anticoagulant drug usage did not increase the recurrence risk. The preoperative MRI findings, especially T1WI findings, have predictive value for postoperative recurrence of CSDH and the T1-iso/hypointensity group can be assumed to be a high recurrence risk group. PMID:25746312

Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans.

PubMed

Griffis, Joseph C; Allendorfer, Jane B; Szaflarski, Jerzy P

2016-01-15

Manual lesion delineation by an expert is the standard for lesion identification in MRI scans, but it is time-consuming and can introduce subjective bias. Alternative methods often require multi-modal MRI data, user interaction, scans from a control population, and/or arbitrary statistical thresholding. We present an approach for automatically identifying stroke lesions in individual T1-weighted MRI scans using naïve Bayes classification. Probabilistic tissue segmentation and image algebra were used to create feature maps encoding information about missing and abnormal tissue. Leave-one-case-out training and cross-validation was used to obtain out-of-sample predictions for each of 30 cases with left hemisphere stroke lesions. Our method correctly predicted lesion locations for 30/30 un-trained cases. Post-processing with smoothing (8mm FWHM) and cluster-extent thresholding (100 voxels) was found to improve performance. Quantitative evaluations of post-processed out-of-sample predictions on 30 cases revealed high spatial overlap (mean Dice similarity coefficient=0.66) and volume agreement (mean percent volume difference=28.91; Pearson's r=0.97) with manual lesion delineations. Our automated approach agrees with manual tracing. It provides an alternative to automated methods that require multi-modal MRI data, additional control scans, or user interaction to achieve optimal performance. Our fully trained classifier has applications in neuroimaging and clinical contexts. Copyright © 2015 Elsevier B.V. All rights reserved.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Connectivity Strength-Weighted Sparse Group Representation-Based Brain Network Construction for MCI Classification

PubMed Central

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-01-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. PMID:28150897

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

PubMed Central

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C. M.

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases. PMID:25692885

Decoding multiple sound categories in the human temporal cortex using high resolution fMRI.

PubMed

Zhang, Fengqing; Wang, Ji-Ping; Kim, Jieun; Parrish, Todd; Wong, Patrick C M

2015-01-01

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain's representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.

Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience.

PubMed

Patel, Uday B; Taylor, Fiona; Blomqvist, Lennart; George, Christopher; Evans, Hywel; Tekkis, Paris; Quirke, Philip; Sebag-Montefiore, David; Moran, Brendan; Heald, Richard; Guthrie, Ashley; Bees, Nicola; Swift, Ian; Pennert, Kjell; Brown, Gina

2011-10-01

To assess magnetic resonance imaging (MRI) and pathologic staging after neoadjuvant therapy for rectal cancer in a prospectively enrolled, multicenter study. In a prospective cohort study, 111 patients who had rectal cancer treated by neoadjuvant therapy were assessed for response by MRI and pathology staging by T, N and circumferential resection margin (CRM) status. Tumor regression grade (TRG) was also assessed by MRI. Overall survival (OS) was estimated by using the Kaplan-Meier product-limit method, and Cox proportional hazards models were used to determine associations between staging of good and poor responders on MRI or pathology and survival outcomes after controlling for patient characteristics. On multivariate analysis, the MRI-assessed TRG (mrTRG) hazard ratios (HRs) were independently significant for survival (HR, 4.40; 95% CI, 1.65 to 11.7) and disease-free survival (DFS; HR, 3.28; 95% CI, 1.22 to 8.80). Five-year survival for poor mrTRG was 27% versus 72% (P = .001), and DFS for poor mrTRG was 31% versus 64% (P = .007). Preoperative MRI-predicted CRM independently predicted local recurrence (LR; HR, 4.25; 95% CI, 1.45 to 12.51). Five-year survival for poor post-treatment pathologic T stage (ypT) was 39% versus 76% (P = .001); DFS for the same was 38% versus 84% (P = .001); and LR for the same was 27% versus 6% (P = .018). The 5-year survival for involved pCRM was 30% versus 59% (P = .001); DFS, 28 versus 62% (P = .02); and LR, 56% versus 10% (P = .001). Pathology node status did not predict outcomes. MRI assessment of TRG and CRM are imaging markers that predict survival outcomes for good and poor responders and provide an opportunity for the multidisciplinary team to offer additional treatment options before planning definitive surgery. Postoperative histopathology assessment of ypT and CRM but not post-treatment N status were important postsurgical predictors of outcome.

[The contribution of the clinical examination, electroencephalogram, and brain MRI in assessing the prognosis in term newborns with neonatal encephalopathy. A cohort of 30 newborns before the introduction of treatment with hypothermia].

PubMed

Jadas, V; Brasseur-Daudruy, M; Chollat, C; Pellerin, L; Devaux, A M; Marret, S

2014-02-01

Perinatal asphyxia complicated by hypoxic ischemic brain injury remains a source of neurological lesions. A major aim of neonatologists is to evaluate the severity of neonatal encephalopathy (NE) and to evaluate prognosis. The purpose of this study was to determine the contribution of brain MRI compared to electroencephalogram (EEG) and clinical data in assessing patients' prognosis. Thirty newborns from the pediatric resuscitation unit at Rouen university hospital were enrolled in a retrospective study between January 2006 and December 2008, prior to introduction of hypothermia treatment. All 30 newborns had at least two anamnestic criteria of perinatal asphyxia, one brain MRI in the first 5 days of life and another after 7 days of life as well as an early EEG in the first 2 days of life. Then, the infants were seen in consultation to assess neurodevelopment. This study showed a relation between NE stage and prognosis. During stage 1, prognosis was good, whereas stage 3 was associated with poor neurodevelopment outcome. Normal clinical examination before the 8th day of life was a good prognostic factor in this study. There was a relationship between severity of EEG after the 5th day of life and poor outcome. During stage 2, EEG patterns varied in severity, and brain MRI provided a better prognosis. Lesions of the basal ganglia and a decreased or absent signal of the posterior limb of the internal capsule were poor prognostic factors during brain MRI. These lesions were underestimated during standard MRI in the first days of life but were visible with diffusion sequences. Cognitive impairment affected 40% of surviving children, justifying extended pediatric follow-up. This study confirms the usefulness of brain MRI as a diagnostic tool in hypoxic ischemic encephalopathy in association with clinical data and EEG tracings. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Perineural spread in head and neck tumors.

PubMed

Brea Álvarez, B; Tuñón Gómez, M

2014-01-01

Perineural spread is the dissemination of some types of head and neck tumors along nervous structures. Perineural spread has negative repercussions on treatment because it requires more extensive resection and larger fields of irradiation. Moreover, perineural spread is associated with increased local recurrence, and it is considered an independent indicator of poor prognosis in the TNM classification for tumor staging. However, perineural spread often goes undetected on imaging studies. In this update, we review the concept of perineural spread, its pathogenesis, and the main pathways and connections among the facial nerves, which are essential to understand this process. Furthermore, we discuss the appropriate techniques for imaging studies, and we describe and illustrate the typical imaging signs that help identify perineural spread on CT and MRI. Finally, we discuss the differential diagnosis with other entities. Copyright © 2013 SERAM. Published by Elsevier Espana. All rights reserved.

Ex vivo MRI evaluation of breast tumors: a novel tool for verifying resection of nonpalpable only MRI detected lesions.

PubMed

Agresti, Roberto; Trecate, Giovanna; Ferraris, Cristina; Valeri, Barbara; Maugeri, Ilaria; Pellitteri, Cristina; Martelli, Gabriele; Migliavacca, Silvana; Carcangiu, Maria Luisa; Bohm, Silvia; Maffioli, Lorenzo; Vergnaghi, Daniele; Panizza, Pietro

2013-01-01

A fundamental question in surgery of only magnetic resonance imaging (MRI)-detected breast lesions is to ensure their removal when they are not palpable by clinical examination and surgical exploration. This is especially relevant in the case of small tumors, carcinoma in situ or lobular carcinoma. Thirty-nine patients were enrolled in the study, 21 patients with breast lesions detected by both conventional imaging and breast MRI (bMRI) and 18 patients with bMRI findings only. Preoperative bMRI allowed staging the disease and localizing the lesion. In the operating theater, contrast medium was injected 1 minute before skin incision. After removal, surgical specimens were submitted to ex vivo MRI, performed using a dedicated surface coil and Spair inversion recovery sequences for suppression of fat signal intensity. All MRI enhancing lesions were completely included within the surgical specimen and visualized by ex vivo MRI. In the first 21 patients, bMRI was able to visualize branching margins or satellite nodules around the core lesion, and allowed for better staging of the surrounding in situ carcinoma; in the last 18 patients, eight of whom were breast cancer type 1 susceptibility protein (BRCA) mutation carriers, bMRI identified 12 malignant tumors, otherwise undetectable, that were all visualized by ex vivo MRI. This is the first description of a procedure that re-enhances breast lesions within a surgical specimen, demonstrating the surgical removal of nonpalpable breast lesions diagnosed only with bMRI. This new strategy reproduces the morphology and the entire extension of the primary lesion on the specimen, with potentially better local surgical control, reducing additional unplanned surgery. © 2013 Wiley Periodicals, Inc.

Identifying metastatic breast tumors using textural kinetic features of a contrast based habitat in DCE-MRI

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Drukteinis, Jennifer S.

2015-03-01

The ability to identify aggressive tumors from indolent tumors using quantitative analysis on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) would dramatically change the breast cancer treatment paradigm. With this prognostic information, patients with aggressive tumors that have the ability to spread to distant sites outside of the breast could be selected for more aggressive treatment and surveillance regimens. Conversely, patients with tumors that do not have the propensity to metastasize could be treated less aggressively, avoiding some of the morbidity associated with surgery, radiation and chemotherapy. We propose a computer aided detection framework to determine which breast cancers will metastasize to the loco-regional lymph nodes as well as which tumors will eventually go on to develop distant metastses using quantitative image analysis and radiomics. We defined a new contrast based tumor habitat and analyzed textural kinetic features from this habitat for classification purposes. The proposed tumor habitat, which we call combined-habitat, is derived from the intersection of two individual tumor sub-regions: one that exhibits rapid initial contrast uptake and the other that exhibits rapid delayed contrast washout. Hence the combined-habitat represents the tumor sub-region within which the pixels undergo both rapid initial uptake and rapid delayed washout. We analyzed a dataset of twenty-seven representative two dimensional (2D) images from volumetric DCE-MRI of breast tumors, for classification of tumors with no lymph nodes from tumors with positive number of axillary lymph nodes. For this classification an accuracy of 88.9% was achieved. Twenty of the twenty-seven patients were analyzed for classification of distant metastatic tumors from indolent cancers (tumors with no lymph nodes), for which the accuracy was 84.3%.

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

PubMed

Kim, Yong-Ku; Na, Kyoung-Sae

2018-01-03

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of a brain MRI-based hidden Markov model for dementia recognition.

PubMed

Chen, Ying; Pham, Tuan D

2013-01-01

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Role of New Functional MRI Techniques in the Diagnosis, Staging, and Followup of Gynecological Cancer: Comparison with PET-CT

PubMed Central

Alvarez Moreno, Elena; Jimenez de la Peña, Mar; Cano Alonso, Raquel

2012-01-01

Recent developments in diagnostic imaging techniques have magnified the role and potential of both MRI and PET-CT in female pelvic imaging. This article reviews the techniques and clinical applications of new functional MRI (fMRI) including diffusion-weighted MRI (DWI), dynamic contrast-enhanced (DCE)-MRI, comparing with PET-CT. These new emerging provide not only anatomic but also functional imaging, allowing detection of small volumes of active tumor at diagnosis and early disease relapse, which may not result in detectable morphological changes at conventional imaging. This information is useful in distinguishing between recurrent/residual tumor and post-treatment changes and assessing treatment response, with a clear impact on patient management. Both PET-CT and now fMRI have proved to be very valuable tools for evaluation of gynecologic tumors. Most papers try to compare these techniques, but in our experience both are complementary in management of these patients. Meanwhile PET-CT is superior in diagnosis of ganglionar disease; fMRI presents higher accuracy in local preoperative staging. Both techniques can be used as biomarkers of tumor response and present high accuracy in diagnosis of local recurrence and peritoneal dissemination, with complementary roles depending on histological type, anatomic location and tumoral volume. PMID:22315683

MRI and PET Imaging in Predicting Treatment Response in Patients With Stage IB-IVA Cervical Cancer

ClinicalTrials.gov

2018-06-18

Cervical Adenocarcinoma; Cervical Adenosquamous Carcinoma; Cervical Squamous Cell Carcinoma, Not Otherwise Specified; Cervical Undifferentiated Carcinoma; Recurrent Cervical Carcinoma; Stage IB2 Cervical Cancer; Stage II Cervical Cancer; Stage IIA Cervical Cancer; Stage IIB Cervical Cancer; Stage III Cervical Cancer; Stage IIIA Cervical Cancer; Stage IIIB Cervical Cancer; Stage IVA Cervical Cancer

Twenty-four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability

PubMed Central

Gao, Jianyong; Tian, Gang; Han, Xu; Zhu, Qiang

2018-01-01

Oral squamous cell carcinoma (OSCC) is the sixth most common type cancer worldwide, with poor prognosis. The present study aimed to identify gene signatures that could classify OSCC and predict prognosis in different stages. A training data set (GSE41613) and two validation data sets (GSE42743 and GSE26549) were acquired from the online Gene Expression Omnibus database. In the training data set, patients were classified based on the tumor-node-metastasis staging system, and subsequently grouped into low stage (L) or high stage (H). Signature genes between L and H stages were selected by disparity index analysis, and classification was performed by the expression of these signature genes. The established classification was compared with the L and H classification, and fivefold cross validation was used to evaluate the stability. Enrichment analysis for the signature genes was implemented by the Database for Annotation, Visualization and Integration Discovery. Two validation data sets were used to determine the precise of classification. Survival analysis was conducted followed each classification using the package ‘survival’ in R software. A set of 24 signature genes was identified based on the classification model with the Fi value of 0.47, which was used to distinguish OSCC samples in two different stages. Overall survival of patients in the H stage was higher than those in the L stage. Signature genes were primarily enriched in ‘ether lipid metabolism’ pathway and biological processes such as ‘positive regulation of adaptive immune response’ and ‘apoptotic cell clearance’. The results provided a novel 24-gene set that may be used as biomarkers to predict OSCC prognosis with high accuracy, which may be used to determine an appropriate treatment program for patients with OSCC in addition to the traditional evaluation index. PMID:29257303

Characteristics of early MRI in children and adolescents with vanishing white matter.

PubMed

van der Lei, Hannemieke D; Steenweg, Marjan E; Barkhof, Frederik; de Grauw, Ton; d'Hooghe, Marc; Morton, Richard; Shah, Siddharth; Wolf, Nicole; van der Knaap, Marjo S

2012-02-01

MRI in vanishing white matter typically shows diffuse abnormality of the cerebral white matter, which becomes increasingly rarefied and cystic. We investigated the MRI characteristics preceding this stage. In a retrospective observational study, we evaluated all available MRIs in our database of DNA-confirmed VWM patients and selected MRIs without diffuse cerebral white matter abnormalities and without signs of rarefaction or cystic degeneration in patients below 20 years of age. A previously established scoring list was used to evaluate the MRIs. An MRI of seven patients fulfilled the criteria. All had confluent and symmetrical abnormalities in the periventricular and bordering deep white matter. In young patients, myelination was delayed. The inner rim of the corpus callosum was affected in all patients. In early stages of VWM, MRI does not necessarily display diffuse cerebral white matter involvement and rarefaction or cystic degeneration. If the MRI abnormalities do not meet the criteria for VWM, it helps to look at the corpus callosum. If the inner rim (the callosal-septal interface) is affected, VWM should be considered. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Contrast-enhanced dynamic and diffusion-weighted magnetic resonance imaging at 3.0 T to assess early-stage nasopharyngeal carcinoma.

PubMed

Ni, Liangping; Liu, Ying

2018-04-01

The present study aimed to assess early-stage nasopharyngeal carcinoma (NPC) with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) at 3.0 T. A total of 44 patients newly diagnosed with NPC were included in the present study. All patients underwent MR examination at 3.0 T using DCE-MRI and DWI. The volume transfer constant ( K trans ), flux rate constant between extravascular extracellular space and plasma ( K ep ), the volume of extravascular extracellular space per unit volume of tissue ( V e ) and the apparent diffusion coefficient (ADC) of tumours were investigated. Furthermore, the correlation between clinical stages and ADC value and K trans were analysed. The diagnostic accuracy of K trans and ADC were estimated using receiver operating characteristic curves. NPC stage correlated positively with K trans and negatively with ADC values. Additionally, tumour K trans negatively correlated with ADC value. The sensitivity and accuracy of combined K trans and ADC in distinguishing between stage II and stage III and stage III and IV were higher than the values of either measurement used separately. The present study suggested that K trans and ADC derived from DCE-MRI and DWI may be useful to detect stage early NPC accurately. K trans and ADC in combination were superior than either alone.

Evaluation of T1/T2 ratios in a pilot study as a potential biomarker of biopsy: proven benign and malignant breast lesions in correlation with histopathological disease stage.

PubMed

Malikova, Marina A; Tkacz, Jaroslaw N; Slanetz, Priscilla J; Guo, Chao-Yu; Aakil, Adam; Jara, Hernan

2017-08-01

Early breast cancer detection is important for intervention and prognosis. Advances in treatment and outcome require diagnostic tools with highly positive predictive value. To study the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions. A cross-sectional study of 69 women with 69 known or suspicious breast lesions were scanned with mixed-turbo spin echo pulse sequence. Patients were grouped according to histopathological assessment of disease stage: untreated malignant tumor, treated malignancy and benign disease. Elevated T1/T2 means were observed for biopsy-proven malignant lesions and for malignant lesions treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. The qMRI-obtained T1/T2 ratios correlated with histopathology. Analysis revealed correlation between elevated T1/T2 ratio and disease stage. This could provide valuable complementary information on tissue properties as an additional diagnostic tool.

Diagnostic accuracy for macroscopic classification of nodular hepatocellular carcinoma: comparison of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging and angiography-assisted computed tomography.

PubMed

Tada, Toshifumi; Kumada, Takashi; Toyoda, Hidenori; Ito, Takanori; Sone, Yasuhiro; Okuda, Seiji; Ogawa, Sadanobu; Igura, Takumi; Imai, Yasuharu

2015-01-01

The macroscopic type of hepatocellular carcinoma (HCC) is a predictor of prognosis. We clarified the diagnostic value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) in the macroscopic classification of nodular hepatocellular carcinoma (HCC) as compared to angiography-assisted computed tomography (CT). A total of 71 surgically resected nodular HCCs with a maximum diameter of ≤5 cm were investigated. HCCs were evaluated preoperatively using Gd-EOB-DTPA-enhanced MRI and angiography-assisted CT. HCCs were pathologically classified as simple nodular (SN), SN with extranodular growth (SN-EG), or confluent multinodular (CMN). SN-EG and CMN were grouped as non-SN. Five readers independently reviewed the images using a five-point scale. We examined the accuracy of both imaging modalities in differentiating between SN and non-SN HCC. Overall, the area under the receiver operating characteristic curve (A z ) for the diagnosis of non-SN did not differ between Gd-EOB-DTPA-enhanced MRI and angiography-assisted CT [0.879 (95% confidence interval (CI), 0.779-0.937) and 0.845 (95% CI, 0.723-0.919), respectively]. For HCCs >2 cm, the A z for Gd-EOB-DTPA-enhanced MRI was greater than 0.9. The sensitivity, specificity, and accuracy of Gd-EOB-DTPA-enhanced MRI for identifying non-SN were equal to or higher than values with angiography-assisted CT in all three categories (all tumors, ≤2 cm, and >2 cm), but the differences were not statistically significant. Using Gd-EOB-DTPA-enhanced MRI to assess the macroscopic findings in nodular HCC was equal or superior to using angiography-assisted CT.

Lobar intracerebral haematomas: Neuropathological and 7.0-tesla magnetic resonance imaging evaluation.

PubMed

De Reuck, Jacques; Cordonnier, Charlotte; Deramecourt, Vincent; Auger, Florent; Durieux, Nicolas; Leys, Didier; Pasquier, Florence; Maurage, Claude-Alain; Bordet, Regis

2016-10-15

The Boston criteria for cerebral amyloid angiopathy (CAA) need validation by neuropathological examination in patients with lobar cerebral haematomas (LCHs). In "vivo" 1.5-tesla magnetic resonance imaging (MRI) is unreliable to detect the age-related signal changes in LCHs. This post-mortem study investigates the validity of the Boston criteria in brains with LCHs and the signal changes during their time course with 7.0-tesla MRI. Seventeen CAA brains including 26 LCHs were compared to 13 non-CAA brains with 14 LCHs. The evolution of the signal changes with time was examined in 25 LCHs with T2 and T2* 7.0-tesla MRI. In the CAA group LCHs were predominantly located in the parieto-occipital lobes. Also white matter changes were more severe with more cortical microinfarcts and cortical microbleeds. On MRI there was a progressive shift of the intensity of the hyposignal from the haematoma core in the acute stage to the boundaries later on. During the residual stage the hyposignal mildly decreased in the boundaries with an increase of the superficial siderosis and haematoma core collapse. Our post-mortem study of LCHs confirms the validity of the Boston criteria for CAA. Also 7.0-tesla MRI allows staging the age of the LCHs. Copyright © 2016 Elsevier B.V. All rights reserved.

