Dataset Information

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

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

SUBMITTER: Ion-Margineanu A

PROVIDER: S-EPMC5504183 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Ion-Mărgineanu Adrian A Kocevar Gabriel G Stamile Claudio C Sima Diana M DM Durand-Dubief Françoise F Van Huffel Sabine S Sappey-Marinier Dominique D

Frontiers in neuroscience 20170711

<b>Purpose:</b> The purpose of this study is classifying multiple sclerosis (MS) patients in the four clinical forms as defined by the McDonald criteria using machine learning algorithms trained on clinical data combined with lesion loads and magnetic resonance metabolic features. <b>Materials and Methods:</b> Eighty-seven MS patients [12 Clinically Isolated Syndrome (CIS), 30 Relapse Remitting (RR), 17 Primary Progressive (PP), and 28 Secondary Progressive (SP)] and 18 healthy controls were inc ...[more]

PMID: 28744195

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Project description:Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

Project description:Cortical lesions (CLs) contribute to physical and cognitive disability in multiple sclerosis (MS). Accurate methods for visualization of CLs are necessary for future clinical studies and therapeutic trials in MS.To evaluate the clinical relevance of measures of CL burden derived from high-field magnetic resonance imaging (MRI) in MS.An observational clinical imaging study was conducted at an academic MS center. Participants included 36 individuals with MS (30 relapsing-remitting, 6 secondary or primary progressive) and 15 healthy individuals serving as controls. The study was conducted from March 10, 2010, to November 23, 2012, and analysis was performed from June 1, 2011, to September 30, 2014. Seven-Tesla MRI of the brain was performed with 0.5-mm isotropic resolution magnetization-prepared rapid acquisition gradient echo (MPRAGE) and whole-brain, 3-dimensional, 1.0-mm isotropic resolution magnetization-prepared, fluid-attenuated inversion recovery (MPFLAIR). Cortical lesions, seen as hypointensities on MPRAGE, were manually segmented. Lesions were classified as leukocortical, intracortical, or subpial. Images were segmented using the Lesion-TOADS (Topology-Preserving Anatomical Segmentation) algorithm, and brain structure volumes and white matter (WM) lesion volume were reported. Volumes were normalized to intracranial volume.Physical disability was measured by the Expanded Disability Status Scale (EDSS). Cognitive disability was measured with the Minimal Assessment of Cognitive Function in MS battery.Cortical lesions were noted in 35 of 36 participants (97%), with a median of 16 lesions per participant (range, 0-99). Leukocortical lesion volume correlated with WM lesion volume (??=?0.50; P?=?.003) but not with cortical volume; subpial lesion volume inversely correlated with cortical volume (??=?-0.36; P?=?.04) but not with WM lesion volume. Total CL count and volume, measured as median (range), were significantly increased in participants with EDSS scores of 5.0 or more vs those with scores less than 5.0 (count: 29 [11-99] vs 13 [0-51]; volume: 2.81?×?10-4 [1.30?×?10-4 to 7.90?×?10-4] vs 1.50?×?10-4 [0 to 1.01?×?10-3]) and in cognitively impaired vs unimpaired individuals (count: 21 [0-99] vs 13 [1-54]; volume: 3.51?×?10-4 [0 to 1.01?×?10-4] vs 1.19?×?10-4 [0 to 7.17?×?10-4]). Cortical lesion volume correlated with EDSS scores more robustly than did WM lesion volume (??=?0.59 vs 0.36). Increasing log[CL volume] conferred a 3-fold increase in the odds of cognitive impairment (odds ratio [OR], 3.36; 95% CI, 1.07-10.59; P?=?.04) after adjustment for age and sex and a 14-fold increase in odds after adjustment for WM lesion volume and atrophy (OR, 14.26; 95% CI, 1.06-192.37; P?=?.045). Leukocortical lesions had the greatest effect on cognition (OR for log?[leukocortical lesion volume], 9.65; 95% CI, 1.70-54.59, P?=?.01).This study provides in vivo evidence that CLs are associated with cognitive and physical disability in MS and that leukocortical and subpial lesion subtypes have differing clinical relevance. Quantitative assessments of CL burden on high-field MRI may further our understanding of the development of disability and progression in MS and lead to more effective treatments.

Project description:PurposeThis study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.MethodsBrain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.ResultsAfter feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.ConclusionThe combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Dataset Information

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

Publications

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

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