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.

PubMed

Zhang, Han; Zuo, Xi-Nian; Ma, Shuang-Ye; Zang, Yu-Feng; Milham, Michael P; Zhu, Chao-Zhe

2010-07-15

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets. Copyright 2010 Elsevier Inc. All rights reserved.

Evaluation of Multimodal Imaging Biomarkers of Prostate Cancer

DTIC Science & Technology

2015-09-01

and PET images. Figure 2 highlights the dynamic uptake of TSPO as compared to muscle. Across 60 minutes the %ID/cc continues to increase which is...p53 double null mutant mouse model. Towards that end, we have successfully acquired anatomic MRI and PET data in orthotopic tumors within the Pten...castration resistant prostate cancer, MRI, PET , FDHT, image optimization 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES

Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis.

PubMed

Lin, Dongyun; Sun, Lei; Toh, Kar-Ann; Zhang, Jing Bo; Lin, Zhiping

2018-05-01

Automated biomedical image classification could confront the challenges of high level noise, image blur, illumination variation and complicated geometric correspondence among various categorical biomedical patterns in practice. To handle these challenges, we propose a cascade method consisting of two stages for biomedical image classification. At stage 1, we propose a confidence score based classification rule with a reject option for a preliminary decision using the support vector machine (SVM). The testing images going through stage 1 are separated into two groups based on their confidence scores. Those testing images with sufficiently high confidence scores are classified at stage 1 while the others with low confidence scores are rejected and fed to stage 2. At stage 2, the rejected images from stage 1 are first processed by a subspace analysis technique called eigenfeature regularization and extraction (ERE), and then classified by another SVM trained in the transformed subspace learned by ERE. At both stages, images are represented based on two types of local features, i.e., SIFT and SURF, respectively. They are encoded using various bag-of-words (BoW) models to handle biomedical patterns with and without geometric correspondence, respectively. Extensive experiments are implemented to evaluate the proposed method on three benchmark real-world biomedical image datasets. The proposed method significantly outperforms several competing state-of-the-art methods in terms of classification accuracy. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Classification and MR imaging of triangular fibrocartilage complex lesions].

PubMed

Zhan, H L; Liu, Y; Bai, R J; Qian, Z H; Ye, W; Li, Y X; Wu, B D

2016-06-07

To explore the MRI characteristics of injuries of triangular fibrocartilage complex (TFCC), and provide imaging basis for the early diagnosis and treatment of the injuries. A total of 10 healthy volunteers without wrist injuries and 200 patients from Beijing Jishuitan Hospital who complained ulnar-sided wrist pain and were highly suspected as the injury of TFCC underwent the wrist magnetic resonance examination. All subjects were in a prone position and underwent examination on coronal T1WI scan and PD-FS on 3 planes respectively. Then the MRI characteristics of 3 healthy volunteers and 67 patients with TFCC injuries that confirmed by operation were analyzed. According to the comparative analysis of normal anatomy and Palmer classification, the injuries were classified and MRI features of different types of injuries were analyzed. At last, imaging findings were compared with surgical results. Three healthy volunteers without injuries showed mainly in low signal intensity on T1WI and PD-FS images. According to Palmer classification, there were 52 traumatic injuries (ⅠA 9, ⅠB 25, ⅠC 3, ⅠD 13, In addition, 1 has central perforation and ulnar avulsion and 1 has ulnar and radial injuries simultaneously) and 15 degenerative injuries (ⅡA 5, ⅡB 1, ⅡC 2 , ⅡD 1 , ⅡE 6) among 67 patients. The central perforation mainly demonstrated as linear high signal perpendicular to the disk, and run in a sagittal line. The ulnar, distal, and radial avulsion mainly showed the injuries were irregular, the structures were ambiguous, and there was high signal intensity in the injured structures on PD-FS. Degenerative injuries demonstrated the irregularity of TFC and heterogeneous signals on PD-FS. There were mixed intermediate-high signals and changes in the articular cartilage of lunate and ulna, high signal in the lunotriquetral ligament and ulnocarpal or radioulnar arthritis. MRI can demonstrate the anatomy of TFCC accurately, evaluate and make the general classification of injuries. It is of significance for the early diagnosis and treatment protocols of the TFCC injuries.

Alterations of functional connectivities from early to middle adulthood: Clues from multivariate pattern analysis of resting-state fMRI data.

PubMed

Tian, Lixia; Ma, Lin; Wang, Linlin

2016-04-01

In contrast to extended research interests in the maturation and aging of human brain, alterations of brain structure and function from early to middle adulthood have been much less studied. The aim of the present study was to investigate the extent and pattern of the alterations of functional interactions between brain regions from early to middle adulthood. We carried out the study by multivariate pattern analysis of resting-state fMRI (RS-fMRI) data of 63 adults aged 18 to 45 years. Specifically, using elastic net, we performed brain age estimation and age-group classification (young adults aged 18-28 years vs. middle-aged adults aged 35-45 years) based on the resting-state functional connectivities (RSFCs) between 160 regions of interest (ROIs) evaluated on the RS-fMRI data of each subject. The results indicate that the estimated brain ages were significantly correlated with the chronological age (R=0.78, MAE=4.81), and a classification rate of 94.44% and area under the receiver operating characteristic curve (AUC) of 0.99 were obtained when classifying the young and middle-aged adults. These results provide strong evidence that functional interactions between brain regions undergo notable alterations from early to middle adulthood. By analyzing the RSFCs that contribute to brain age estimation/age-group classification, we found that a majority of the RSFCs were inter-network, and we speculate that inter-network RSFCs might mature late but age early as compared to intra-network ones. In addition, the strengthening/weakening of the RSFCs associated with the left/right hemispheric ROIs, the weakening of cortico-cerebellar RSFCs and the strengthening of the RSFCs between the default mode network and other networks contributed much to both brain age estimation and age-group classification. All these alterations might reflect that aging of brain function is already in progress in middle adulthood. Overall, the present study indicated that the RSFCs undergo notable alterations from early to middle adulthood and highlighted the necessity of careful considerations of possible influences of these alterations in related studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Keratoconus: The ABCD Grading System.

PubMed

Belin, M W; Duncan, J K

2016-06-01

To propose a new keratoconus classification/staging system that utilises current tomographic data and better reflects the anatomical and functional changes seen in keratoconus. A previously published normative database was reanalysed to generate both anterior and posterior average radii of curvature (ARC and PRC) taken from a 3.0 mm optical zone centred on the thinnest point of the cornea. Mean and standard deviations were recorded and anterior data were compared to the existing Amsler-Krumeich (AK) Classification. ARC, PRC, thinnest pachymetry and distance visual acuity were then used to construct a keratoconus classification. 672 eyes of 336 patients were analysed. Anterior and posterior values were 7.65 ± 0.236 mm and 6.26 ± 0.214 mm, respectively, and thinnest pachymetry values were 534.2 ± 30.36 µm. The ARC values were 2.63, 5.47 and 6.44 standard deviations from the mean values of stages 1-3 in the AK classification, respectively. PRC staging uses the same standard deviation gates. The pachymetric values differed by 4.42 and 7.72 standard deviations for stages 2 and 3, respectively. A new keratoconus staging incorporates anterior and posterior curvature, thinnest pachymetric values, and distance visual acuity and consists of stages 0-4 (5 stages). The proposed system closely matches the existing AK classification stages 1-4 on anterior curvature. As it incorporates posterior curvature and thickness measurements based on the thinnest point, rather than apical measurements, the new staging system better reflects the anatomical changes seen in keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

Endometrial cancer: magnetic resonance imaging.

PubMed

Manfredi, R; Gui, B; Maresca, G; Fanfani, F; Bonomo, L

2005-01-01

Carcinoma of the endometrium is the most common invasive gynecologic malignancy of the female genital tract. Clinically, patients with endometrial carcinoma present with abnormal uterine bleeding. The role of magnetic resonance imaging (MRI) in endometrial carcinoma is disease staging and treatment planning. MRI has been shown to be the most valuable imaging mod-ality in this task, compared with endovaginal ultrasound and computed tomography, because of its intrinsic contrast resolution and multiplanar capability. MRI protocol includes axial T1-weighted images; axial, sagittal, and coronal T2-weighted images; and dynamic gadolinium-enhanced T1-weighted imaging. MR examination is usually performed in the supine position with a phased array multicoil using a four-coil configuration. Endometrial carcinoma is isointense with the normal endometrium and myometrium on noncontrast T1-weighted images and has a variable appearance on T2-weighted images demonstrating heterogeneous signal intensity. The appearance of noninvasive endometrial carcinoma on MRI is characterized by a normal or thickened endometrium, with an intact junctional zone and a sharp tumor-myometrium interface. Invasive endometrial carcinoma is characterized disruption or irregularity of the junctional zone by intermediate signal intensity mass on T2-weighted images. Invasion of the cervical stroma is diagnosed when the low signal intensity cervical stroma is disrupted by the higher signal intensity endometrial carcinoma. MRI in endometrial carcinoma performs better than other imaging modalities in disease staging and treatment planning. Further, the accuracy and the cost of MRI are equivalent to those of surgical staging.

Follow up of MRI bone marrow edema in the treated diabetic Charcot foot – a review of patient charts

PubMed Central

Chantelau, Ernst-A.; Zweck, Brigitte; Haage, Patrick

2018-01-01

ABSTRACT Background: Ill-defined areas of water-like signal on bone magnetic resonance imaging (MRI), characterized as bone marrow edema or edema-equivalent signal-changes (EESC), is a hallmark of active-stage pedal neuro-osteoarthropathy (Charcot foot) in painless diabetic neuropathy, and is accompanied by local soft-tissue edema and hyperthermia. The longitudinal effects on EESC of treating the foot in a walking cast were elucidated by reviewing consecutive cases of a diabetic foot clinic. Study design: Retrospective observational study, chart review Material and methods: Cases with active-stage Charcot foot were considered, in whom written reports on baseline and follow-up MRI studies were available for assessment. Only cases without concomitant infection or skin ulcer were chosen, in whom both was documented, onset of symptomatic foot swelling and patient compliance with cast treatment. Results: From 1994 to 2017, 45 consecutive cases in 37 patients were retrieved, with 95 MRI follow-up studies (1–6 per case, average interval between studies 13 weeks). Decreasing EESC was documented in 66/95 (69%) follow-up studies. However, 29/95 (31%) studies revealed temporarily increasing, migrating or stagnating EESC. Conclusion: EESC on MRI disappear in response to prolonged offloading and immobilizing treatment; however, physiologic as well as pathologic fluctuations of posttraumatic EESC have to be considered when interpreting the MR images. Conventional MRI is useful for surveillance of active-stage Charcot foot recovery. PMID:29713425

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

NASA Astrophysics Data System (ADS)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Metric learning for automatic sleep stage classification.

PubMed

Phan, Huy; Do, Quan; Do, The-Luan; Vu, Duc-Lung

2013-01-01

We introduce in this paper a metric learning approach for automatic sleep stage classification based on single-channel EEG data. We show that learning a global metric from training data instead of using the default Euclidean metric, the k-nearest neighbor classification rule outperforms state-of-the-art methods on Sleep-EDF dataset with various classification settings. The overall accuracy for Awake/Sleep and 4-class classification setting are 98.32% and 94.49% respectively. Furthermore, the superior accuracy is achieved by performing classification on a low-dimensional feature space derived from time and frequency domains and without the need for artifact removal as a preprocessing step.

Low back pain: an assessment using positional MRI and MDT.

PubMed

Hedberg, Karen; Alexander, Lyndsay A; Cooper, Kay; Hancock, Elizabeth; Ross, Jenny; Smith, Francis W

2013-04-01

Current guidelines advise against the use of routine imaging for low back pain. Positional MRI can provide enhanced assessment of the lumbar spine in functionally loaded positions which are often relevant to the presenting clinical symptoms. The purpose of this case report is to highlight the use of positional MRI in the assessment and classification of a subject with low back pain. A low back pain subject underwent a Mechanical Diagnosis and Therapy (MDT) assessment and positional MRI scan of the lumbar spine. The MDT assessment classified the subject as "other" since the subjective history indicated a possible posterior derangement whilst the objective assessment indicated a possible anterior derangement. Positional MRI scanning in flexed, upright and extended sitting postures confirmed the MDT assessment findings to reveal a dynamic spinal stenosis which reduced in flexion and increased in extension. Copyright © 2012 Elsevier Ltd. All rights reserved.

Revision concepts and distinctive points of the new Japanese classification for biliary tract cancers in comparison with the 7(th) edition of the Union for International Cancer Control and the American Joint Committee on Cancer staging system.

PubMed

Ohtsuka, Masayuki; Miyakawa, Shuichi; Nagino, Masato; Takada, Tadahiro; Miyazaki, Masaru

2015-03-01

The 3(rd) English edition of the Japanese classification of the biliary tract cancers (JC) is now available in this journal. The primary aim of this revision is to provide all clinicians and researchers with a common language of cancer staging at an international level. On the other hand, there are several important issues that should be solved for the optimization of the staging system. Revision concepts and major revision points of the 3(rd) English edition of the JC were reviewed. Furthermore, comparing with the 7(th) edition of staging system developed by the Union for International Cancer Control (UICC) and the American Joint Committee on Cancer (AJCC), distinctive points in the JC was discussed. In this edition of the JC, the same stage groupings as those in the UICC/AJCC staging system were basically adopted. T, N, and M categories were also identical in principle with those in the UICC/AJCC staging system, although slight modifications were proposed as the "Japanese rules". As distinctive points, perihilar cholangiocarcinomas and ampullary region carcinomas were clearly defined. Intraepithelial tumor was discriminated from invasive carcinoma at ductal resection margins. Classifications of site-specific surgical margin status remained in this edition. Histological classification was based on that in the former editions of the JC, but adopted some parts of the World Health Organization classification. The JC now share its staging system of the biliary tact carcinomas with the UICC/AJCC staging system. Future validation of the "Japanese rules" could provide important evidence to make globally standardized staging system. © 2015 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

An Imaging Biomarker for Assessing Hepatic Function in Patients with Primary Sclerosing Cholangitis.

PubMed

Schulze, Jennifer; Lenzen, Henrike; Hinrichs, Jan B; Ringe, Burckhardt; Manns, Michael P; Wacker, Frank; Ringe, Kristina I

2018-05-15

We aimed to evaluate the potential of hepatobiliary phase magnetic resonance imaging (MRI) as parameter for assessment of hepatocellular function in patients with primary sclerosing cholangitis (PSC). We collected data from 111 patients (83 male, 28 female; median, 44 years old), from March 2012 through March 2016, with a confirmed diagnosis of PSC who underwent MRI evaluation before and after injection (hepatobiliary phase) of a hepatocyte-specific contrast agent (gadoxetate disodium). Signal intensities were measured in each liver segment. Mean relative enhancement values were calculated and correlated with findings from liver functions tests, prognostic scoring systems (model for end-stage liver disease [MELD] score; Mayo risk score; Amsterdam-Oxford-PSC score), abnormalities detected by endoscopic retrograde cholangiopancreatography (using the Amsterdam cholangiographic classification system), and clinical endpoints (liver transplantation, cholangiocarcinoma, liver-related death). Our primary aim was to associate relative enhancement values with liver function and patient outcomes. Most patients had moderate-stage disease and had intermediate levels of risk (median MELD score, 8 and median Mayo score, 0.27). Clinical endpoints were reached by 21 patients (6 developed cholangiocarcinoma, 8 underwent liver transplantation, and 7 patients died). The highest levels of correlations were observed for relative enhancement 20 min after contrast injection and level of alkaline phosphatase (r= -0.636), bilirubin (r= -0.646), albumin (r= 0.538); as well as international normalized ratio (r=0.456); MELD score (r= -0.587); Mayo risk score (r= -0.535), and Amsterdam-Oxford model score (r= -0.595) (P<.0001). Relative enhancement correlated with all clinical endpoints (all P<.05). A cutoff relative enhancement value of 0.65 identified patients with a clinical endpoint with 73.9% sensitivity 92.9% specificity (area under the receiver operating characteristic curve, 0.901; likelihood ratio, 10.34; P<.0001). In an analysis of 111 patients with PSC, we found MRI-measured relative enhancement, using a hepatocyte-specific contrast agent, to identify patients with clinical outcomes with 73.9% sensitivity 92.9% specificity. Long-term, multicenter studies are needed to further evaluate this marker of PSC progression. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Interobserver and intraobserver reliability of the modified Waldenström classification system for staging of Legg-Calvé-Perthes disease.

PubMed

Hyman, Joshua E; Trupia, Evan P; Wright, Margaret L; Matsumoto, Hiroko; Jo, Chan-Hee; Mulpuri, Kishore; Joseph, Benjamin; Kim, Harry K W

2015-04-15

The absence of a reliable classification system for Legg-Calvé-Perthes disease has contributed to difficulty in establishing consistent management strategies and in interpreting outcome studies. The purpose of this study was to assess interobserver and intraobserver reliability of the modified Waldenström classification system among a large and diverse group of pediatric orthopaedic surgeons. Twenty surgeons independently completed the first two rounds of staging: two assessments of forty deidentified radiographs of patients with Legg-Calvé-Perthes disease in various stages. Ten of the twenty surgeons completed another two rounds of staging after the addition of a second pair of radiographs in sequence. Kappa values were calculated within and between each of the rounds. Interobserver kappa values for the classification for surveys 1, 2, 3, and 4 were 0.81, 0.82, 0.76, and 0.80, respectively (with 0.61 to 0.80 considered substantial agreement and 0.81 to 1.0, nearly perfect agreement). Intraobserver agreement for the classification was an average of 0.88 (range, 0.77 to 0.96) between surveys 1 and 2 and an average of 0.87 (range, 0.81 to 0.94) between surveys 3 and 4. The modified Waldenström classification system for staging of Legg-Calvé-Perthes disease demonstrated substantial to almost perfect agreement between and within observers across multiple rounds of study. In doing so, the results of this study provide a foundation for future validation studies, in which the classification stage will be associated with clinical outcomes. Copyright © 2015 by The Journal of Bone and Joint Surgery, Incorporated.

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation.

PubMed

Dyrholm, Mads; Goldman, Robin; Sajda, Paul; Brown, Truman R

2009-02-01

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

Assessing paedophilia based on the haemodynamic brain response to face images.

PubMed

Ponseti, Jorge; Granert, Oliver; Van Eimeren, Thilo; Jansen, Olav; Wolff, Stephan; Beier, Klaus; Deuschl, Günther; Huchzermeier, Christian; Stirn, Aglaja; Bosinski, Hartmut; Roman Siebner, Hartwig

2016-01-01

Objective assessment of sexual preferences may be of relevance in the treatment and prognosis of child sexual offenders. Previous research has indicated that this can be achieved by pattern classification of brain responses to sexual child and adult images. Our recent research showed that human face processing is tuned to sexual age preferences. This observation prompted us to test whether paedophilia can be inferred based on the haemodynamic brain responses to adult and child faces. Twenty-four men sexually attracted to prepubescent boys or girls (paedophiles) and 32 men sexually attracted to men or women (teleiophiles) were exposed to images of child and adult, male and female faces during a functional magnetic resonance imaging (fMRI) session. A cross-validated, automatic pattern classification algorithm of brain responses to facial stimuli yielded four misclassified participants (three false positives), corresponding to a specificity of 91% and a sensitivity of 95%. These results indicate that the functional response to facial stimuli can be reliably used for fMRI-based classification of paedophilia, bypassing the problem of showing child sexual stimuli to paedophiles.

Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration.

PubMed

Young Kim, Eun; Johnson, Hans J

2013-01-01

A robust multi-modal tool, for automated registration, bias correction, and tissue classification, has been implemented for large-scale heterogeneous multi-site longitudinal MR data analysis. This work focused on improving the an iterative optimization framework between bias-correction, registration, and tissue classification inspired from previous work. The primary contributions are robustness improvements from incorporation of following four elements: (1) utilize multi-modal and repeated scans, (2) incorporate high-deformable registration, (3) use extended set of tissue definitions, and (4) use of multi-modal aware intensity-context priors. The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with quality assessments through the expert visual inspection. The implementation of this tool is tailored for, but not limited to, large-scale data processing with great data variation with a flexible interface. In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal MR scans collected at multi-sites. The tool was evaluated by using both simulated and simulated and human subject MRI images. With these enhancements, the results showed improved robustness for large-scale heterogeneous MRI processing.

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

PubMed

Zeng, Ling-Li; Wang, Huaning; Hu, Panpan; Yang, Bo; Pu, Weidan; Shen, Hui; Chen, Xingui; Liu, Zhening; Yin, Hong; Tan, Qingrong; Wang, Kai; Hu, Dewen

2018-04-01

A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Accuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the "disconnectivity" model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. Copyright © 2018 German Center for Neurodegenerative Diseases (DZNE). Published by Elsevier B.V. All rights reserved.

Functional Communication Profiles in Children with Cerebral Palsy in Relation to Gross Motor Function and Manual and Intellectual Ability.

PubMed

Choi, Ja Young; Park, Jieun; Choi, Yoon Seong; Goh, Yu Ra; Park, Eun Sook

2018-07-01

The aim of the present study was to investigate communication function using classification systems and its association with other functional profiles, including gross motor function, manual ability, intellectual functioning, and brain magnetic resonance imaging (MRI) characteristics in children with cerebral palsy (CP). This study recruited 117 individuals with CP aged from 4 to 16 years. The Communication Function Classification System (CFCS), Viking Speech Scale (VSS), Speech Language Profile Groups (SLPG), Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), and intellectual functioning were assessed in the children along with brain MRI categorization. Very strong relationships were noted among the VSS, CFCS, and SLPG, although these three communication systems provide complementary information, especially for children with mid-range communication impairment. These three communication classification systems were strongly related with the MACS, but moderately related with the GMFCS. Multiple logistic regression analysis indicated that manual ability and intellectual functioning were significantly related with VSS and CFCS function, whereas only intellectual functioning was significantly related with SLPG functioning in children with CP. Communication function in children with a periventricular white matter lesion (PVWL) varied widely. In the cases with a PVWL, poor functioning was more common on the SLPG, compared to the VSS and CFCS. Very strong relationships were noted among three communication classification systems that are closely related with intellectual ability. Compared to gross motor function, manual ability seemed more closely related with communication function in these children. © Copyright: Yonsei University College of Medicine 2018.

The Society for Vascular Surgery lower extremity threatened limb classification system based on Wound, Ischemia, and foot Infection (WIfI) correlates with risk of major amputation and time to wound healing.

PubMed

Zhan, Luke X; Branco, Bernardino C; Armstrong, David G; Mills, Joseph L

2015-04-01

The purpose of this study was to evaluate whether the new Society for Vascular Surgery (SVS) Wound, Ischemia, and foot Infection (WIfI) classification system correlates with important clinical outcomes for limb salvage and wound healing. A total of 201 consecutive patients with threatened limbs treated from 2010 to 2011 in an academic medical center were analyzed. These patients were stratified into clinical stages 1 to 4 on the basis of the SVS WIfI classification. The SVS objective performance goals of major amputation, 1-year amputation-free survival (AFS) rate, and wound healing time (WHT) according to WIfI clinical stages were compared. The mean age was 58 years (79% male, 93% with diabetes). Forty-two patients required major amputation (21%); 159 (78%) had limb salvage. The amputation group had a significantly higher prevalence of advanced stage 4 patients (P < .001), whereas the limb salvage group presented predominantly as stages 1 to 3. Patients in clinical stages 3 and 4 had a significantly higher incidence of amputation (P < .001), decreased AFS (P < .001), and delayed WHT (P < .002) compared with those in stages 1 and 2. Among patients presenting with stage 3, primarily as a result of wound and ischemia grades, revascularization resulted in accelerated WHT (P = .008). These data support the underlying concept of the SVS WIfI, that an appropriate classification system correlates with important clinical outcomes for limb salvage and wound healing. As the clinical stage progresses, the risk of major amputation increases, 1-year AFS declines, and WHT is prolonged. We further demonstrated benefit of revascularization to improve WHT in selected patients, especially those in stage 3. Future efforts are warranted to incorporate the SVS WIfI classification into clinical decision-making algorithms in conjunction with a comorbidity index and anatomic classification. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images.

PubMed

Farhan, Saima; Fahiem, Muhammad Abuzar; Tauseef, Huma

2014-01-01

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

PubMed

Ma, Xu; Cheng, Yongmei; Hao, Shuai

2016-12-10

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Evaluation of the Outcome of Lung Nodules Missed on 18F-FDG PET/MRI Compared with 18F-FDG PET/CT in Patients with Known Malignancies.

PubMed

Sawicki, Lino M; Grueneisen, Johannes; Buchbender, Christian; Schaarschmidt, Benedikt M; Gomez, Benedikt; Ruhlmann, Verena; Umutlu, Lale; Antoch, Gerald; Heusch, Philipp

2016-01-01

The lower detection rate of (18)F-FDG PET/MRI than (18)F-FDG PET/CT regarding small lung nodules should be considered in the staging of malignant tumors. The purpose of this study was to evaluate the outcome of these small lung nodules missed by (18)F-FDG PET/MRI. Fifty-one oncologic patients (mean age ± SD, 56.6 ± 14.0 y; 29 women, 22 men; tumor stages, I [n = 7], II [n = 7], III [n = 9], IV [n = 28]) who underwent (18)F-FDG PET/CT and subsequent (18)F-FDG PET/MRI on the same day were retrospectively enrolled. Images were analyzed by 2 interpreters in random order and separate sessions with a minimum of 4 wk apart. A maximum of 10 lung nodules was identified for each patient on baseline imaging. The presence, size, and presence of focal tracer uptake was noted for each lung nodule detected on (18)F-FDG PET/CT and (18)F-FDG PET/MRI using a postcontrast T1-weighted 3-dimensional gradient echo volume-interpolated breath-hold examination sequence with fat suppression as morphologic dataset. Follow-up CT or (18)F-FDG PET/CT (mean time to follow-up, 11 mo; range, 3-35 mo) was used as a reference standard to define each missed nodule as benign or malignant based on changes in size and potential new tracer uptake. Nodule-to-nodule comparison between baseline and follow-up was performed using descriptive statistics. Out of 134 lung nodules found on (18)F-FDG PET/CT, (18)F-FDG PET/MRI detected 92 nodules. Accordingly, 42 lung nodules (average size ± SD, 3.9 ± 1.3 mm; range, 2-7 mm) were missed by (18)F-FDG PET/MRI. None of the missed lung nodules presented with focal tracer uptake on baseline imaging or follow-up (18)F-FDG PET/CT. Thirty-three out of 42 missed lung nodules (78.6%) in 26 patients were rated benign, whereas 9 nodules (21.4%) in 4 patients were rated malignant. As a result, 1 patient required upstaging from tumor stage I to IV. Although most small lung nodules missed on (18)F-FDG PET/MRI were found to be benign, there was a relevant number of undetected metastases. However, in patients with advanced tumor stages the clinical impact remains controversial as upstaging is usually more relevant in lower stages. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Functional classification of schizophrenia using feed forward neural networks.

PubMed

Jafri, Madiha J; Calhoun, Vince D

2006-01-01

In medicine, the nature of an illness is often determined through behavioral or biological markers. The process of diagnosis becomes difficult when dealing with mental disorders since they rely primarily on behavioral markers. Schizophrenia is an example of a complex mental disorder that relies on aberrant behavior such as auditory hallucinations, dampening of emotions, paranoia, etc. This research is an attempt to determine a biological marker for schizophrenia through the use of functional magnetic resonance imaging (fMRI). In this paper, we propose a method of classification of schizophrenia and healthy controls, using a neural network approach and functional brain 'modes'estimated from resting state data using independent component analysis. A reliable technique for discriminating schizophrenia based upon fMRI would be a significant advance and may also provide additional information about the biological implications of mental illness.

Combining diffusion-weighted MRI with Gd-EOB-DTPA-enhanced MRI improves the detection of colorectal liver metastases.

PubMed

Koh, D-M; Collins, D J; Wallace, T; Chau, I; Riddell, A M

2012-07-01

To compare the diagnostic accuracy of gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced MRI, diffusion-weighted MRI (DW-MRI) and a combination of both techniques for the detection of colorectal hepatic metastases. 72 patients with suspected colorectal liver metastases underwent Gd-EOB-DTPA MRI and DW-MRI. Images were retrospectively reviewed with unenhanced T(1) and T(2) weighted images as Gd-EOB-DTPA image set, DW-MRI image set and combined image set by two independent radiologists. Each lesion detected was scored for size, location and likelihood of metastasis, and compared with surgery and follow-up imaging. Diagnostic accuracy was compared using receiver operating characteristics and interobserver agreement by kappa statistics. 417 lesions (310 metastases, 107 benign) were found in 72 patients. For both readers, diagnostic accuracy using the combined image set was higher [area under the curve (Az)=0.96, 0.97] than Gd-EOB-DTPA image set (Az=0.86, 0.89) or DW-MRI image set (Az=0.93, 0.92). Using combined image set improved identification of liver metastases compared with Gd-EOB-DTPA image set (p<0.001) or DW-MRI image set (p<0.001). There was very good interobserver agreement for lesion classification (κ=0.81-0.88). Combining DW-MRI with Gd-EOB-DTPA-enhanced T(1) weighted MRI significantly improved the detection of colorectal liver metastases.

PET Imaging of Tau Pathology and Relationship to Amyloid, Longitudinal MRI, and Cognitive Change in Down Syndrome: Results from the Down Syndrome Biomarker Initiative (DSBI).

PubMed

Rafii, Michael S; Lukic, Ana S; Andrews, Randolph D; Brewer, James; Rissman, Robert A; Strother, Stephen C; Wernick, Miles N; Pennington, Craig; Mobley, William C; Ness, Seth; Matthews, Dawn C

2017-01-01

Adults with Down syndrome (DS) represent an enriched population for the development of Alzheimer's disease (AD), which could aid the study of therapeutic interventions, and in turn, could benefit from discoveries made in other AD populations. 1) Understand the relationship between tau pathology and age, amyloid deposition, neurodegeneration (MRI and FDG PET), and cognitive and functional performance; 2) detect and differentiate AD-specific changes from DS-specific brain changes in longitudinal MRI. Twelve non-demented adults, ages 30 to 60, with DS were enrolled in the Down Syndrome Biomarker Initiative (DSBI), a 3-year, observational, cohort study to demonstrate the feasibility of conducting AD intervention/prevention trials in adults with DS. We collected imaging data with 18F-AV-1451 tau PET, AV-45 amyloid PET, FDG PET, and volumetric MRI, as well as cognitive and functional measures and additional laboratory measures. All amyloid negative subjects imaged were tau-negative. Among the amyloid positive subjects, three had tau in regions associated with Braak stage VI, two at stage V, and one at stage II. Amyloid and tau burden correlated with age. The MRI analysis produced two distinct volumetric patterns. The first differentiated DS from normal (NL) and AD, did not correlate with age or amyloid, and was longitudinally stable. The second pattern reflected AD-like atrophy and differentiated NL from AD. Tau PET and MRI atrophy correlated with several cognitive and functional measures. Tau accumulation is associated with amyloid positivity and age, as well as with progressive neurodegeneration measurable using FDG and MRI. Tau correlates with cognitive decline, as do AD-specific hypometabolism and atrophy.

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

PubMed

Song, Yang; Zhang, Yu-Dong; Yan, Xu; Liu, Hui; Zhou, Minxiong; Hu, Bingwen; Yang, Guang

2018-04-16

Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Retrospective. In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. T 2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting that DCNN could be used in computer-aided diagnosis (CAD) for PCa classification. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

[New International Classification of Chronic Pancreatitis (M-ANNHEIM multifactor classification system, 2007): principles, merits, and demerits].

PubMed

Tsimmerman, Ia S

2008-01-01

The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.

A new classification of a preovulatory oocyte maturation stage suitable for the synchronization of ovulation in controlled reproduction of Eurasian perch Perca fluviatilis L.

PubMed

Zarski, Daniel; Bokor, Zoltán; Kotrik, László; Urbanyi, Béla; Horváth, Akos; Targońska, Katarzyna; Krejszeff, Sławomir; Palińska, Katarzyna; Kucharczyk, Dariusz

2011-11-01

To improve controlled reproduction of Eurasian perch Perca fluviatilis, the criteria for the evaluation of final oocyte maturation stages were revised. The new classification covers six preovulatory maturational stages (I -VI) from the end of vitellogenesis to germinal vesicle breakdown (GVBD) and was based on macroscopic changes of preovulatory oocytes (position of the germinal vesicle, GVBD, oil droplets coalescence). The observation was performed during out-of-season artificial reproduction with the use of a single hCG injection (500 IU/kg). The classification was subsequently verified with the controlled reproduction of wild female perch with the use of hormonal stimulation (500 IU hCG/kg of body weight at 12°C). The females were at different maturational stages and constituted respective experimental groups (I-VI). During the experiment, ovulation appeared to be considerably synchronized within particular groups. Statistical differences in latency time (time between hormonal treatment and ovulation) were found between experimental groups (mean latency time: 110, 92, 68, 49, 29 and 18 h in groups representing VI, V, IV, III, II and I stage of the proposed classification, respectively). The proposed classification and the results presented in the study allowed for effective synchronisation of ovulation. The use of our new oocyte maturation classification may positively influence the effectiveness of Eurasian perch production.

Congenital portosystemic shunts in children: a new anatomical classification correlated with surgical strategy.

PubMed

Blanc, Thomas; Guerin, Florent; Franchi-Abella, Stéphanie; Jacquemin, Emmanuel; Pariente, Danièle; Soubrane, Olivier; Branchereau, Sophie; Gauthier, Frédéric

2014-07-01

To propose an anatomical classification of congenital portosystemic shunts (CPSs) correlating with conservative surgery. CPSs entail a risk of life-threatening complications because of poor portal inflow, which may be prevented or cured by their closure. Current classifications based on portal origin of the shunt are not helpful for planning conservative surgery. Twenty-three patients who underwent at least 1 surgical procedure to close the CPSs were included in this retrospective study (1997-2012). We designed a classification according to the ending of the shunt in the caval system. We analyzed the results and outcomes of surgery according to this classification. Two patients had an extrahepatic portosystemic shunt, 17 had a portacaval shunt [subdivided in 5 end-to-side-like portal-caval, 7 side-to-side-like portal-caval, and 5 H-shaped (H-type portal-caval)], 2 had portal-to-hepatic vein shunts (portohepatic), and 2 had a persistent ductus venosus. All extrahepatic portosystemic shunts, H-type portal-caval, portohepatic, and patent ductus venosus patients had a successful 1-stage ligation. All 5 end-to-side-like portal-caval patients had a threadlike intrahepatic portal venous system; a 2-stage complete closure was successfully achieved for 4 and a partial closure for 1. The first 2 side-to-side-like portal-caval patients had a successful 2-stage closure whereas the 5 others had a 1-stage longitudinal caval partition. All patients are alive and none needed a liver transplantation. Our classification correlates the anatomy of CPSs and the surgical strategy: outcomes are good provided end-to-side-like portal-caval shunts patients have a 2-stage closure, side-to-side portal-caval shunts patients have a 1-stage caval partition, and the others have a 1-stage ligation.

Numeric pathologic lymph node classification shows prognostic superiority to topographic pN classification in esophageal squamous cell carcinoma.

PubMed

Sugawara, Kotaro; Yamashita, Hiroharu; Uemura, Yukari; Mitsui, Takashi; Yagi, Koichi; Nishida, Masato; Aikou, Susumu; Mori, Kazuhiko; Nomura, Sachiyo; Seto, Yasuyuki

2017-10-01

The current eighth tumor node metastasis lymph node category pathologic lymph node staging system for esophageal squamous cell carcinoma is based solely on the number of metastatic nodes and does not consider anatomic distribution. We aimed to assess the prognostic capability of the eighth tumor node metastasis pathologic lymph node staging system (numeric-based) compared with the 11th Japan Esophageal Society (topography-based) pathologic lymph node staging system in patients with esophageal squamous cell carcinoma. We retrospectively reviewed the clinical records of 289 patients with esophageal squamous cell carcinoma who underwent esophagectomy with extended lymph node dissection during the period from January 2006 through June 2016. We compared discrimination abilities for overall survival, recurrence-free survival, and cancer-specific survival between these 2 staging systems using C-statistics. The median number of dissected and metastatic nodes was 61 (25% to 75% quartile range, 45 to 79) and 1 (25% to 75% quartile range, 0 to 3), respectively. The eighth tumor node metastasis pathologic lymph node staging system had a greater ability to accurately determine overall survival (C-statistics: tumor node metastasis classification, 0.69, 95% confidence interval, 0.62-0.76; Japan Esophageal Society classification; 0.65, 95% confidence interval, 0.58-0.71; P = .014) and cancer-specific survival (C-statistics: tumor node metastasis classification, 0.78, 95% confidence interval, 0.70-0.87; Japan Esophageal Society classification; 0.72, 95% confidence interval, 0.64-0.80; P = .018). Rates of total recurrence rose as the eighth tumor node metastasis pathologic lymph node stage increased, while stratification of patients according to the topography-based node classification system was not feasible. Numeric nodal staging is an essential tool for stratifying the oncologic outcomes of patients with esophageal squamous cell carcinoma even in the cohort in which adequate numbers of lymph nodes were harvested. Copyright © 2017 Elsevier Inc. All rights reserved.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

PubMed

Nguyen, Anthony N; Lawley, Michael J; Hansen, David P; Bowman, Rayleen V; Clarke, Belinda E; Duhig, Edwina E; Colquist, Shoni

2010-01-01

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Rectal cancer staging: Multidetector-row computed tomography diagnostic accuracy in assessment of mesorectal fascia invasion

PubMed Central

Ippolito, Davide; Drago, Silvia Girolama; Franzesi, Cammillo Talei; Fior, Davide; Sironi, Sandro

2016-01-01

AIM: To assess the diagnostic accuracy of multidetector-row computed tomography (MDCT) as compared with conventional magnetic resonance imaging (MRI), in identifying mesorectal fascia (MRF) invasion in rectal cancer patients. METHODS: Ninety-one patients with biopsy proven rectal adenocarcinoma referred for thoracic and abdominal CT staging were enrolled in this study. The contrast-enhanced MDCT scans were performed on a 256 row scanner (ICT, Philips) with the following acquisition parameters: tube voltage 120 KV, tube current 150-300 mAs. Imaging data were reviewed as axial and as multiplanar reconstructions (MPRs) images along the rectal tumor axis. MRI study, performed on 1.5 T with dedicated phased array multicoil, included multiplanar T2 and axial T1 sequences and diffusion weighted images (DWI). Axial and MPR CT images independently were compared to MRI and MRF involvement was determined. Diagnostic accuracy of both modalities was compared and statistically analyzed. RESULTS: According to MRI, the MRF was involved in 51 patients and not involved in 40 patients. DWI allowed to recognize the tumor as a focal mass with high signal intensity on high b-value images, compared with the signal of the normal adjacent rectal wall or with the lower tissue signal intensity background. The number of patients correctly staged by the native axial CT images was 71 out of 91 (41 with involved MRF; 30 with not involved MRF), while by using the MPR 80 patients were correctly staged (45 with involved MRF; 35 with not involved MRF). Local tumor staging suggested by MDCT agreed with those of MRI, obtaining for CT axial images sensitivity and specificity of 80.4% and 75%, positive predictive value (PPV) 80.4%, negative predictive value (NPV) 75% and accuracy 78%; while performing MPR the sensitivity and specificity increased to 88% and 87.5%, PPV was 90%, NPV 85.36% and accuracy 88%. MPR images showed higher diagnostic accuracy, in terms of MRF involvement, than native axial images, as compared to the reference magnetic resonance images. The difference in accuracy was statistically significant (P = 0.02). CONCLUSION: New generation CT scanner, using high resolution MPR images, represents a reliable diagnostic tool in assessment of loco-regional and whole body staging of advanced rectal cancer, especially in patients with MRI contraindications. PMID:27239115

Evaluating Mesorectal Lymph Nodes in Rectal Cancer Before and After Neoadjuvant Chemoradiation Using Thin-Section T2-Weighted Magnetic Resonance Imaging

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

Koh, Dow-Mu; Chau, Ian; Tait, Diana

2008-06-01

Purpose: To apply thin-section T2-weighted magnetic resoance imaging (MRI) to evaluate the number, size, distribution, and morphology of benign and malignant mesorectal lymph nodes before and after chemoradiation treatment compared with histopathologic findings. Methods and Materials: Twenty-five patients with poor-risk adenocarcinoma of the rectum treated with neoadjuvant chemoradiation were evaluated prospectively. Thin-section T2-weighted MR images obtained before and after chemoradiation treatment were independently reviewed in consensus by 2 expert radiologists to determine the tumor stage, nodal size, nodal distribution, and nodal stage. Total mesorectal excision surgery after chemoradiation allowed MR nodal stage to be compared with histopathology using {kappa} statistics.more » Nodal downstaging was compared using the Chi-square test. Results: Before chemoradiation, 152 mesorectal nodes were visible (mean, 6.2 mm; 100 benign, 52 malignant) and 4 of 52 malignant nodes were in contact with the mesorectal fascia. The nodal staging was 7/25 N0, 10/25 N1, and 7/25 N2. After chemoradiation, only 29 nodes (mean, 4.1 mm; 24 benign, 5 malignant) were visible, and none were in contact with the mesorectal fascia. Nodal downstaging was observed: 20/25 N0 and 5/25 N1 (p < 0.01, Chi-square test). There was good agreement between MRI and pathologic T-staging ({kappa} = 0.64) and N-staging ({kappa} = 0.65) after chemoradiation. Conclusions: Neoadjuvant chemoradiation treatment resulted in a decrease in size and number of malignant- and benign-appearing mesorectal nodes on MRI. Nodal downstaging and nodal regression from the mesorectal fascia were observed after treatment. MRI is a useful tool for assessing nodal response to neoadjuvant treatment.« less

Evaluation and validation of the diagnostic value of the apparent diffusion coefficient for differentiating early-stage endometrial carcinomas from benign mimickers at 3T MRI.

PubMed

Wang, Xue; Zhao, Yu; Hu, Yumin; Zhou, Yongjin; Ye, Xinjian; Liu, Kun; Bai, Guanghui; Guo, Anna; Du, Meimei; Jiang, Lezhen; Wang, Jinhong; Yan, Zhihan

2017-07-11

Previous researchers obtained various apparent diffusion coefficient (ADC) cutoff values to differentiate endometrial carcinoma from benign mimickers with 1.5T magnetic resonance imaging (MRI). Few studies have used 3T MRI or validated the effectiveness of these cutoff ADC values prospectively. This study was designed in two stages to obtain a cutoff ADC value at 3T MRI and to validate prospectively the role of the ADC value. First, we conducted a retrospective study of 60 patients to evaluate the diagnostic value of ADC by obtain a theoretical cutoff ADC value for differentiating between benign and malignant endometrial lesions. Student's t test revealed that ADC values for stage I endometrial carcinomas were significantly lower than those for benign lesions. The area under the curve value of the receiver operating characteristic curve was 0.993, and the cutoff ADC value was 0.98 × 10-3 mm2/s. The sensitivity, specificity, and overall accuracy of diagnosing stage I endometrial carcinoma were 100%, 97.1%, and 98.3%, respectively. Second, we conducted a prospective study of 26 patients to validate the use of the cutoff ADC value obtained in the study's first stage. The sensitivity, specificity, and overall accuracy for differentiating malignant from benign endometrial lesions based on the cutoff ADC value obtained earlier were as follows: radiologist 1 attained 86.67%, 100.0%, and 92.31%, respectively; radiologist 2 attained 86.67%, 91.0%, and 88.5%, respectively. Our results suggest that ADC values could be a potential biomarker for use as a quantitative and qualitative tool for differentiating between early-stage endometrial carcinomas and benign mimickers.

Evaluation and validation of the diagnostic value of the apparent diffusion coefficient for differentiating early-stage endometrial carcinomas from benign mimickers at 3T MRI

PubMed Central

Hu, Yumin; Zhou, Yongjin; Ye, Xinjian; Liu, Kun; Bai, Guanghui; Guo, Anna; Du, Meimei; Jiang, Lezhen

2017-01-01

Previous researchers obtained various apparent diffusion coefficient (ADC) cutoff values to differentiate endometrial carcinoma from benign mimickers with 1.5T magnetic resonance imaging (MRI). Few studies have used 3T MRI or validated the effectiveness of these cutoff ADC values prospectively. This study was designed in two stages to obtain a cutoff ADC value at 3T MRI and to validate prospectively the role of the ADC value. First, we conducted a retrospective study of 60 patients to evaluate the diagnostic value of ADC by obtain a theoretical cutoff ADC value for differentiating between benign and malignant endometrial lesions. Student's t test revealed that ADC values for stage I endometrial carcinomas were significantly lower than those for benign lesions. The area under the curve value of the receiver operating characteristic curve was 0.993, and the cutoff ADC value was 0.98 × 10−3 mm2/s. The sensitivity, specificity, and overall accuracy of diagnosing stage I endometrial carcinoma were 100%, 97.1%, and 98.3%, respectively. Second, we conducted a prospective study of 26 patients to validate the use of the cutoff ADC value obtained in the study's first stage. The sensitivity, specificity, and overall accuracy for differentiating malignant from benign endometrial lesions based on the cutoff ADC value obtained earlier were as follows: radiologist 1 attained 86.67%, 100.0%, and 92.31%, respectively; radiologist 2 attained 86.67%, 91.0%, and 88.5%, respectively. Our results suggest that ADC values could be a potential biomarker for use as a quantitative and qualitative tool for differentiating between early-stage endometrial carcinomas and benign mimickers. PMID:28634318

[Study of dynamic contrast-enhanced characteristics of stage-I endometrial carcinomas versus polyps with 3.0 T MRI].

PubMed

Wang, Xue; Lu, Yi; Zhang, Xiaoxia; Ji, Taotao; Liu, Kun; Ye, Xinjian; Bai, Guanghui; Yan, Zhihan

2015-01-20

To comparatively analyze the dynamic contrast-enhanced (DCE) characteristics and its clinical value between stage-I endometrial carcinomas versus polyps with 3.0T magnetic resonance imaging (MRI). A retrospective analysis was performed for DCE-MRI manifestation in 27 patients with histopathologically proved endometrial masses. There were stage-I endometrial carcinomas (n = 14) and polyps (n = 13). The signal intensity of solid component was measured and time-intensity curves (TIC) was obtained. TIC of lesions were divided into 4 subtypes. The time-to-peak (TTP) and signal intensity (SI) were determined from TICs. The arterial phase relative signal increase ratio (ARSIR), maximal relative signal increase ratio (MRSIR), signal enhancement ratio (SER) and signal intensity difference values (D) of each phase were calculated based on TIC curves respectively. The TIC of 14 stage-I endometrial carcinomas included type I (n = 4), type II (n = 6) and type IV (n = 4). The TIC of 13 polyps included type III (n = 3) and type IV (n = 10). The D values in each phase of 14 stage-I endometrial carcinomas were lower than normal muscle layers. There were statistic differences (P < 0.05) of each phase including 32, 48, 64, 109, 154, 199 s. For stage-I endometrial carcinomas, MRSIR and TTP were lower (P < 0.01) than normal muscle layers while SER was higher (P < 0.01) than normal muscle layers . The each phase of D of stage-I endometrial carcinomas were lower than polyps, and there were statistic differences (P < 0.05) of each phase including 32, 48, 64, 109, 154, 199 s. The MRSIR and TTP of stage-I endometrial carcinomas were lower (P < 0.01) than those of polyps while SER was higher (P < 0.01) than polyps. DCE-MRI can reflect enhanced features of stage-I endometrial carcinomas and polyps during different phases quantitatively. Parameters of DCE-MR and TIC are helpful in the diagnosis and differential diagnosis of stage-I endometrial carcinomas versus polyps.

Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

NASA Astrophysics Data System (ADS)

Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

2014-03-01

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

Computed tomography and magnetic resonance imaging of adult renal cell carcinoma associated with Xp11.2 translocation.

PubMed

Dang, Trien T; Ziv, Etay; Weinstein, Stefanie; Meng, Maxwell V; Wang, Zhen; Coakley, Fergus V

2012-01-01

This study aimed to report the computed tomography (CT) and magnetic resonance imaging (MRI) findings of renal cell carcinoma associated with Xp11.2 translocation in adults. We retrospectively identified 9 adults with renal cell carcinoma associated with Xp11.2 translocation who underwent baseline cross-sectional imaging with CT (n = 9) or MRI (n = 3). All available clinical, imaging, and histopathological records were reviewed. Mean patient age was 24 years (range, 18-45 years). Eight of 9 cancers demonstrated imaging findings of hemorrhage or necrosis (n = 3), advanced stage disease (n = 2), or both (n = 3) at CT or MRI. The possibility of renal cell carcinoma associated with Xp11.2 translocation should be considered for a renal mass seen in a patient 45 years or younger, which demonstrates hemorrhage or necrosis or advanced stage disease at CT or MRI.

Osteochondritis dissecans of the humeral capitellum: reliability of four classification systems using radiographs and computed tomography.

PubMed

Claessen, Femke M A P; van den Ende, Kimberly I M; Doornberg, Job N; Guitton, Thierry G; Eygendaal, Denise; van den Bekerom, Michel P J

2015-10-01

The radiographic appearance of osteochondritis dissecans (OCD) of the humeral capitellum varies according to the stage of the lesion. It is important to evaluate the stage of OCD lesion carefully to guide treatment. We compared the interobserver reliability of currently used classification systems for OCD of the humeral capitellum to identify the most reliable classification system. Thirty-two musculoskeletal radiologists and orthopaedic surgeons specialized in elbow surgery from several countries evaluated anteroposterior and lateral radiographs and corresponding computed tomography (CT) scans of 22 patients to classify the stage of OCD of the humeral capitellum according to the classification systems developed by (1) Minami, (2) Berndt and Harty, (3) Ferkel and Sgaglione, and (4) Anderson on a Web-based study platform including a Digital Imaging and Communications in Medicine viewer. Magnetic resonance imaging was not evaluated as part of this study. We measured agreement among observers using the Siegel and Castellan multirater κ. All OCD classification systems, except for Berndt and Harty, which had poor agreement among observers (κ = 0.20), had fair interobserver agreement: κ was 0.27 for the Minami, 0.23 for Anderson, and 0.22 for Ferkel and Sgaglione classifications. The Minami Classification was significantly more reliable than the other classifications (P < .001). The Minami Classification was the most reliable for classifying different stages of OCD of the humeral capitellum. However, it is unclear whether radiographic evidence of OCD of the humeral capitellum, as categorized by the Minami Classification, guides treatment in clinical practice as a result of this fair agreement. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Expert Reliability for the World Health Organization Standardized Ultrasound Classification of Cystic Echinococcosis

PubMed Central

Solomon, Nadia; Fields, Paul J.; Tamarozzi, Francesca; Brunetti, Enrico; Macpherson, Calum N. L.

2017-01-01

Cystic echinococcosis (CE), a parasitic zoonosis, results in cyst formation in the viscera. Cyst morphology depends on developmental stage. In 2003, the World Health Organization (WHO) published a standardized ultrasound (US) classification for CE, for use among experts as a standard of comparison. This study examined the reliability of this classification. Eleven international CE and US experts completed an assessment of eight WHO classification images and 88 test images representing cyst stages. Inter- and intraobserver reliability and observer performance were assessed using Fleiss' and Cohen's kappa. Interobserver reliability was moderate for WHO images (κ = 0.600, P < 0.0001) and substantial for test images (κ = 0.644, P < 0.0001), with substantial to almost perfect interobserver reliability for stages with pathognomonic signs (CE1, CE2, and CE3) for WHO (0.618 < κ < 0.904) and test images (0.642 < κ < 0.768). Comparisons of expert performances against the majority classification for each image were significant for WHO (0.413 < κ < 1.000, P < 0.005) and test images (0.718 < κ < 0.905, P < 0.0001); and intraobserver reliability was significant for WHO (0.520 < κ < 1.000, P < 0.005) and test images (0.690 < κ < 0.896, P < 0.0001). Findings demonstrate moderate to substantial interobserver and substantial to almost perfect intraobserver reliability for the WHO classification, with substantial to almost perfect interobserver reliability for pathognomonic stages. This confirms experts' abilities to reliably identify WHO-defined pathognomonic signs of CE, demonstrating that the WHO classification provides a reproducible way of staging CE. PMID:28070008

Spatial and temporal MRI profile of ischemic tissue after the acute stages of a permanent mouse model of stroke.

PubMed

Bogaert-Buchmann, A; Poittevin, M; Po, C; Dupont, D; Sebrié, C; Tomita, Y; Trandinh, A; Seylaz, J; Pinard, E; Méric, P; Kubis, N; Gillet, B

2013-01-01

To characterize the progression of injured tissue resulting from a permanent focal cerebral ischemia after the acute phase, Magnetic Resonance Imaging (MRI) monitoring was performed on adult male C57BL/6J mice in the subacute stages, and correlated to histological analyses. Lesions were induced by electrocoagulation of the middle cerebral artery. Serial MRI measurements and weighted-images (T2, T1, T2* and Diffusion Tensor Imaging) were performed on a 9.4T scanner. Histological data (Cresyl-Violet staining and laminin-, Iba1- and GFAP-immunostainings) were obtained 1 and 2 weeks after the stroke. Two days after stroke, tissues assumed to correspond to the infarct core, were detected as a hyperintensity signal area in T2-weighted images. One week later, low-intensity signal areas appeared. Longitudinal MRI study showed that these areas remained present over the following week, and was mainly linked to a drop of the T2 relaxation time value in the corresponding tissues. Correlation with histological data and immuno-histochemistry showed that these areas corresponded to microglial cells. The present data provide, for the first time detailed MRI parameters of microglial cells dynamics, allowing its non-invasive monitoring during the chronic stages of a stroke. This could be particularly interesting in regards to emerging anti-inflammatory stroke therapies.

Spatial and Temporal MRI Profile of Ischemic Tissue after the Acute Stages of a Permanent Mouse Model of Stroke

PubMed Central

Bogaert-Buchmann, A; Poittevin, M; Po, C; Dupont, D; Sebrié, C; Tomita, Y; TranDinh, A; Seylaz, J; Pinard, E; Méric, P; Kubis, N; Gillet, B

2013-01-01

Object: To characterize the progression of injured tissue resulting from a permanent focal cerebral ischemia after the acute phase, Magnetic Resonance Imaging (MRI) monitoring was performed on adult male C57BL/6J mice in the subacute stages, and correlated to histological analyses. Material and methods: Lesions were induced by electrocoagulation of the middle cerebral artery. Serial MRI measurements and weighted-images (T2, T1, T2* and Diffusion Tensor Imaging) were performed on a 9.4T scanner. Histological data (Cresyl-Violet staining and laminin-, Iba1- and GFAP-immunostainings) were obtained 1 and 2 weeks after the stroke. Results: Two days after stroke, tissues assumed to correspond to the infarct core, were detected as a hyperintensity signal area in T2-weighted images. One week later, low-intensity signal areas appeared. Longitudinal MRI study showed that these areas remained present over the following week, and was mainly linked to a drop of the T2 relaxation time value in the corresponding tissues. Correlation with histological data and immuno-histochemistry showed that these areas corresponded to microglial cells. Conclusion: The present data provide, for the first time detailed MRI parameters of microglial cells dynamics, allowing its non-invasive monitoring during the chronic stages of a stroke. This could be particularly interesting in regards to emerging anti-inflammatory stroke therapies. PMID:23459141

Empirical study of classification process for two-stage turbo air classifier in series

NASA Astrophysics Data System (ADS)

Yu, Yuan; Liu, Jiaxiang; Li, Gang

2013-05-01

The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the "fish-hook" effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the "fish-hook" effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.

Intraindividual Comparison of 18F-PSMA-1007 PET/CT, Multiparametric MRI, and Radical Prostatectomy Specimens in Patients with Primary Prostate Cancer: A Retrospective, Proof-of-Concept Study.

PubMed

Kesch, Claudia; Vinsensia, Maria; Radtke, Jan P; Schlemmer, Heinz P; Heller, Martina; Ellert, Elena; Holland-Letz, Tim; Duensing, Stefan; Grabe, Nils; Afshar-Oromieh, Ali; Wieczorek, Kathrin; Schäfer, Martin; Neels, Oliver C; Cardinale, Jens; Kratochwil, Clemens; Hohenfellner, Markus; Kopka, Klaus; Haberkorn, Uwe; Hadaschik, Boris A; Giesel, Frederik L

2017-11-01

68 Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT represents an advanced method for the staging of primary prostate cancer (PCa) and diagnosis of recurrent or metastatic PCa. However, because of the narrow availability of 68 Ga the development of alternative tracers is of high interest. The objective of this study was to examine the value of the new PET tracer 18 F-PSMA-1007 for the staging of local disease by comparing it with multiparametric MRI (mpMRI) and radical prostatectomy (RP) histopathology. Methods: In 2016, 18 F-PSMA-1007 PET/CT was performed in 10 men with biopsy-confirmed high-risk PCa. Nine patients underwent mpMRI in the process of primary diagnosis. Consecutively, RP was performed in all 10 men. Agreement analysis was performed retrospectively. PSMA staining was added for representative sections in RP specimen slices. Localization and agreement analysis of 18 F-PSMA-1007 PET/CT, mpMRI, and RP specimens was performed by dividing the prostate into 38 sections as described in the prostate imaging reporting and data system (PI-RADS) (version 2). Sensitivity, specificity, positive predictive values, negative predictive values (NPVs), and accuracy were calculated for total and near-total agreement. Results: 18 F-PSMA-1007 PET/CT had an NPV of 68% and an accuracy of 75%, and mpMRI had an NPV of 88% and an accuracy of 73% for total agreement. Near-total agreement analysis resulted in an NPV of 91% and an accuracy of 93% for 18 F-PSMA-1007 PET/CT and 91% and 87% for mpMRI, respectively. Retrospective combination of mpMRI and PET/CT had an accuracy of 81% for total and 93% for near-total agreement. Conclusion: Comparison with RP histopathology demonstrates that 18 F-PSMA-1007 PET/CT is promising for accurate local staging of PCa. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Intra-individual comparison of (68)Ga-PSMA-11-PET/CT and multi-parametric MR for imaging of primary prostate cancer.

PubMed

Giesel, F L; Sterzing, F; Schlemmer, H P; Holland-Letz, T; Mier, W; Rius, M; Afshar-Oromieh, A; Kopka, K; Debus, J; Haberkorn, U; Kratochwil, C

2016-07-01

Multi-parametric magnetic resonance imaging (MP-MRI) is currently the most comprehensive work up for non-invasive primary tumor staging of prostate cancer (PCa). Prostate-specific membrane antigen (PSMA)-Positron emission tomography-computed tomography (PET/CT) is presented to be a highly promising new technique for N- and M-staging in recurrent PCa-patients. The actual investigation analyses the potential of (68)Ga-PSMA11-PET/CT to assess the extent of primary prostate cancer by intra-individual comparison to MP-MRI. In a retrospective study, ten patients with primary PCa underwent MP-MRI and PSMA-PET/CT for initial staging. All tumors were proven histopathological by biopsy. Image analysis was done in a quantitative (SUVmax) and qualitative (blinded read) fashion based on PI-RADS. The PI-RADS schema was then translated into a 3D-matrix and the euclidian distance of this coordinate system was used to quantify the extend of agreement. Both MP-MRI and PSMA-PET/CT presented a good allocation of the PCa, which was also in concordance to the tumor location validated in eight-segment resolution by biopsy. An Isocontour of 50 % SUVmax in PSMA-PET resulted in visually concordant tumor extension in comparison to MP-MRI (T2w and DWI). For 89.4 % of sections containing a tumor according to MP-MRI, the tumor was also identified in total or near-total agreement (euclidian distance ≤1) by PSMA-PET. Vice versa for 96.8 % of the sections identified as tumor bearing by PSMA-PET the tumor was also found in total or near-total agreement by MP-MRI. PSMA-PET/CT and MP-MRI correlated well with regard to tumor allocation in patients with a high pre-test probability for large tumors. Further research will be needed to evaluate its value in challenging situation such as prostatitis or after repeated negative biopsies.

MRI With Gadoxetate Disodium in Measuring Tumors in Patients With Liver Cancer

ClinicalTrials.gov

2015-10-14

Adult Hepatocellular Carcinoma; Advanced Adult Hepatocellular Carcinoma; BCLC Stage 0 Adult Hepatocellular Carcinoma; BCLC Stage A Adult Hepatocellular Carcinoma; BCLC Stage B Adult Hepatocellular Carcinoma; BCLC Stage C Adult Hepatocellular Carcinoma; BCLC Stage D Adult Hepatocellular Carcinoma; Localized Non-Resectable Adult Liver Carcinoma; Localized Resectable Adult Liver Carcinoma

Inter-Observer Agreement of Whole-Body Computed Tomography in Staging and Response Assessment in Lymphoma: The Lugano Classification.

PubMed

Razek, Ahmed Abdel Khalek Abdel; Shamaa, Sameh; Lattif, Mahmoud Abdel; Yousef, Hanan Hamid

2017-01-01

To assess inter-observer agreement of whole-body computed tomography (WBCT) in staging and response assessment in lymphoma according to the Lugano classification. Retrospective analysis was conducted of 115 consecutive patients with lymphomas (45 females, 70 males; mean age of 46 years). Patients underwent WBCT with a 64 multi-detector CT device for staging and response assessment after a complete course of chemotherapy. Image analysis was performed by 2 reviewers according to the Lugano classification for staging and response assessment. The overall inter-observer agreement of WBCT in staging of lymphoma was excellent ( k =0.90, percent agreement=94.9%). There was an excellent inter-observer agreement for stage I ( k =0.93, percent agreement=96.4%), stage II ( k =0.90, percent agreement=94.8%), stage III ( k =0.89, percent agreement=94.6%) and stage IV ( k =0.88, percent agreement=94%). The overall inter-observer agreement in response assessment after a completer course of treatment was excellent ( k =0.91, percent agreement=95.8%). There was an excellent inter-observer agreement in progressive disease ( k =0.94, percent agreement=97.1%), stable disease ( k =0.90, percent agreement=95%), partial response ( k =0.96, percent agreement=98.1%) and complete response ( k =0.87, Percent agreement=93.3%). We concluded that WBCT is a reliable and reproducible imaging modality for staging and treatment assessment in lymphoma according to the Lugano classification.

Multi-institutional analysis of CT and MRI reports evaluating indeterminate renal masses: comparison to a national survey investigating desired report elements.

PubMed

Hu, Eric M; Zhang, Andrew; Silverman, Stuart G; Pedrosa, Ivan; Wang, Zhen J; Smith, Andrew D; Chandarana, Hersh; Doshi, Ankur; Shinagare, Atul B; Remer, Erick M; Kaffenberger, Samuel D; Miller, David C; Davenport, Matthew S

2018-04-17

To determine the need for a standardized renal mass reporting template by analyzing reports of indeterminate renal masses and comparing their contents to stated preferences of radiologists and urologists. The host IRB waived regulatory oversight for this multi-institutional HIPAA-compliant quality improvement effort. CT and MRI reports created to characterize an indeterminate renal mass were analyzed from 6 community (median: 17 reports/site) and 6 academic (median: 23 reports/site) United States practices. Report contents were compared to a published national survey of stated preferences by academic radiologists and urologists from 9 institutions. Descriptive statistics and Chi-square tests were calculated. Of 319 reports, 85% (271; 192 CT, 79 MRI) reported a possibly malignant mass (236 solid, 35 cystic). Some essential elements were commonly described: size (99% [269/271]), mass type (solid vs. cystic; 99% [268/271]), enhancement (presence vs. absence; 92% [248/271]). Other essential elements had incomplete penetrance: the presence or absence of fat in solid masses (14% [34/236]), size comparisons when available (79% [111/140]), Bosniak classification for cystic masses (54% [19/35]). Preferred but non-essential elements generally were described in less than half of reports. Nephrometry scores usually were not included for local therapy candidates (12% [30/257]). Academic practices were significantly more likely than community practices to include mass characterization details, probability of malignancy, and staging. Community practices were significantly more likely to include management recommendations. Renal mass reporting elements considered essential or preferred often are omitted in radiology reports. Variation exists across radiologists and practice settings. A standardized template may mitigate these inconsistencies.

Performance assessment of automated tissue characterization for prostate H and E stained histopathology

NASA Astrophysics Data System (ADS)

DiFranco, Matthew D.; Reynolds, Hayley M.; Mitchell, Catherine; Williams, Scott; Allan, Prue; Haworth, Annette

2015-03-01

Reliable automated prostate tumor detection and characterization in whole-mount histology images is sought in many applications, including post-resection tumor staging and as ground-truth data for multi-parametric MRI interpretation. In this study, an ensemble-based supervised classification algorithm for high-resolution histology images was trained on tile-based image features including histogram and gray-level co-occurrence statistics. The algorithm was assessed using different combinations of H and E prostate slides from two separate medical centers and at two different magnifications (400x and 200x), with the aim of applying tumor classification models to new data. Slides from both datasets were annotated by expert pathologists in order to identify homogeneous cancerous and non-cancerous tissue regions of interest, which were then categorized as (1) low-grade tumor (LG-PCa), including Gleason 3 and high-grade prostatic intraepithelial neoplasia (HG-PIN), (2) high-grade tumor (HG-PCa), including various Gleason 4 and 5 patterns, or (3) non-cancerous, including benign stroma and benign prostatic hyperplasia (BPH). Classification models for both LG-PCa and HG-PCa were separately trained using a support vector machine (SVM) approach, and per-tile tumor prediction maps were generated from the resulting ensembles. Results showed high sensitivity for predicting HG-PCa with an AUC up to 0.822 using training data from both medical centres, while LG-PCa showed a lower sensitivity of 0.763 with the same training data. Visual inspection of cancer probability heatmaps from 9 patients showed that 17/19 tumors were detected, and HG-PCa generally reported less false positives than LG-PCa.

Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.

PubMed

Yu, Renping; Zhang, Han; An, Le; Chen, Xiaobo; Wei, Zhihui; Shen, Dinggang

2017-05-01

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l 1 -norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Tissue Tracking: Applications for Brain MRI Classification

DTIC Science & Technology

2007-01-01

General Hospital, Center for Morphometric Analysis.10,11 The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally...learned priors,” Image Processing, IEEE Transactions on 9(2), pp. 299–301, 2000. 5. P. Olver, G. Sapiro, and A. Tannenbaum, “Invariant Geometric Evolutions...MRI,” NeuroImage 22(3), pp. 1060–1075, 2004. 16. A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer, “ Morphometric analysis of white matter lesions in

MRI Features of Hepatocellular Carcinoma Related to Biologic Behavior

PubMed Central

Cho, Eun-Suk

2015-01-01

Imaging studies including magnetic resonance imaging (MRI) play a crucial role in the diagnosis and staging of hepatocellular carcinoma (HCC). Several recent studies reveal a large number of MRI features related to the prognosis of HCC. In this review, we discuss various MRI features of HCC and their implications for the diagnosis and prognosis as imaging biomarkers. As a whole, the favorable MRI findings of HCC are small size, encapsulation, intralesional fat, high apparent diffusion coefficient (ADC) value, and smooth margins or hyperintensity on the hepatobiliary phase of gadoxetic acid-enhanced MRI. Unfavorable findings include large size, multifocality, low ADC value, non-smooth margins or hypointensity on hepatobiliary phase images. MRI findings are potential imaging biomarkers in patients with HCC. PMID:25995679

Relative Value of Restaging MRI, CT, and FDG-PET Scan After Preoperative Chemoradiation for Rectal Cancer.

PubMed

Schneider, Daniel A; Akhurst, Timothy J; Ngan, Samuel Y; Warrier, Satish K; Michael, Michael; Lynch, Andrew C; Te Marvelde, Luc; Heriot, Alexander G

2016-03-01

Management of rectal cancer has become multidisciplinary and is driven by the stage of the disease, with increased focus on restaging rectal cancer after neoadjuvant therapy. The purpose of this study was to assess the relative impact of restaging after preoperative chemoradiation with FDG-PET scan, CT, and MRI in the management of patients with rectal cancer. This was a retrospective study from a single institution. This study was conducted at a tertiary cancer center. A total of 199 patients met the inclusion criteria: patients with rectal adenocarcinoma; staged with positron emission tomography, CT, and MRI; T2 to T4, N0 to N2, M0 to M1; treated with neoadjuvant chemoradiation 50.4 Gy and infusional 5-fluorouracil; and restaged 4 weeks after chemoradiation before surgery between 2003 and 2013. Comparisons of the tumor stage among different imaging modalities before and after neoadjuvant chemoradiation were performed. The impact of restaging on the management plan was assessed. The stage at presentation was T2, 8.04%; T3, 65.33%; T4, 26.63%; N0, 17.09%; N1, 47.74%; N2, 34.67%; M0, 81.91%; and M1, 18.09%. Changes in disease stage postneoadjuvant chemoradiation were observed in 99 patients (50%). The management plans of 29 patients (15%) were changed. The impact of each restaging modality on management for all of the patients was positron emission tomography, 11%; CT, 4%; and MRI, 4%. In patients with metastatic disease at primary staging, the relative impact of each restaging modality in changing management was positron emission tomography, 32%; CT, 18%; and MRI, 6%. This study was limited by its single-center and retrospective design. Operations were performed 4 weeks after restaging. Changes in the extent of disease after long-course chemoradiotherapy result in changes of management in a significant percentage of patients. Positron emission tomography has the most significant impact in the change of management overall, and its use in restaging advanced rectal cancer should be further explored.

Recursive Partitioning Analysis for New Classification of Patients With Esophageal Cancer Treated by Chemoradiotherapy

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

Nomura, Motoo, E-mail: excell@hkg.odn.ne.jp; Department of Clinical Oncology, Aichi Cancer Center Hospital, Nagoya; Department of Radiation Oncology, Aichi Cancer Center Hospital, Nagoya

2012-11-01

Background: The 7th edition of the American Joint Committee on Cancer staging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the maximum metastatic lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Methods: Information on 402 patients with esophageal cancer undergoing CRT at two institutions was reviewed. Univariate and multivariate analyses of data from one institution were used to assessmore » the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. Results: By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p < 0.05). The resulting new staging classification was based on the T and ND. The four new stages led to good separation of survival curves in both the developmental and validation datasets (p < 0.05). Conclusions: Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power, and discriminated effectively for patients with esophageal cancer undergoing CRT.« less

[The diagnostic value of cine-MR imaging in diseases of great vessels].

PubMed

Sasaki, S; Yoshida, H; Matsui, Y; Sakuma, M; Yasuda, K; Tanabe, T; Chouji, H

1990-02-01

The diagnostic value of cine magnetic resonance imaging (cine-MRI) was evaluated in 10 patients with disease of great vessels. The parameters necessary to decide the appropriate treatment, such as presence and extension of intimal flap, DeBakey type classification, identification of the entry, differentiation between true and false lumen, and between thrombosis and slow flow were demonstrated in all patients with dissecting aortic aneurysm. However, abdominal aortic branches could not be demonstrated enough by cine-MRI, therefore conventional AOG was necessary to choose the operative procedure in these cases. In patients with thoracic aortic aneurysm (TAA), cine-MRI was valuable in demonstrating both blood flow and thrombus in the lumen of aneurysm, and AOG was thought to be unnecessary in most cases. Cine-MRI is a promising new technique for the evaluation of diseases of great vessels.

Visualization of suspicious lesions in breast MRI based on intelligent neural systems

NASA Astrophysics Data System (ADS)

Twellmann, Thorsten; Lange, Oliver; Nattkemper, Tim Wilhelm; Meyer-Bäse, Anke

2006-05-01

Intelligent medical systems based on supervised and unsupervised artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems represent an important component of future sophisticated computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

Classification of fMRI resting-state maps using machine learning techniques: A comparative study

NASA Astrophysics Data System (ADS)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

We compare the efficiency of Principal Component Analysis (PCA) and nonlinear learning manifold algorithms (ISOMAP and Diffusion maps) for classifying brain maps between groups of schizophrenia patients and healthy from fMRI scans during a resting-state experiment. After a standard pre-processing pipeline, we applied spatial Independent component analysis (ICA) to reduce (a) noise and (b) spatial-temporal dimensionality of fMRI maps. On the cross-correlation matrix of the ICA components, we applied PCA, ISOMAP and Diffusion Maps to find an embedded low-dimensional space. Finally, support-vector-machines (SVM) and k-NN algorithms were used to evaluate the performance of the algorithms in classifying between the two groups.

Imaging Characteristics of Prostate Cancer Patients Who Discontinued Active Surveillance on 3-T Multiparametric Prostate MRI.

PubMed

Habibian, David J; Liu, Corinne C; Dao, Alex; Kosinski, Kaitlin E; Katz, Aaron E

2017-03-01

Early-stage prostate cancer may be followed with active surveillance to avoid overtreatment. Our institution's active surveillance regimen uses annual MRI in place of serial biopsies, and biopsies are performed only when clinically necessary. The objective of our study was to report the multiparametric MRI characteristics of prostate cancer patients who discontinued active surveillance at our institution after repeat imaging revealed possible evidence of tumor upgrading. The Department of Urology at Winthrop University Hospital prospectively maintains a database of prostate cancer patients who are monitored with active surveillance. At the time of this study, there were 200 prostate cancer patients being monitored with active surveillance. Of those patients, 114 patients had an initial multiparametric MRI study that was performed before active surveillance started and at least one follow-up multiparametric MRI study that was performed after active surveillance began. The MRI findings were evaluated and correlated with pathology results, if available. Fourteen patients discontinued active surveillance because changes on follow-up MRI suggested progression of cancer. Follow-up MRI showed an enlarged or more prominent lesion compared with the appearance on a previous MRI in three (21.4%) patients, a new lesion or lesions suspicious for cancer in two (14.3%) patients, and findings suspicious for or confirming extracapsular extension in nine (64.3%) patients. Seven of the 14 (50.0%) patients had a biopsy after follow-up multiparametric MRI, and biopsy results led to tumor upgrading in six of the 14 (42.9%) patients. The duration of active surveillance ranged from 4 to 110 months. All patients received definitive treatment. The small number of patients with follow-up multiparametric MRI findings showing worsening disease supports the role of MRI in patients with early-stage prostate cancer. Multiparametric MRI is useful in monitoring patients on active surveillance and may identify patients with clinically significant cancer amenable to definitive treatment.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

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

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Multiple Sparse Representations Classification

PubMed Central

Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik

2015-01-01

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and sparsity level. PMID:26177106

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions.

PubMed

Lukas, Vanessa A; Fishbein, Kenneth W; Reiter, David A; Lin, Ping-Chang; Schneider, Erika; Spencer, Richard G

2015-07-01

To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images. Pre- and posttrypsin degradation values of T1 , T2 , T2 *, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC). The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T1 (ACC = 0.99 ± 0.01), T2 derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T2 derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm(2) : ACC = 0.80 ± 0.05; b = 666 s/mm(2) : ACC = 0.85 ± 0.04). In particular, T1 values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm(2) , as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies. Classification according to T1 values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules. © 2014 Wiley Periodicals, Inc.

MRI Predictive Factors for Tumor Response in Rectal Cancer Following Neoadjuvant Chemoradiation Therapy - Implications for Induction Chemotherapy?

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

Yu, Stanley K.T.; Tait, Diana; Chau, Ian

2013-11-01

Purpose: Clinical and magnetic resonance imaging (MRI) characteristics at baseline and following chemoradiation therapy (CRT) most strongly associated with histopathologic response were investigated and survival outcomes evaluated in accordance with imaging and pathological response. Methods and Materials: Responders were defined as mrT3c/d-4 downstaged to ypT0-2 on pathology or low at risk mrT2 downstaged to ypT1 or T0. Multivariate logistic regression of baseline and posttreatment MRI: T, N, extramural venous invasion (EMVI), circumferential resection margin, craniocaudal length <5 cm, and MRI tumor height ≤5 cm were used to identify independent predictor(s) for response. An association between induction chemotherapy and EMVI statusmore » was analyzed. Survival outcomes for pathologic and MRI responders and nonresponders were analyzed. Results: Two hundred eighty-one patients were eligible; 114 (41%) patients were pathology responders. Baseline MRI negative EMVI (odds ratio 2.94, P=.007), tumor height ≤5 cm (OR 1.96, P=.02), and mrEMVI status change (positive to negative) following CRT (OR 3.09, P<.001) were the only predictors for response. There was a strong association detected between induction chemotherapy and ymrEMVI status change after CRT (OR 9.0, P<.003). ymrT0-2 gave a positive predictive value of 80% and OR of 9.1 for ypT0-2. ymrN stage accuracy of ypN stage was 75%. Three-year disease-free survival for pathology and MRI responders were similar at 80% and 79% and significantly better than poor responders. Conclusions: Tumor height and mrEMVI status are more important than baseline size and stage of the tumor as predictors of response to CRT. Both MRI- and pathologic-defined responders have significantly improved survival. “Good response” to CRT in locally advanced rectal cancer with ypT0-2 carries significantly better 3-year overall survival and disease-free survival. Use of induction chemotherapy for improving mrEMVI status and knowledge of MRI predictive factors could be taken into account in the pursuit of individualized neoadjuvant treatments for patients with rectal cancer.« less

A floating-point digital receiver for MRI.

PubMed

Hoenninger, John C; Crooks, Lawrence E; Arakawa, Mitsuaki

2002-07-01

A magnetic resonance imaging (MRI) system requires the highest possible signal fidelity and stability for clinical applications. Quadrature analog receivers have problems with channel matching, dc offset and analog-to-digital linearity. Fixed-point digital receivers (DRs) reduce all of these problems. We have demonstrated that a floating-point DR using large (order 124 to 512) FIR low-pass filters also overcomes these problems, automatically provides long word length and has low latency between signals. A preloaded table of finite impuls response (FIR) filter coefficients provides fast switching between one of 129 different one-stage and two-stage multrate FIR low-pass filters with bandwidths between 4 KHz and 125 KHz. This design has been implemented on a dual channel circuit board for a commercial MRI system.

Logistic regression analysis of psychosocial correlates associated with recovery from schizophrenia in a Chinese community.

PubMed

Tse, Samson; Davidson, Larry; Chung, Ka-Fai; Yu, Chong Ho; Ng, King Lam; Tsoi, Emily

2015-02-01

More mental health services are adopting the recovery paradigm. This study adds to prior research by (a) using measures of stages of recovery and elements of recovery that were designed and validated in a non-Western, Chinese culture and (b) testing which demographic factors predict advanced recovery and whether placing importance on certain elements predicts advanced recovery. We examined recovery and factors associated with recovery among 75 Hong Kong adults who were diagnosed with schizophrenia and assessed to be in clinical remission. Data were collected on socio-demographic factors, recovery stages and elements associated with recovery. Logistic regression analysis was used to identify variables that could best predict stages of recovery. Receiver operating characteristic curves were used to detect the classification accuracy of the model (i.e. rates of correct classification of stages of recovery). Logistic regression results indicated that stages of recovery could be distinguished with reasonable accuracy for Stage 3 ('living with disability', classification accuracy = 75.45%) and Stage 4 ('living beyond disability', classification accuracy = 75.50%). However, there was no sufficient information to predict Combined Stages 1 and 2 ('overwhelmed by disability' and 'struggling with disability'). It was found that having a meaningful role and age were the most important differentiators of recovery stage. Preliminary findings suggest that adopting salient life roles personally is important to recovery and that this component should be incorporated into mental health services. © The Author(s) 2014.

Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method

PubMed Central

Dickie, David Alexander; Job, Dominic E.; Gonzalez, David Rodriguez; Shenkin, Susan D.; Wardlaw, Joanna M.

2015-01-01

Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease. PMID:26023913

Comparison between breast MRI and contrast-enhanced spectral mammography.

PubMed

Łuczyńska, Elżbieta; Heinze-Paluchowska, Sylwia; Hendrick, Edward; Dyczek, Sonia; Ryś, Janusz; Herman, Krzysztof; Blecharz, Paweł; Jakubowicz, Jerzy

2015-05-12

The main goal of this study was to compare contrast-enhanced spectral mammography (CESM) and breast magnetic resonance imaging (MRI) with histopathological results and to compare the sensitivity, accuracy, and positive and negative predictive values for both imaging modalities. After ethics approval, CESM and MRI examinations were performed in 102 patients who had suspicious lesions described in conventional mammography. All visible lesions were evaluated independently by 2 experienced radiologists using BI-RADS classifications (scale 1-5). Dimensions of lesions measured with each modality were compared to postoperative histopathology results. There were 102 patients entered into CESM/MRI studies and 118 lesions were identified by the combination of CESM and breast MRI. Histopathology confirmed that 81 of 118 lesions were malignant and 37 were benign. Of the 81 malignant lesions, 72 were invasive cancers and 9 were in situ cancers. Sensitivity was 100% with CESM and 93% with breast MRI. Accuracy was 79% with CESM and 73% with breast MRI. ROC curve areas based on BI-RADS were 0.83 for CESM and 0.84 for breast MRI. Lesion size estimates on CESM and breast MRI were similar, both slightly larger than those from histopathology. Our results indicate that CESM has the potential to be a valuable diagnostic method that enables accurate detection of malignant breast lesions, has high negative predictive value, and a false-positive rate similar to that of breast MRI.

The prognostic efficacy and improvements of the 7th edition Union for International Cancer Control tumor-node-metastasis classifications for Chinese patients with gastric cancer: Results based on a retrospective three-decade population study.

PubMed

Gu, Huizi; Li, Dongmei; Zhu, Haitao; Zhang, Hao; Yu, Ying; Qin, Dongxue; Yi, Mei; Li, Xiang; Lu, Ping

2017-03-01

This study aimed to evaluate survival trends for patients with gastric cancer in northeast China in the most recent three decades and analyze the applicability of the UICC tumor-node-metastasis (TNM) classification 7th edition for Chinese patients with gastric cancer. A review of all inpatient and outpatient records of patients with gastric cancer was conducted in the first hospital of China Medical University and the Liaoning Cancer Hospital and Institute. All patients who met the inclusion criteria and were seen from January 1980 through December 2009 were included in the study. The primary outcome was 5-year survival, which was analyzed according to decade of diagnosis and TNM classifications. From 1980 through 2009, the 5-year survival rates for patients with gastric cancer (n=2414) increased from 39.1% to 57.3%. Decade of diagnosis was significantly associated with patient survival (p = 0.013), and the 5-year survival rate in the 2000s was remarkably higher than that in the 1980s and 1990s (p = 0.004 and 0.049, respectively). When classified according to the UICC TNM classification of gastric cancer 7th edition, the prognoses of stage IIIA and stage IIIB patients were not significantly different (p = 0.077). However, if stage T4b and stage N0 patients were classified as stage IIIA, the prognoses of stage IIIA and stage IIIB patients were significantly different (p < 0.001). Hence, there was a significant difference in survival during the three time periods in Northeast China. Classifying stage T4b and stage N0 patients as stage IIIA according to the 7th edition of UICC gastric cancer TNM classifications better stratified Chinese patients and predicted prognoses.

Experience with International Neuroblastoma Staging System and Pathology Classification

PubMed Central

Ikeda, H; Iehara, T; Tsuchida, Y; Kaneko, M; Hata, J; Naito, H; Iwafuchi, M; Ohnuma, N; Mugishima, H; Toyoda, Y; Hamazaki, M; Mimaya, J; Kondo, S; Kawa, K; Okada, A; Hiyama, E; Suita, S; Takamatsu, H

2002-01-01

The International Neuroblastoma Staging System and Pathology Classification were proposed in 1988 and in 1999, respectively, but their clinical value has not yet been fully studied in new patients. Six hundred and forty-four patients with neuroblastoma treated between January 1995 and December 1999 were analysed by these classifications. The 4-year overall survival rate of patients <12 months of age with INSS stages 1, 2A, 2B, 3 and 4S disease was 98.5%, which was significantly higher than the 73.1% rate in stage 4 patients <12 months (P<0.0001). When patients were ⩾12 months, the 4-year overall survival rate of patients with neuroblastoma at 1, 2A, 2B and 3 stages was 100% and that of patients at stage 4 was 48.5% (P<0.0001). As to the International Neuroblastoma Pathology Classification histology, the 4-year overall survival rate was 98.8% in patients with favourable histology and 60.7% in those with unfavourable histology in the <12 months group (P<0.0001). In the ⩾12 months group, the 4-year oral survival of patients with favourable histology was 95.3% and that of patients with unfavourable histology was 50.6% (P<0.0001). Among biological factors, MYCN amplification, DNA diploidy and 1p deletions were significantly associated with poor prognosis in patients <12 months, as were MYCN amplification and DNA diploidy in patients ⩾12 months of age. Multivariate analysis showed that the INSS stage (stage 4 vs other stages) and International Neuroblastoma Pathology Classification histology (unfavourable vs favourable) were significantly and independently associated with the survival of patients undergoing treatment, stratified by age, stage and MYCN amplification (P=0.0002 and P=0.0051, respectively). British Journal of Cancer (2002) 86, 1110–1116. DOI: 10.1038/sj/bjc/6600231 www.bjcancer.com © 2002 Cancer Research UK PMID:11953858

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

PubMed

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

[Cervical vertebrae: Mandibular growth dynamism indicators?].

PubMed

Raberin, Monique; Cozor, Ilinca; Gobert-Jacquart, Stéphanie

2012-03-01

A study of mandibular growth maturation was performed on a population of 103 patients during orthodontic treatment (69 girls and 34 boys) from 11 to 16 years, having initially a Class II skeletal discrepancy. The relationship between wrist maturation indices and the cervical vertebrae maturation was studied by Lamparski classification. Significant correlations were found between Björk stages, MP3=, MP3 cap and MP3 U and respectively Lamparski stages as CVS 2, CVS 3-4 and CVS 5-6. This retrospective longitudinal study identified three mandibular variables at three different maturation stages according to Björk classification and to the six stages of Lamparski classification. The relationships between these different maturation stages and a quantitative mandibular response permit to estimate optimal time for our orthodontic therapy. The results indicate a significant increase in mandibular length between CVS 4 and CVS 5, suggesting the persistence of a condylar response to a stimulation therapy after CVS3 or CVS 4 stages (MP3 cap). Mandibular growth seems to continue after MP3 U stage or CVS 5 stage. © EDP Sciences, SFODF, 2012.

Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features

NASA Astrophysics Data System (ADS)

Ahmed, H. O. A.; Wong, M. L. D.; Nandi, A. K.

2018-01-01

Condition classification of rolling element bearings in rotating machines is important to prevent the breakdown of industrial machinery. A considerable amount of literature has been published on bearing faults classification. These studies aim to determine automatically the current status of a roller element bearing. Of these studies, methods based on compressed sensing (CS) have received some attention recently due to their ability to allow one to sample below the Nyquist sampling rate. This technology has many possible uses in machine condition monitoring and has been investigated as a possible approach for fault detection and classification in the compressed domain, i.e., without reconstructing the original signal. However, previous CS based methods have been found to be too weak for highly compressed data. The present paper explores computationally, for the first time, the effects of sparse autoencoder based over-complete sparse representations on the classification performance of highly compressed measurements of bearing vibration signals. For this study, the CS method was used to produce highly compressed measurements of the original bearing dataset. Then, an effective deep neural network (DNN) with unsupervised feature learning algorithm based on sparse autoencoder is used for learning over-complete sparse representations of these compressed datasets. Finally, the fault classification is achieved using two stages, namely, pre-training classification based on stacked autoencoder and softmax regression layer form the deep net stage (the first stage), and re-training classification based on backpropagation (BP) algorithm forms the fine-tuning stage (the second stage). The experimental results show that the proposed method is able to achieve high levels of accuracy even with extremely compressed measurements compared with the existing techniques.

68Ga-PSMA PET/CT vs. mpMRI for locoregional prostate cancer staging: correlation with final histopathology.

PubMed

Berger, I; Annabattula, C; Lewis, J; Shetty, D V; Kam, J; Maclean, F; Arianayagam, M; Canagasingham, B; Ferguson, R; Khadra, M; Ko, R; Winter, M; Loh, H; Varol, C

2018-06-01

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) can be used to locate lesions based on PSMA avidity, however guidelines on its use are limited by its infancy. We aimed to compare multiparametric magnetic resonance imaging (mpMRI) and PSMA PET/CT to prostatectomy histopathology. We conducted a chart review from February 2015 to January 2017 of 50 male patients staged for prostate cancer using PSMA PET/CT and mpMRI who then underwent radical prostatectomy. Pre-operative PSMA PET/CT and mpMRI were paired with corresponding histopathology. Correlations, sensitivity, and specificity were used for comparisons. A total of 81 lesions were confirmed by histopathology. Fifty index lesions were detected by histopathology, all of which were detected by PSMA PET/CT (100% detection), and 47 by mpMRI (94% detection). Thirty-one histologically confirmed secondary lesions were detected, 29 of which were detected by PSMA PET/CT (93.5% detection), and 16 by mpMRI (51.6% detection). PSMA had better sensitivity for index lesion localization than mpMRI (81.1 vs. 64.8%). Specificity was similar for PSMA PET/CT and mpMRI (84.6 vs. 82.7%). SUV max of index lesions ranged from 2.9 to 39.6 (M = 9.27 ± 6.41). Index lesion SUV max was positively correlated with PSA (rho = 0.48, p < 0.001) and ISUP grade (rho = 0.51, p < 0.001). PSMA-PET/CT provided superior detection of prostate cancer lesions with better sensitivity than mpMRI. PSMA-PET/CT can be used to enhance locoregional mpMRI to provide improved detection and characterization of lesions.

MRI contrast agent for targeting glioma: interleukin-13 labeled liposome encapsulating gadolinium-DTPA

PubMed Central

Liu, Xiaoli; Madhankumar, Achuthamangalam B.; Miller, Patti A.; Duck, Kari A.; Hafenstein, Susan; Rizk, Elias; Slagle-Webb, Becky; Sheehan, Jonas M.; Connor, James R.; Yang, Qing X.

2016-01-01

Background Detection of glioma with MRI contrast agent is limited to cases in which the blood-brain barrier (BBB) is compromised as contrast agents cannot cross the BBB. Thus, an early-stage infiltrating tumor is not detectable. Interleukin-13 receptor alpha 2 (IL-13Rα2), which has been shown to be overexpressed in glioma, can be used as a target moiety. We hypothesized that liposomes conjugated with IL-13 and encapsulating MRI contrast agent are capable of passing through an intact BBB and producing MRI contrast with greater sensitivity. Methods The targeted MRI contrast agent was created by encapsulating Magnevist (Gd-DTPA) into liposomes conjugated with IL-13 and characterized by particle size distribution, cytotoxicity, and MRI relaxivity. MR image intensity was evaluated in the brain in normal mice post injection of Gd-DTPA and IL-13-liposome-Gd-DTPA one day apart. The specificity for glioma detection by IL-13-liposome-Gd-DTPA was demonstrated in an intracranial glioma mouse model and validated histologically. Results The average size of IL-13-liposome-Gd-DTPA was 137 ± 43 nm with relaxivity of 4.0 ± 0.4 L/mmole-s at 7 Tesla. No significant cytotoxicity was observed with MTS assay and serum chemistry in mice. The MRI signal intensity was enhanced up to 15% post injection of IL-13-liposome-Gd-DTPA in normal brain tissue following a similar time course as that for the pituitary gland outside of the BBB. MRI enhanced by IL-13-liposome-Gd-DTPA detected small tumor masses in addition to those seen with Magnevist-enhanced MRI. Conclusions IL-13-liposome-Gd-DTPA is able to pass through the uncompromised BBB and detect an early stage glioma that cannot be seen with conventional contrast-enhanced MRI. PMID:26519740

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

PubMed

Donnelly-Kehoe, Patricio Andres; Pascariello, Guido Orlando; Gómez, Juan Carlos

2018-05-15

We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Detection of muscle gap by L-BIA in muscle injuries: clinical prognosis.

PubMed

Nescolarde, L; Yanguas, J; Terricabras, J; Lukaski, H; Alomar, X; Rosell-Ferrer, J; Rodas, G

2017-06-21

Sport-related muscle injury classifications are based basically on imaging criteria such as ultrasound (US) and magnetic resonance imaging (MRI) without consensus because of a lack of clinical prognostics for return-to-play (RTP), which is conditioned upon the severity of the injury, and this in turn with the muscle gap (muscular fibers retraction). Recently, Futbol Club Barcelona's medical department proposed a new muscle injury classification in which muscle gap plays an important role, with the drawback that it is not always possible to identify by MRI. Localized bioimpedance measurement (L-BIA) has emerged as a non-invasive technique for supporting US and MRI to quantify the disrupted soft tissue structure in injured muscles. To correlate the severity of the injury according to the gap with the RTP, through the percent of change in resistance (R), reactance (Xc) and phase-angle (PA) by L-BIA measurements in 22 muscle injuries. After grouping the data according to the muscle gap (by MRI exam), there were significant differences in R between grade 1 and grade 2f (myotendinous or myofascial muscle injury with feather-like appearance), as well as between grade 2f and grade 2g (myotendinous or myofascial muscle injury with feather and gap). The Xc and PA values decrease significantly between each grade (i.e. 1 versus 2f, 1 versus 2g and 2f versus 2g). In addition, the severity of the muscle gap adversely affected the RTP with significant differences observed between 1 and 2g as well as between 2f and 2g. These results show that L-BIA could aid MRI and US in identifying the severity of an injured muscle according to muscle gap and therefore to accurately predict the RTP.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

The Frequency and Severity of Magnetic Resonance Imaging Abnormalities in Infants with Mild Neonatal Encephalopathy.

PubMed

Walsh, Brian H; Neil, Jeffrey; Morey, JoAnn; Yang, Edward; Silvera, Michelle V; Inder, Terrie E; Ortinau, Cynthia

2017-08-01

To assess and contrast the incidence and severity of abnormalities on cerebral magnetic resonance imaging (MRI) between infants with mild, moderate, and severe neonatal encephalopathy who received therapeutic hypothermia. This retrospective cohort studied infants with mild, moderate, and severe neonatal encephalopathy who received therapeutic hypothermia at a single tertiary neonatal intensive care unit between 2013 and 2015. Two neuroradiologists masked to the clinical condition evaluated brain MRIs for cerebral injury after therapeutic hypothermia using the Barkovich classification system. Additional abnormalities not included in this classification system were also noted. The rate, pattern, and severity of abnormalities/injury were compared across the grades of neonatal encephalopathy. Eighty-nine infants received therapeutic hypothermia and met study criteria, 48 with mild neonatal encephalopathy, 35 with moderate neonatal encephalopathy, and 6 with severe neonatal encephalopathy. Forty-eight infants (54%) had an abnormality on MRI. There was no difference in the rate of overall MRI abnormalities by grade of neonatal encephalopathy (mild neonatal encephalopathy 54%, moderate neonatal encephalopathy 54%, and severe neonatal encephalopathy 50%; P= .89). Basal ganglia/thalamic injury was more common in those with severe neonatal encephalopathy (mild neonatal encephalopathy 4%, moderate neonatal encephalopathy 9%, severe neonatal encephalopathy 34%; P = .03). In contrast, watershed injury did not differ between neonatal encephalopathy grades (mild neonatal encephalopathy 36%, moderate neonatal encephalopathy 32%, severe neonatal encephalopathy 50%; P = .3). Mild neonatal encephalopathy is commonly associated with MRI abnormalities after therapeutic hypothermia. The grade of neonatal encephalopathy during the first hours of life may not discriminate adequately between infants with and without cerebral injury noted on MRI after therapeutic hypothermia. Copyright © 2017 Elsevier Inc. All rights reserved.

A Review of Challenges in the Use of fMRI for Disease Classification / Characterization and A Projection Pursuit Application from Multi-site fMRI Schizophrenia Study.

PubMed

Demirci, Oguz; Clark, Vincent P; Magnotta, Vincent A; Andreasen, Nancy C; Lauriello, John; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

2008-09-01

Functional magnetic resonance imaging (fMRI) is a fairly new technique that has the potential to characterize and classify brain disorders such as schizophrenia. It has the possibility of playing a crucial role in designing objective prognostic/diagnostic tools, but also presents numerous challenges to analysis and interpretation. Classification provides results for individual subjects, rather than results related to group differences. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions out of high dimensional data with a limited number of subjects, especially for heterogeneous disorders whose pathophysiology is unknown. Numerous research efforts have been reported in the field using fMRI activation of schizophrenia patients and healthy controls. However, the results are usually not generalizable to larger data sets and require careful definition of the techniques used both in designing algorithms and reporting prediction accuracies. In this review paper, we survey a number of previous reports and also identify possible biases (cross-validation, class size, e.g.) in class comparison/prediction problems. Some suggestions to improve the effectiveness of the presentation of the prediction accuracy results are provided. We also present our own results using a projection pursuit algorithm followed by an application of independent component analysis proposed in an earlier study. We classify schizophrenia versus healthy controls using fMRI data of 155 subjects from two sites obtained during three different tasks. The results are compared in order to investigate the effectiveness of each task and differences between patients with schizophrenia and healthy controls were investigated.

Age group classification and gender detection based on forced expiratory spirometry.

PubMed

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

[The optimization of chondromalacia patellae diagnosis by NMR tomography. The use of an apparatus for cartilage compression].

PubMed

König, H; Dinkelaker, F; Wolf, K J

1991-08-01

The aim of this study was to improve the MRI diagnosis of CMP, with special reference to the early stages and accurate staging. For this purpose, the retropatellar cartilage was examined by MRI while compression was carried out, using 21 patients and five normal controls. The compression was applied by means of a specially constructed device. Changes in cartilage thickness and signal intensity were evaluated quantitatively during FLASH and FISP sequences. In all patients the results of arthroscopies were available and in 12 patients, cartilage biopsies had been obtained. CMP stage I could be distinguished from normal cartilage by reduction in cartilage thickness and signal increase from the oedematous cartilage during compression. In CMP stages II/III, abnormal protein deposition of collagen type I could be demonstrated by its compressibility. In stages III and IV, the method does not add any significant additional information.

Musculoskeletal manifestations of systemic lupus erythmatosus.

PubMed

Mahmoud, Khaled; Zayat, Ahmed; Vital, Edward M

2017-09-01

Imaging studies suggest potential changes to the classification and assessment of inflammatory musculoskeletal lupus. This is important because of the burden of disease but the potential for new targeted therapies. Using our current classification and treatment, musculoskeletal symptoms continue to impact significantly on quality of life and work disability. Ultrasound and MRI studies suggested that new approaches to the diagnosis, classification, and evaluation of these symptoms are needed. Many patients with pain but no synovitis have ultrasound-proven joint and tendon inflammation but would not qualify for clinical trials or score highly on disease activity instruments. MRI studies show that erosions are more common than previously thought and may have a different pathogenesis than RA. Immunology studies suggest differences from other autoimmune synovitis, with a complex role for type I interferons. A wide range of biologic therapies appear more consistently effective for arthritis than some other manifestations. Changes to the selection of patients for therapy and stratification using musculoskeletal imaging may offer new approaches to clinical trials and the routine care of systemic lupus erythematosus patients with inflammatory musculoskeletal symptoms. Outcomes may thereby be improved using existing therapies. There are significant knowledge gaps that must be addressed to achieve these potential improved outcomes.

JointMMCC: Joint Maximum-Margin Classification and Clustering of Imaging Data

PubMed Central

Filipovych, Roman; Resnick, Susan M.; Davatzikos, Christos

2012-01-01

A number of conditions are characterized by pathologies that form continuous or nearly-continuous spectra spanning from the absence of pathology to very pronounced pathological changes (e.g., normal aging, Mild Cognitive Impairment, Alzheimer's). Moreover, diseases are often highly heterogeneous with a number of diagnostic subcategories or subconditions lying within the spectra (e.g., Autism Spectrum Disorder, schizophrenia). Discovering coherent subpopulations of subjects within the spectrum of pathological changes may further our understanding of diseases, and potentially identify subconditions that require alternative or modified treatment options. In this paper, we propose an approach that aims at identifying coherent subpopulations with respect to the underlying MRI in the scenario where the condition is heterogeneous and pathological changes form a continuous spectrum. We describe a Joint Maximum-Margin Classification and Clustering (JointMMCC) approach that jointly detects the pathologic population via semi-supervised classification, as well as disentangles heterogeneity of the pathological cohort by solving a clustering subproblem. We propose an efficient solution to the non-convex optimization problem associated with JointMMCC. We apply our proposed approach to an MRI study of aging, and identify coherent subpopulations (i.e., clusters) of cognitively less stable adults. PMID:22328179

Pullout failure strength of the posterior horn of the medial meniscus with root ligament tear.

PubMed

Kim, Young-Mo; Joo, Yong-Bum

2013-07-01

To evaluate the reparability of the posterior horn of the medial meniscus with root ligament tear by measuring the actual pullout failure strength of a simple vertical suture of an arthroscopic subtotal meniscectomized posterior horn of the medial meniscus. From November 2009 to May 2010, nine posterior horns of the medial meniscus specimens were collected from arthroscopic subtotal meniscectomy performed as a treatment for root ligament rupture of the posterior horn of the medial meniscus. Simple vertical sutures were performed on the specimens, and pullout failure load was tested with a biaxial servohydraulic testing machine (Model 8874; Instron Corp., Norwood, MA, USA). The degree of degeneration, extrusion, and medial displacement of the medial meniscus were evaluated with magnetic resonance imaging (MRI). The Kellgren-Lawrence classification was used in standing plain radiography, and mechanical alignment was measured using orthoroentgenography. Tear morphology was classified into ligament proper type or meniscoligamentous junctional type according to the site of the torn root ligament of the posterior horn of the medial meniscus during arthroscopy. The mean pullout failure strength of the posterior horn of the medial meniscus was 71.6 ± 23.2 N (range, 41.4-107.7 N). The degree of degeneration of the posterior horn of the medial meniscus on MRI showed statistically significant correlation with pullout failure strength and Kellgren-Lawrence classification. Pullout failure strength showed correlation with mechanical alignment and Kellgren-Lawrence classification (P < 0.05). The measurement of pullout failure strength of the posterior horn of the medial meniscus with root ligament tear showed a degree of repairability. The degree of degeneration of the posterior horn of the medial meniscus on MRI showed a significant correlation with the pullout failure strength. The pullout failure strength was also not only correlated with the degree of degeneration of the posterior horn of the medial meniscus, but also with mechanical alignment and Kellgren-Lawrence classification, which represent bony degenerative change.

Development of a brain MRI-based hidden Markov model for dementia recognition

PubMed Central

2013-01-01

Background Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition. Methods Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range. Results The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia. Conclusion The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia. PMID:24564961

Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI.

PubMed

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

2017-02-01

We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.

Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning.

PubMed

Kesler, Shelli R; Rao, Arvind; Blayney, Douglas W; Oakley-Girvan, Ingrid A; Karuturi, Meghan; Palesh, Oxana

2017-01-01

We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34-65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy ( p < 0.0001). The neuroimaging-based model was significantly more accurate than a model involving patient-related and medical variables ( p = 0.005). Hub regions belonging to several distinct functional networks were the most important predictors of cognitive outcome. Characteristics of these hubs indicated potential spread of brain injury from default mode to other networks over time. These findings suggest that resting state fMRI is a promising tool for predicting future cognitive impairment associated with breast cancer. This information could inform treatment decision making by identifying patients at highest risk for long-term cognitive impairment.

Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning

PubMed Central

Kesler, Shelli R.; Rao, Arvind; Blayney, Douglas W.; Oakley-Girvan, Ingrid A.; Karuturi, Meghan; Palesh, Oxana

2017-01-01

We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34–65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy (p < 0.0001). The neuroimaging-based model was significantly more accurate than a model involving patient-related and medical variables (p = 0.005). Hub regions belonging to several distinct functional networks were the most important predictors of cognitive outcome. Characteristics of these hubs indicated potential spread of brain injury from default mode to other networks over time. These findings suggest that resting state fMRI is a promising tool for predicting future cognitive impairment associated with breast cancer. This information could inform treatment decision making by identifying patients at highest risk for long-term cognitive impairment. PMID:29187817

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

PubMed

Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI.

PubMed

Dikaios, Nikolaos; Alkalbani, Jokha; Sidhu, Harbir Singh; Fujiwara, Taiki; Abd-Alazeez, Mohamed; Kirkham, Alex; Allen, Clare; Ahmed, Hashim; Emberton, Mark; Freeman, Alex; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit

2015-02-01

We aimed to develop logistic regression (LR) models for classifying prostate cancer within the transition zone on multi-parametric magnetic resonance imaging (mp-MRI). One hundred and fifty-five patients (training cohort, 70 patients; temporal validation cohort, 85 patients) underwent mp-MRI and transperineal-template-prostate-mapping (TPM) biopsy. Positive cores were classified by cancer definitions: (1) any-cancer; (2) definition-1 [≥Gleason 4 + 3 or ≥ 6 mm cancer core length (CCL)] [high risk significant]; and (3) definition-2 (≥Gleason 3 + 4 or ≥ 4 mm CCL) cancer [intermediate-high risk significant]. For each, logistic-regression mp-MRI models were derived from the training cohort and validated internally and with the temporal cohort. Sensitivity/specificity and the area under the receiver operating characteristic (ROC-AUC) curve were calculated. LR model performance was compared to radiologists' performance. Twenty-eight of 70 patients from the training cohort, and 25/85 patients from the temporal validation cohort had significant cancer on TPM. The ROC-AUC of the LR model for classification of cancer was 0.73/0.67 at internal/temporal validation. The radiologist A/B ROC-AUC was 0.65/0.74 (temporal cohort). For patients scored by radiologists as Prostate Imaging Reporting and Data System (Pi-RADS) score 3, sensitivity/specificity of radiologist A 'best guess' and LR model was 0.14/0.54 and 0.71/0.61, respectively; and radiologist B 'best guess' and LR model was 0.40/0.34 and 0.50/0.76, respectively. LR models can improve classification of Pi-RADS score 3 lesions similar to experienced radiologists. • MRI helps find prostate cancer in the anterior of the gland • Logistic regression models based on mp-MRI can classify prostate cancer • Computers can help confirm cancer in areas doctors are uncertain about.

Diagnosis and classification of chondral knee injuries: comparison between magnetic resonance imaging and arthroscopy.

PubMed

Danieli, Marcus Vinicius; Guerreiro, João Paulo Fernandes; Queiroz, Alexandre deOliveira; Pereira, Hamilton daRosa; Tagima, Susi; Marini, Marcelo Garcia; Cataneo, Daniele Cristina

2016-05-01

To compare the magnetic resonance imaging (MRI) findings of patients undergoing knee arthroscopy for chondral lesions. The hypothesis was that MRI displays low sensitivity in the diagnosis and classification of chondral injuries. A total of 83 knees were evaluated. The MRIs were performed using the same machine (GE SIGNA HDX 1.45 T). The MRI results were compared with the arthroscopy findings, and an agreement analysis was performed. Thirty-eight of the 83 MRI exams were evaluated by another radiologist for inter-observer agreement analysis. These analyses were performed using the kappa (κ) coefficient. The highest incidence of chondral injury was in the patella (14.4 %). The κ coefficient was 0.31 for the patellar surface; 0.38 for the trochlea; 0.46 for the medial femoral condyle; 0.51 for the lateral femoral condyle; and 0.19 for the lateral plateau. After dividing the injuries into two groups (ICRS Grades 0-II and Grades III and IV), the following κ coefficients were obtained as follows: 0.49 (patella); 0.53 (trochlea); 0.46 (medial femoral condyle); 0.43 (medial plateau); 0.67 (lateral femoral condyle); and 0.51 (lateral plateau). The MRI sensitivity was 76.4 % (patella), 88.2 % (trochlea), 69.7 % (medial femoral condyle), 85.7 % (medial plateau), 81.8 % (lateral femoral condyle) and 75 % (lateral plateau). Comparing the radiologists' evaluations, the following κ coefficients were obtained as follows: 0.73 (patella); 0.63 (trochlea); 0.84 (medial femoral condyle); 0.72 (medial plateau); 0.77 (lateral femoral condyle); and 0.91 (lateral plateau). Compared with arthroscopy, MRI displays moderate sensitivity for detecting and classifying chondral knee injuries. It is an important image method, but we must be careful in the assessment of patients with suspected chondral lesions. III.

Multimodal MRI-based imputation of the Aβ+ in early mild cognitive impairment

PubMed Central

Tosun, Duygu; Joshi, Sarang; Weiner, Michael W; for the Alzheimer's Disease Neuroimaging Initiative

2014-01-01

Objective The primary goal of this study was to identify brain atrophy from structural MRI (magnetic resonance imaging) and cerebral blood flow (CBF) patterns from arterial spin labeling perfusion MRI that are best predictors of the Aβ-burden, measured as composite 18F-AV45-PET (positron emission tomography) uptake, in individuals with early mild cognitive impairment (MCI). Furthermore, another objective was to assess the relative importance of imaging modalities in classification of Aβ+/Aβ− early MCI. Methods Sixty-seven Alzheimer's Disease Neuroimaging Initiative (ADNI)-GO/2 participants with early MCI were included. Voxel-wise anatomical shape variation measures were computed by estimating the initial diffeomorphic mapping momenta from an unbiased control template. CBF measures normalized to average motor cortex CBF were mapped onto the template space. Using partial least squares regression, we identified the structural and CBF signatures of Aβ after accounting for normal cofounding effects of age, gender, and education. Results 18F-AV45-positive early MCIs could be identified with 83% classification accuracy, 87% positive predictive value, and 84% negative predictive value by multidisciplinary classifiers combining demographics data, ApoE ε4-genotype, and a multimodal MRI-based Aβ score. Interpretation Multimodal MRI can be used to predict the amyloid status of early-MCI individuals. MRI is a very attractive candidate for the identification of inexpensive and noninvasive surrogate biomarkers of Aβ deposition. Our approach is expected to have value for the identification of individuals likely to be Aβ+ in circumstances where cost or logistical problems prevent Aβ detection using cerebrospinal fluid analysis or Aβ-PET. This can also be used in clinical settings and clinical trials, aiding subject recruitment and evaluation of treatment efficacy. Imputation of the Aβ-positivity status could also complement Aβ-PET by identifying individuals who would benefit the most from this assessment. PMID:24729983

Value of Endorectal MRI in Romanian Men for High Risk of Prostate Cancer: MRI Findings Compared with Saturation Biopsy.

PubMed

Lebovici, A; Sfrangeu, S A; Caraiani, C; Lucan, C; Suciu, M; Elec, F; Iacob, Gh; Buruian, M

2015-01-01

To evaluate the potentials of T2 weighted (T2W)MRI and diffusion weighted (DW) MRI for prostate cancer(PCa) detection, local staging and treatment planning in high-risk group. Endorectal MRI was performed in 17 Romanian men (median age: 66 years; range: 58 75 years), prostate specific antigen (PSA) serum levels (median: 20 ng mL; range: 8.6 100 ng mL) with positive findings for PCa(median Gleason score: 8; range: 7 - 9). Imaging findings were compared to standarised 20-core transperineal saturation biopsy. The prostate was divided into 16 standart sectors(10 posterior and 6 anterior). Overall, prostate cancer was detected in 16 patients(94%) on DW-MRI alone and in all 17 patients (100%) on T2W-MRI alone, and on combined imaging. On T2W-MRI165 sectors out of 272 were suspicious for PCa and 124 (75%)were cancer positive. On DW-MRI 126 sectors out of 272 were suspicious for PCa and 118 (95%) were cancer positive. On the combined imaging approach 134 sectors out of 272 were suspicious for PCa and 126 (94%) were cancer positive. This resulted in diagnostic accuracies per sector of 76% for T2WMRI, 86% for DW-MRI and 89% for combined imaging. Multifocal PCa was confirmed both on MR imaging and by biopsy in 8 of the 17 men (47%) Extra capsular extension(ECE) or seminal vesicles invasion (SVI) was highly suspected in 8 (47%) respectively 7 (41%) of the 17 patients. 6 patients(35%) presented both ECE and SVI. MRI findings were taken into account for treatment planning and none of these patients underwent radical prostatectomy and instead was treated with palliative cryotherapy, radiotherapy and hormone therapy. Endorectal MRI is highly accurate in PCa detection in the high-risk group and seems to have an important role in local staging and treatment planning for Romanian population. Celsius.

TH-A-BRF-04: Intra-Fraction Motion Characterization for Early Stage Rectal Cancer Using Cine-MRI

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

Kleijnen, J; Asselen, B; Burbach, M

2014-06-15

Purpose: To investigate the intra-fraction motion in patients with early stage rectal cancer using cine-MRI. Methods: Sixteen patient diagnosed with early stage rectal cancer underwent 1.5 T MR imaging prior to each treatment fraction of their short course radiotherapy (n=76). During each scan session, three 2D sagittal cine-MRIs were performed: at the beginning (Start), after 9:30 minutes (Mid), and after 18 minutes (End). Each cine-MRI has a duration of one minute at 2Hz temporal resolution, resulting in a total of 3:48 hours of cine-MRI. Additionally, standard T2-weighted (T2w) imaging was performed. Clinical target volume (CTV) an tumor (GTV) were delineatedmore » on the T2w scan and transferred to the first time-point of each cine-MRI scan. Within each cine-MRI, the first frame was registered to the remaining frames of the scan, using a non-rigid B-spline registration. To investigate potential drifts, a similar registration was performed between the first frame of the Start and End scans.To evaluate the motion, the distances by which the edge pixels of the delineations move in anterior-posterior (AP) and cranial-caudal (CC) direction, were determined using the deformation field of the registrations. The distance which incorporated 95% of these edge pixels (dist95%) was determined within each cine-MRI, and between Start- End scans, respectively. Results: Within a cine-MRI, we observed an average dist95% for the CTV of 1.3mm/1.5mm (SD=0.7mm/0.6mm) and for the GTV of 1.2mm/1.5mm (SD=0.8mm/0.9mm), in respectively AP/CC. For the CTV motion between the Start and End scan, an average dist95% of 5.5mm/5.3mm (SD=3.1mm/2.5mm) was found, in respectively AP/CC. For the GTV motion, an average dist95% of 3.6mm/3.9mm (SD=2.2mm/2.5mm) was found in AP/CC, respectively. Conclusion: Although intra-fraction motion within a one minute cine-MRI is limited, substantial intra-fraction motion was observed within the 18 minute time period between the Start and End cine-MRI.« less

[MRI semiotics features of experimental acute intracerebral hematomas].

PubMed

Burenchev, D V; Skvortsova, V I; Tvorogova, T V; Guseva, O I; Gubskiĭ, L V; Kupriianov, D A; Pirogov, Iu A

2009-01-01

The aim of this study was to assess the possibility of revealing intracerebral hematomas (ICH), using MRI, within the first hours after onset and to determine their MRI semiotics features. Thirty animals with experimental ICH were studied. A method of two-stage introduction of autologous blood was used to develop ICH as human spontaneous intracranial hematomas. Within 3-5h after blood introduction to the rat brain. The control MRI was performed in the 3rd and 7th days after blood injections. ICH were definitely identified in the first MRI scans. The MRI semiotics features of acute ICH and their transformations were assessed. The high sensitivity of MRI to ICH as well as the uniform manifestations in all animals were shown. In conclusion, the method has high specificity for acute ICH detection.

Resting State Functional Connectivity in Mild Traumatic Brain Injury at the Acute Stage: Independent Component and Seed-Based Analyses

PubMed Central

Iraji, Armin; Benson, Randall R.; Welch, Robert D.; O'Neil, Brian J.; Woodard, John L.; Imran Ayaz, Syed; Kulek, Andrew; Mika, Valerie; Medado, Patrick; Soltanian-Zadeh, Hamid; Liu, Tianming; Haacke, E. Mark

2015-01-01

Abstract Mild traumatic brain injury (mTBI) accounts for more than 1 million emergency visits each year. Most of the injured stay in the emergency department for a few hours and are discharged home without a specific follow-up plan because of their negative clinical structural imaging. Advanced magnetic resonance imaging (MRI), particularly functional MRI (fMRI), has been reported as being sensitive to functional disturbances after brain injury. In this study, a cohort of 12 patients with mTBI were prospectively recruited from the emergency department of our local Level-1 trauma center for an advanced MRI scan at the acute stage. Sixteen age- and sex-matched controls were also recruited for comparison. Both group-based and individual-based independent component analysis of resting-state fMRI (rsfMRI) demonstrated reduced functional connectivity in both posterior cingulate cortex (PCC) and precuneus regions in comparison with controls, which is part of the default mode network (DMN). Further seed-based analysis confirmed reduced functional connectivity in these two regions and also demonstrated increased connectivity between these regions and other regions of the brain in mTBI. Seed-based analysis using the thalamus, hippocampus, and amygdala regions further demonstrated increased functional connectivity between these regions and other regions of the brain, particularly in the frontal lobe, in mTBI. Our data demonstrate alterations of multiple brain networks at the resting state, particularly increased functional connectivity in the frontal lobe, in response to brain concussion at the acute stage. Resting-state functional connectivity of the DMN could serve as a potential biomarker for improved detection of mTBI in the acute setting. PMID:25285363

Value of diffusion-weighted MRI and apparent diffusion coefficient measurements for predicting the response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy.

PubMed

Iannicelli, Elsa; Di Pietropaolo, Marco; Pilozzi, Emanuela; Osti, Mattia Falchetto; Valentino, Maria; Masoni, Luigi; Ferri, Mario

2016-10-01

The aim of our study was to assess the performance value of magnetic resonance imaging (MRI) in the restaging of locally advanced rectal cancer after neoadjuvant chemoradiotherapy (CRT) and in the identification of good vs. poor responders to neoadjuvant therapy. A total of 34 patients with locally advanced rectal cancer underwent MRI prior to and after CRT. T stage and tumor regression grade (TRG) on post-CRT MRI was compared with the pathological staging ypT and TRG. Tumor volume and the apparent diffusion coefficient (ADC) were measured using diffusion-weighted imaging (DWI) before and after neoadjuvant CRT; the percentage of tumor volume reduction and the change of ADC (ΔADC) was also calculated. ADC parameters and the percentage of tumor volume reduction were correlated to histopathological results. The diagnostic performance of ADC and volume reduction to assess tumor response was evaluated by calculating the area under the ROC curve and the optimal cut-off values. A significant correlation between the T stage and the TRG defined in DW-MRI after CRT and the ypT and the TRG observed on the surgical specimens was found (p = 0.001; p < 0.001). The mean post-CRT ADC and ΔADC in responder patients was significantly higher compared to non-responder ones (p = 0.001; p = 0.01). Furthermore, the mean post-CRT ADC values were significantly higher in tumors with T-downstage (p = 0.01). DW-MRI may have a significant role in the restaging and in the evaluation of post-CRT response of locally advanced rectal cancer. Quantitative analysis of DWI through ADC map may result in a promising noninvasive tool to evaluate the response to therapy.

Myocardial perfusion quantification using simultaneously acquired 13 NH3 -ammonia PET and dynamic contrast-enhanced MRI in patients at rest and stress.

PubMed

Kunze, Karl P; Nekolla, Stephan G; Rischpler, Christoph; Zhang, Shelley HuaLei; Hayes, Carmel; Langwieser, Nicolas; Ibrahim, Tareq; Laugwitz, Karl-Ludwig; Schwaiger, Markus

2018-04-19

Systematic differences with respect to myocardial perfusion quantification exist between DCE-MRI and PET. Using the potential of integrated PET/MRI, this study was conceived to compare perfusion quantification on the basis of simultaneously acquired 13 NH 3 -ammonia PET and DCE-MRI data in patients at rest and stress. Twenty-nine patients were examined on a 3T PET/MRI scanner. DCE-MRI was implemented in dual-sequence design and additional T 1 mapping for signal normalization. Four different deconvolution methods including a modified version of the Fermi technique were compared against 13 NH 3 -ammonia results. Cohort-average flow comparison yielded higher resting flows for DCE-MRI than for PET and, therefore, significantly lower DCE-MRI perfusion ratios under the common assumption of equal arterial and tissue hematocrit. Absolute flow values were strongly correlated in both slice-average (R 2  = 0.82) and regional (R 2  = 0.7) evaluations. Different DCE-MRI deconvolution methods yielded similar flow result with exception of an unconstrained Fermi method exhibiting outliers at high flows when compared with PET. Thresholds for Ischemia classification may not be directly tradable between PET and MRI flow values. Differences in perfusion ratios between PET and DCE-MRI may be lifted by using stress/rest-specific hematocrit conversion. Proper physiological constraints are advised in model-constrained deconvolution. © 2018 International Society for Magnetic Resonance in Medicine.

Can magnetic resonance imaging obviate the need for biopsy for microcalcifications?

PubMed Central

Chishima, Takashi

2017-01-01

Background Although microcalcifications detected with mammography (MG) are usually biopsied, biopsies cannot be performed in all cases. We sought to determine if magnetic resonance imaging (MRI) findings could indicate whether stereotactic vacuum-assisted biopsy (SVAB) is necessary. Methods Patients with mammographically detected Breast Imaging-Reporting and Data System (BI-RADS) 3, 4, and 5 microcalcifications were analyzed from April 2012 to September 2014. All patients underwent MRI. All patients with enhancing lesions in the region of the microcalcifications underwent SVAB. Non-enhancing lesions were followed or biopsied, depending on the patient's preferences. MRI findings were classified as either malignant-suspicious or benign-suspicious (“none” or “nonspecific”), and we evaluated the positive predictive value (PPV) and negative predictive value (NPV) of these classifications for predicting malignancy. Results A total of 87 patients underwent both MRI and SVAB. The NPV of MRI was 100% in the group with no enhancement. In BI-RADS category 3, there were 57 benign-suspicious lesions on MRI, of which eight were malignant (NPV of MRI: 85.0%). Conclusions It may be possible to omit SVAB for microcalcifications if there is no enhancement on MRI; however, any kind of enhancement indicates the need for biopsy in cases of BI-RADS 3 calcifications on MG. PMID:28861368

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.

Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease.

PubMed

Knight, Michael J; McCann, Bryony; Kauppinen, Risto A; Coulthard, Elizabeth J

2016-01-01

Recent pharmaceutical trials have demonstrated that slowing or reversing pathology in Alzheimer's disease is likely to be possible only in the earliest stages of disease, perhaps even before significant symptoms develop. Pathology in Alzheimer's disease accumulates for well over a decade before symptoms are detected giving a large potential window of opportunity for intervention. It is therefore important that imaging techniques detect subtle changes in brain tissue before significant macroscopic brain atrophy. Current diagnostic techniques often do not permit early diagnosis or are too expensive for routine clinical use. Magnetic Resonance Imaging (MRI) is the most versatile, affordable, and powerful imaging modality currently available, being able to deliver detailed analyses of anatomy, tissue volumes, and tissue state. In this mini-review, we consider how MRI might detect patients at risk of future dementia in the early stages of pathological change when symptoms are mild. We consider the contributions made by the various modalities of MRI (structural, diffusion, perfusion, relaxometry) in identifying not just atrophy (a late-stage AD symptom) but more subtle changes reflective of early dementia pathology. The sensitivity of MRI not just to gross anatomy but to the underlying "health" at the cellular (and even molecular) scales, makes it very well suited to this task.

Correlation between liver histology and novel magnetic resonance imaging in adult patients with non-alcoholic fatty liver disease - MRI accurately quantifies hepatic steatosis in NAFLD.

PubMed

Permutt, Z; Le, T-A; Peterson, M R; Seki, E; Brenner, D A; Sirlin, C; Loomba, R

2012-07-01

Conventional magnetic resonance imaging (MRI) techniques that measure hepatic steatosis are limited by T1 bias, T(2)* decay and multi-frequency signal-interference effects of protons in fat. Newer MR techniques such as the proton density-fat fraction (PDFF) that correct for these factors have not been specifically compared to liver biopsy in adult patients with non-alcoholic fatty liver disease (NAFLD). To examine the association between MRI-determined PDFF and histology-determined steatosis grade, and their association with fibrosis. A total of 51 adult patients with biopsy-confirmed NAFLD underwent metabolic-biochemical profiling, MRI-determined PDFF measurement of hepatic steatosis and liver biopsy assessment according to NASH-CRN histological scoring system. The average MRI-determined PDFF increased significantly with increasing histology-determined steatosis grade: 8.9% at grade-1, 16.3% at grade-2, and 25.0% at grade-3 with P ≤ 0.0001 (correlation: r(2) = 0.56, P < 0.0001). Patients with stage-4 fibrosis, when compared with patients with stage 0-3 fibrosis, had significantly lower hepatic steatosis by both MRI-determined PDFF (7.6% vs. 17.8%, P < 0.005) and histology-determined steatosis grade (1.4 vs. 2.2, P < 0.05). NAFLD patients with grade 1 steatosis were more likely to have characteristics of advanced liver disease including higher average AST:ALT (0.87 vs. 0.60, P < 0.02), GGT (140 vs. 67, P < 0.01), and INR (1.06 vs. 0.99, P < 0.01), higher stage of fibrosis and hepatocellular ballooning. MRI-determined proton density-fat fraction correlates with histology-determined steatosis grade in adults with NAFLD. Steatosis is non-linearly related to fibrosis progression. In patients with NAFLD, a low amount of hepatic steatosis on imaging does not necessarily indicate mild disease. © 2012 Blackwell Publishing Ltd.

Magnetic resonance imaging and liver histology as biomarkers of hepatic steatosis in children with nonalcoholic fatty liver disease.

PubMed

Schwimmer, Jeffrey B; Middleton, Michael S; Behling, Cynthia; Newton, Kimberly P; Awai, Hannah I; Paiz, Melissa N; Lam, Jessica; Hooker, Jonathan C; Hamilton, Gavin; Fontanesi, John; Sirlin, Claude B

2015-06-01

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children. In order to advance the field of NAFLD, noninvasive imaging methods for measuring liver fat are needed. Advanced magnetic resonance imaging (MRI) has shown great promise for the quantitative assessment of hepatic steatosis but has not been validated in children. Therefore, this study was designed to evaluate the correlation and diagnostic accuracy of MRI-estimated liver proton density fat fraction (PDFF), a biomarker for hepatic steatosis, compared to histologic steatosis grade in children. The study included 174 children with a mean age of 14.0 years. Liver PDFF estimated by MRI was significantly (P < 0.01) correlated (0.725) with steatosis grade. The correlation of MRI-estimated liver PDFF and steatosis grade was influenced by both sex and fibrosis stage. The correlation was significantly (P < 0.01) stronger in girls (0.86) than in boys (0.70). The correlation was significantly (P < 0.01) weaker in children with stage 2-4 fibrosis (0.61) than children with no fibrosis (0.76) or stage 1 fibrosis (0.78). The diagnostic accuracy of commonly used threshold values to distinguish between no steatosis and mild steatosis ranged from 0.69 to 0.82. The overall accuracy of predicting the histologic steatosis grade from MRI-estimated liver PDFF was 56%. No single threshold had sufficient sensitivity and specificity to be considered diagnostic for an individual child. Advanced magnitude-based MRI can be used to estimate liver PDFF in children, and those PDFF values correlate well with steatosis grade by liver histology. Thus, magnitude-based MRI has the potential for clinical utility in the evaluation of NAFLD, but at this time no single threshold value has sufficient accuracy to be considered diagnostic for an individual child. © 2015 by the American Association for the Study of Liver Diseases.

Magnetic Resonance Imaging and Liver Histology as Biomarkers of Hepatic Steatosis in Children with Nonalcoholic Fatty Liver Disease

PubMed Central

Schwimmer, Jeffrey B.; Middleton, Michael S.; Behling, Cynthia; Newton, Kimberly P.; Awai, Hannah I.; Paiz, Melissa N.; Lam, Jessica; Hooker, Jonathan C.; Hamilton, Gavin; Fontanesi, John; Sirlin, Claude B.

2015-01-01

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children. In order to advance the field of NAFLD, noninvasive imaging methods for measuring liver fat are needed. Advanced magnetic resonance imaging (MRI) has shown great promise for the quantitative assessment of hepatic steatosis but has not been validated in children. Therefore, this study was designed to evaluate the correlation and diagnostic accuracy of MRI-estimated liver proton density fat fraction (PDFF), a biomarker for hepatic steatosis, compared to histologic steatosis grade in children. The study included 174 children with a mean age of 14.0 years. MRI-estimated liver PDFF was significantly (p < 0.01) correlated (0.725) with steatosis grade. Correlation of MRI-estimated liver PDFF and steatosis grade was influenced by both sex and fibrosis stage. The correlation was significantly (p<0.01) stronger in girls (0.86) than in boys (0.70). The correlation was significantly (p<0.01) weaker in children with stage 2–4 fibrosis (0.61) than children with no fibrosis (0.76) or stage 1 fibrosis (0.78). The diagnostic accuracy of commonly used threshold values to distinguish between no steatosis and mild steatosis ranged from 0.69 to 0.82. The overall accuracy of predicting the histologic steatosis grade from MRI-estimated liver PDFF was 56%. No single threshold had sufficient sensitivity and specificity to be considered diagnostic for an individual child. Conclusions Advanced magnitude-based MRI can be used to estimate liver PDFF in children, and those PDFF values correlate well with steatosis grade by liver histology. Thus magnitude-based MRI has the potential for clinical utility in the evaluation of NAFLD, but at this time no single threshold value has sufficient accuracy to be considered diagnostic for an individual child. PMID:25529941

Risk of breast cancer after a diagnosis of ovarian cancer in BRCA mutation carriers: Is preventive mastectomy warranted?

PubMed

McGee, Jacob; Giannakeas, Vasily; Karlan, Beth; Lubinski, Jan; Gronwald, Jacek; Rosen, Barry; McLaughlin, John; Risch, Harvey; Sun, Ping; Foulkes, William D; Neuhausen, Susan L; Kotsopoulos, Joanne; Narod, Steven A

2017-05-01

Preventive breast surgery and MRI screening are offered to unaffected BRCA mutation carriers. The clinical benefit of these two modalities has not been evaluated among mutation carriers with a history of ovarian cancer. Thus, we sought to determine whether or not BRCA mutation carriers with ovarian cancer would benefit from preventive mastectomy or from MRI screening. First, the annual mortality rate for ovarian cancer patients was estimated for a cohort of 178 BRCA mutation carriers from Ontario, Canada. Next, the actuarial risk of developing breast cancer was estimated using an international registry of 509 BRCA mutation carriers with ovarian cancer. A series of simulations was conducted to evaluate the reduction in the probability of death (from all causes) associated with mastectomy and with MRI-based breast surveillance. Cox proportional hazards models were used to evaluate the impacts of mastectomy and MRI screening on breast cancer incidence as well as on all-cause mortality. Twenty (3.9%) of the 509 patients developed breast cancer within ten years following ovarian cancer diagnosis. The actuarial risk of developing breast cancer at ten years post-diagnosis, conditional on survival from ovarian cancer and other causes of mortality was 7.8%. Based on our simulation results, among all BRCA mutation-carrying patients diagnosed with stage III/IV ovarian cancer at age 50, the chance of dying before age 80 was reduced by less than 1% with MRI and by less than 2% with mastectomy. Greater improvements in survival with MRI or mastectomy were observed for women who had already survived 10years after ovarian cancer, and for women with stage I or II ovarian cancer. Among BRCA mutation-carrying ovarian cancer patients without a personal history of breast cancer, neither preventive mastectomy nor MRI screening is warranted, except for those who have survived ovarian cancer without recurrence for ten years and for those with early stage ovarian cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI.

PubMed

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

2017-04-01

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved.

Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

NASA Astrophysics Data System (ADS)

Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.

2017-04-01

Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (<35 Hz) frequency ranges. MBRLAF is computationally more demanding than RLAF, but highly effective in all EEG frequency ranges. Effectivity is determined by visual inspection, as well as root-mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior effectivity in terms of artifact reduction We demonstrate that physiological EEG components are preserved

[Unicornuate uterus with cavitary non-communicating rudimentary horn: magnetic resonance characterization].

PubMed

Chamorro-Oscullo, Jenny Del Rocío; Sánchez-Cortázar, Julián Antonio; Gómez-Pérez, María de Guadalupe

2018-01-01

Mullerian duct or paramesonephric anomalies are a group of congenital malformations of the female genital tract that result from the alteration in one or more stages of embryonic development. The prevalence has increased, probably due to the progress of diagnostic imaging methods and the relevance that it has acquired in young women with infertility problems. Magnetic resonance imaging (MRI) is currently the method of choice for characterizing the different types of Mullerian anomalies, its complications and associated pathology. Unicornuate uterus correspond to class II of classification of Mullerian duct anomalies developed by the American Society for Reproductive Medicine. According to this, four subtypes have been identified, each with different clinical implications. A cavitated, non-communicating rudimentary horn in a unicornuate uterus is associated with an increased incidence of gynecological problems and obstetric complications that sometimes threaten the lives of patients, reason why its suspicion, diagnosis and treatment is necessary. We report the case of a patient with infertility in which this subtype of congenital malformation was discovered.

Optimizing D'Amico risk groups in radical prostatectomy through the addition of magnetic resonance imaging data.

PubMed

Algarra, R; Zudaire, B; Tienza, A; Velis, J M; Rincón, A; Pascual, I; Zudaire, J

2014-11-01

To improve the predictive efficacy of the D'Amico risk classification system with magnetic resonance imaging (MRI) of the pelvis. We studied 729 patients from a series of 1310 radical prostatectomies for T1-T2 prostate cancer who underwent staging pelvic MRI. Each patient was classified with T2, T3a or T3b MRI, and N (+) patients were excluded. We identified the therapeutic factors that affected the biochemical progression-free survival (BPFS) time (prostate specific antigen [PSA] levels>0.4ng/mL) using a univariate and multivariate study with Cox models. We attempted to improve the predictive power of the D'Amico model (low risk: T1; Gleason 2-6; PSA levels<10ng/mL; intermediate risk: T2 or Gleason 7 or PSA levels 10-20ng/mL; high risk: T3 or Gleason 8-10 or PSA levels>20ng/mL). In the univariate study, the clinical factors that influenced BPFS were the following: Gleason 7 (HR: 1.7); Gleason 8-10 (HR: 2.9); T2 (HR: 1.6); PSA levels 10-20 (HR: 2); PSA levels>20 (HR: 4.3); D'Amico intermediate (HR: 2.1) and high (HR: 4.8) risk; T3a MRI (HR: 2.3) and T3b MRI (HR: 4.5). In the multivariate study, the only variables that affected BPFS were the following: D'Amico intermediate risk (HR: 2; 95% CI 1.2-3.3); D'Amico high risk (HR: 4.1; 95% CI 2.4-6.8); T3a MRI (HR: 1.9; 95% CI 1.2-2.9) and T3b MRI (HR: 3.9; 95% CI 2.5-6.1). Predictive model: Using the multivariate Cox models, we assessed the weight of each variable. A value of 1 was given to D'Amico low risk and T2 MRI; a value of 2 was given to D'Amico intermediate risk and T3a MRI and a value 3 was given to D'Amico high risk and T3b MRI. Each patient had a marker that varied between 2 and 6. The best model included 3 groups, as follows: 494 (67.7%) patients in group 1, with a score of 2-3 points (HR, 1), a BPFS of 86%±2% and 79%±2% at 5 and 10 years, respectively; 179 (24.6%) patients in group 2, with a score of 4 points (HR, 3), a BPFS of 60%±4% and 54%±5% at 5 and 10 years, respectively; and 56 (7.7%) patients in group 3, with a score of 5-6 points (HR, 9.3), a BPFS of 29%±8% and 19%±7% at 5 and 10 years, respectively. The median BPFS time was 1.5 years. MRI data significantly improves the predictive capacity of BPFS when using the D'Amico model data. Copyright © 2013 AEU. Published by Elsevier Espana. All rights reserved.

The prognostic value of natural killer cell infiltration in resected pulmonary adenocarcinoma.

PubMed

Takanami, I; Takeuchi, K; Giga, M

2001-06-01

Natural cytotoxicity caused by mediated natural killer cells is believed to play an important role in host-cancer defense mechanisms. Immunohistochemically, we have detected natural killer cells in tissue specimens from patients with pulmonary adenocarcinoma and have assessed their clinical characteristics. Using the monoclonal antibody for CD57 specific marker for natural killer cells, we quantified natural killer cell infiltration in 150 patients with pulmonary adenocarcinoma who underwent curative tumor resection to investigate the relationship between natural killer cell counts and clinicopathologic factors and prognosis. The natural killer cell count was significantly related to the regulation of tumor progression, involving T classification, N classification, and stage (P =.01 for T classification or stage; P =.02 for N classification). A significant difference in the rate of patient survival was detected between those patients whose tumors had either high or low natural killer cell counts in both the overall and stage I groups (P =.0002 for the overall group; P =.049 for the stage I group). These data indicate that natural killer infiltration may contribute to the regulation of tumor progression and that the natural killer cell count can serve as a useful prognostic marker in overall and stage I pulmonary adenocarcinoma.

Classification, imaging, biopsy and staging of osteosarcoma

PubMed Central

Kundu, Zile Singh

2014-01-01

Osteosarcoma is the most common primary osseous malignancy excluding malignant neoplasms of marrow origin (myeloma, lymphoma and leukemia) and accounts for approximately 20% of bone cancers. It predominantly affects patients younger than 20 years and mainly occurs in the long bones of the extremities, the most common being the metaphyseal area around the knee. These are classified as primary (central or surface) and secondary osteosarcomas arising in preexisting conditions. The conventional plain radiograph is the best for probable diagnosis as it describes features like sun burst appearance, Codman's triangle, new bone formation in soft tissues along with permeative pattern of destruction of the bone and other characteristics for specific subtypes of osteosarcomas. X-ray chest can detect metastasis in the lungs, but computerized tomography (CT) scan of the thorax is more helpful. Magnetic resonance imaging (MRI) of the lesion delineates its extent into the soft tissues, the medullary canal, the joint, skip lesions and the proximity of the tumor to the neurovascular structures. Tc99 bone scan detects the osseous metastases. Positron Emission Tomography (PET) is used for metastatic workup and/or local recurrence after resection. The role of biochemical markers like alkaline phosphatase and lactate dehydrogenase is pertinent for prognosis and treatment response. The biopsy confirms the diagnosis and reveals the grade of the tumor. Enneking system for staging malignant musculoskeletal tumors and American Joint Committee on Cancer (AJCC) staging systems are most commonly used for extremity sarcomas. PMID:24932027

The degeneration and destruction of femoral articular cartilage shows a greater degree of deterioration than that of the tibial and patellar articular cartilage in early stage knee osteoarthritis: a cross-sectional study.

PubMed

Hada, S; Kaneko, H; Sadatsuki, R; Liu, L; Futami, I; Kinoshita, M; Yusup, A; Saita, Y; Takazawa, Y; Ikeda, H; Kaneko, K; Ishijima, M

2014-10-01

The aim of the present study was to examine whether the degenerative and morphological changes of articular cartilage in early stage knee osteoarthritis (OA) occurred equally for both femoral- and tibial- or patellar- articular cartilage using magnetic resonance imaging (MRI)-based analyses. This cross-sectional study was approved by the ethics committee of our university. Fifty patients with early stage painful knee OA were enrolled. The patients underwent 3.0 T MRI on the affected knee joint. Healthy volunteers who did not show MRI-based OA changes were also recruited as controls (n = 19). The degenerative changes of the articular cartilage were quantified by a T2 mapping analysis, and any structural changes were conducted using Whole Organ Magnetic Resonance Imaging Score (WORMS) technique. All patients showed MRI-detected OA morphological changes. The T2 values of femoral condyle (FC) (P < 0.0001) and groove (P = 0.0001) in patients with early stage knee OA were significantly increased in comparison to those in the control, while no significant differences in the T2 values of patellar and tibial plateau (TP) were observed between the patients and the control. The WORMS cartilage and osteophyte scores of the femoral articular cartilage were significantly higher than those in the patellar- (P = 0.001 and P = 0.007, respectively) and tibial- (P = 0.0001 and P < 0.0001, respectively) articular cartilage in the patients with early stage knee OA. The degradation and destruction of the femoral articular cartilage demonstrated a greater degree of deterioration than those of the tibial- and patellar- articular cartilage in patients with early stage knee OA. Copyright © 2014 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Biliary tract enhancement in gadoxetic acid-enhanced MRI correlates with liver function biomarkers.

PubMed

Noda, Yoshifumi; Goshima, Satoshi; Kajita, Kimihiro; Kawada, Hiroshi; Kawai, Nobuyuki; Koyasu, Hiromi; Matsuo, Masayuki; Bae, Kyongtae T

2016-11-01

To evaluate the association between gadoxetic-acid-enhanced magnetic resonance (MR) imaging measurements and laboratory and clinical biomarkers of liver function and fibrosis. One hundred thirty nine consecutive patients with suspected liver disease or liver tumor underwent gadoxetic-acid-enhanced MR imaging. MR imaging measurements during the hepatobiliary phase included biliary tract structure-to-muscle signal intensity ratio (SIR). These measurements were compared with Child-Pugh classification, end-stage liver disease (MELD) score, and aspartate aminotransferase-to-platelet ratio index (APRI). The SIRs of cystic duct and common bile duct were significantly correlated with Child-Pugh classification (P=0.012 for cystic duct and P<0.0001 for common bile duct), MELD score (P=0.0016 and P=0.0033), and APRI (P=0.0022 and P=0.0015). The sensitivity, specificity, and area under the receiver-operating-characteristic curve were: (74%, 88%, 0.86) with the SIR of common bile duct for the detection of patients with Child-Pugh class B or C; (100%, 87%, 0.94) with the SIR of cystic duct for MELD score (>10); (65%, 76%, 0.70) with the SIR of common bile duct for APRI (>1.5). Gadoxetic-acid contrast enhancement of cystic duct and common bile duct could be used as biomarkers to assess liver function. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Effectiveness of Breast MRI and (18)F-FDG PET/CT for the Preoperative Staging of Invasive Lobular Carcinoma versus Ductal Carcinoma.

PubMed

Jung, Na Young; Kim, Sung Hoon; Kim, Sung Hun; Seo, Ye Young; Oh, Jin Kyoung; Choi, Hyun Su; You, Won Jong

2015-03-01

We evaluated the utility of magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) for the preoperative staging of invasive lobular carcinoma (ILC) of the breast and compared the results with those of invasive ductal carcinoma (IDC). The study included pathologically proven 32 ILCs and 73 IDCs. We compared clinical and histopathological characteristics and the diagnostic performances of MRI and (18)F-FDG PET/CT for the primary mass, additional ipsilateral and/or contralateral lesion(s), and axillary lymph node metastasis between the ILC and IDC groups. Primary ILCs were greater in size, but demonstrated lower maximum standardized uptake values than IDCs. All primary masses were detected on MRI. The detection rate for ILCs (75.0%) was lower than that for IDCs (83.6%) on (18)F-FDG PET/CT, but the difference was not significant. For additional ipsilateral lesion(s), the sensitivities and specificities of MRI were 87.5% and 58.3% for ILC and 100.0% and 66.7% for IDC, respectively; whereas the sensitivities and specificities of (18)F-FDG PET/CT were 0% and 91.7% for ILC and 37.5% and 94.7% for IDC, respectively. The sensitivity of (18)F-FDG PET/CT for ipsilateral lesion(s) was significantly lower in the ILC group than the IDC group. The sensitivity for ipsilateral lesion(s) was significantly higher with MRI; however, specificity was higher with (18)F-FDG PET/CT in both tumor groups. There was no significant difference in the diagnostic performance for additional contralateral lesion(s) or axillary lymph node metastasis on MRI or (18)F-FDG PET/CT for ILC versus IDC. The MRI and (18)F-FDG PET/CT detection rates for the primary cancer do not differ between the ILC and IDC groups. Although (18)F-FDG PET/CT demonstrates lower sensitivity for primary and additional ipsilateral lesions, it shows higher specificity for additional ipsilateral lesions, and could play a complementary role in the staging of ILC as well as IDC.

[Role and responsibility of multimodal imaging in head and neck cancer].

PubMed

Gõdény, Mária

2013-09-01

Hungary is first in head and neck cancer mortality in Europe in men and also in women. Head and neck (HN) is a difficult region, its anatomy and also pathology is very complex, various connection points exist between the sites which determine the extension of the disease. Diagnostic algorithms as well as imaging techniques have to be optimized to examine in standard manner. Like most other cancers, prognosis depends largely on the stage of the tumor. Accuracy of tumor detection and evaluation is very important because it affects treatment planning. As non-surgical organ-preserving therapeutic modalities (chemotherapy, chemoradiotherapy, targeted biological therapy) gain general acceptance, the importance of noninvasive diagnostic accuracy as well as radiologic evaluation of the extent of the tumor has increased. Clinical examinations including endoscopy should be combined with radiologic imaging to assess the precise local (T), regional nodal (N), and distant (M) extent of the tumor. Computed tomography (CT) and magnetic resonance imaging (MRI) have become basic tools in the diagnosis of head and neck tumors. They are both useful for assessing deep tumor extensions, able to detect changes missed by endoscopy. It has been shown that the primary determined tumor stage increases in up to 90% of patients after the results of cross sectional imaging. MRI is being increasingly used and has become the gold standard in head and neck cancer for staging, assessing tumor response, finding recurrent tumor and also for treatment planning in radiotherapy. The field strength of MRI scanners has been increasing to 1.5 T and now 3 T with better signal-to-noise ratio, higher resolution images and better tissue diagnosis. Functional MR techniques such as dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) may provide further characterization. PET/CT is beneficial in detecting unsuspected metastatic nodes, distant disease and second primary tumor. PET/CT and MRI both appeared almost similarly accurate in the detection of an occult primary tumor. The effective management of patients depends highly on the competece of radiologists and requires close collaboration between clinical and surgical oncologists, diagnostic and therapeutic radiologists as well as pathologists.

Genetic and Diagnostic Biomarker Development in ASD Toddlers Using Resting State Functional MRI

DTIC Science & Technology

2015-09-01

connectivity networks during natural sleep as a neurologic biomarker for ASD that is suitable for diagnostic use in young children (ages 1-4). Existing... Autism Spectrum Disorders (ASD); functional magnetic resonance imaging (fMRI); connectivity modeling 16. SECURITY CLASSIFICATION OF: 17. LIMITATION...Requirements………………………………….. 4 9. Appendices…………………………………………………………….. 4 1. INTRODUCTION Autism spectrum disorders (ASD), characterized by abnormal

A Genome-Wide Identified Risk Variant for PTSD is a Methylation Quantitative Trait Locus and Confers Decreased Cortical Activation to Fearful Faces

DTIC Science & Technology

2015-05-18

Department of Defense; Grant numbers: W81XWH-09-2-0044, W911NF-09-1-0298; Grant sponsor: Emory and Grady Memorial Hospital General Clinical Research...TERMS GWAS; PTSD; fMRI ; meQTL; epigenetic 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 10...that contribute to this disorder. 2015 Wiley Periodicals, Inc. Key words: GWAS; PTSD; fMRI ; meQTL; epigenetic INTRODUCTION Although post-traumatic