Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images

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

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

A meta‐analysis comparing first‐line immunosuppressants in neuromyelitis optica

ObjectiveAs phase III trials have shown interest in innovative but expensive drugs in the treatment of neuromyelitis optica spectrum disorder (NMOSD), data are needed to clarify strategies in the treatment of neuromyelitis optica (NMO). This meta-analysis compares the efficacy of first-line strategies using rituximab (RTX), mycophenolate mofetil (MMF), or azathioprine (AZA), which are still widely used.MethodsStudies identified by the systematic review of Huang et al. (2019) were selected if they considered at least two first-line immunosuppressants among RTX, MMF, and AZA. We updated this review. The Medline, Cochrane Central Register of Controlled Trials, Embase, and ClinicalTrials databases were queried between November 2018 and April 2020. To be included, the hazard ratio (HR) [95% CI] for the time to first relapse after first-line immunosuppression had to be available, calculable, or provided by the authors.ResultsWe gathered data from 919 NMO patients (232 RTX-, 294 MMF-, and 393 AZA-treated patients). The risk of first relapse after first-line immunosuppression was 1.55 [1.04, 2.31] (p = 0.03) for MMF compared with RTX, 1.42 [0.87, 2.30] (p = 0.16) for AZA compared with RTX, and 0.94 [0.58, 1.54] (p = 0.08) for MMF compared with AZA.InterpretationThe findings suggest that RTX is more efficient than MMF as a first-line therapy. Even if the results of our meta-analysis cannot conclude that RTX has a better efficacy in delaying the first relapse than AZA, the observed effect difference between both treatments combined with the results of previous studies using as outcome the annualized relapse rate may be in favor of RTX

Évaluation de la perte du volume cérébral en IRM comme marqueur individuel de neurodégénérescence des patients atteints de sclérose en plaques.

Brain volume loss is currently a MRI marker of neurodegeneration in MS. The available algorithms for its quantification perfom either direct measurements, or indirect measurements. Their reliability remains difficult to assess especially since there is no gold standard technique. This work consisted first, in a reproducibility study performed on nine patients’ biannual MRI acquisitions (3 time points). These acquisitions were performed on two different MRI systems. Post-processing was applied using seven algorithms: BBSI, FreeSurfer, Jacobian Integration, KNBSI, an algorithm based on segmentation/classification, SIENA and SIENAX. Second, a longitudinal and prospective study was performed in 90 MS patients. The study of inter-technique and inter-site variabilities showed that direct measurement techniques and SIENAX provided heterogeneous values of atrophy. In contrast, indirect measurement algorithms such as BBSI, KNBSI, Jacobian Integration and to a lesser extent SIENA obtained reproducible results. However BBSI, KNBSI and Jacobian Integration algorithms showed lower percentages, suggesting a possible underestimation of atrophy. The evaluation of brain volume loss by Jacobian Integration has shown an atrophy rate of 1.21% over 2 ½ years of the 90 patients’ follow up, and of 1.55%, 1.51%, 0.84%, 1.21% for CIS, RR, SP and PP patients respectively. Jacobian Integration showed its importance in individual monitoring. In the future, assessing brain volume loss requires overcoming of some technical challenges to improve the reliability of the currently available algorithms.La mesure de la perte du volume cérébral est un marqueur IRM de la neurodégénérescence dans la sclérose en plaques. Les techniques actuelles permettent de quantifier soit directement la perte de volume cérébral entre deux examens, soit de la mesurer indirectement à partir du volume cérébral de chaque examen. La fiabilité de ces techniques reste difficile à évaluer en l’absence de gold standard. Ce travail a consisté premièrement, en une étude de reproductibilité réalisée chez 9 patients à partir d’acquisitions semestrielles (3 IRM), sur deux machines différentes et post-traitées par sept algorithmes : BBSI, FreeSurfer, Intégration Jacobienne, KNBSI, un algorithme Segmentation / Classification, SIENA et SIENAX. Deuxièmement, un suivi longitudinal et prospectif a été effectué chez 90 patients SEP. L’étude des variabilités inter-techniques et inter-sites a montré que les techniques de mesures indirectes (Segmentation/Classification, FreeSurfer) et SIENAX fournissaient des pourcentages d’atrophie hétérogènes. A l’inverse, les techniques de mesures directes telles que BBSI, KNBSI, Intégration Jacobienne et à un moindre degré SIENA obtenaient des résultats reproductibles. Toutefois BBSI, KNBSI et l’Intégration Jacobienne obtenaient des pourcentages faibles, suggérant une possible sous-estimation de l’atrophie. L’évaluation de la perte du volume cérébral par Intégration Jacobienne a montré sur 2½ ans de suivi, une atrophie de 1,21% pour les 90 patients et de 1,55%, 1,51%, 0,84%, 1,21% respectivement pour les patients CIS, RR, SP et PP. A l’avenir l’évaluation de la perte de volume cérébral impose des défis d’ordre technique afin d’améliorer la fiabilité des algorithmes actuels

Evaluation of brain volume loss on MRI as an individual marker of neurodegeneration in multiple sclerosis

La mesure de la perte du volume cérébral est un marqueur IRM de la neurodégénérescence dans la sclérose en plaques. Les techniques actuelles permettent de quantifier soit directement la perte de volume cérébral entre deux examens, soit de la mesurer indirectement à partir du volume cérébral de chaque examen. La fiabilité de ces techniques reste difficile à évaluer en l’absence de gold standard. Ce travail a consisté premièrement, en une étude de reproductibilité réalisée chez 9 patients à partir d’acquisitions semestrielles (3 IRM), sur deux machines différentes et post-traitées par sept algorithmes : BBSI, FreeSurfer, Intégration Jacobienne, KNBSI, un algorithme Segmentation / Classification, SIENA et SIENAX. Deuxièmement, un suivi longitudinal et prospectif a été effectué chez 90 patients SEP. L’étude des variabilités inter-techniques et inter-sites a montré que les techniques de mesures indirectes (Segmentation/Classification, FreeSurfer) et SIENAX fournissaient des pourcentages d’atrophie hétérogènes. A l’inverse, les techniques de mesures directes telles que BBSI, KNBSI, Intégration Jacobienne et à un moindre degré SIENA obtenaient des résultats reproductibles. Toutefois BBSI, KNBSI et l’Intégration Jacobienne obtenaient des pourcentages faibles, suggérant une possible sous-estimation de l’atrophie. L’évaluation de la perte du volume cérébral par Intégration Jacobienne a montré sur 2½ ans de suivi, une atrophie de 1,21% pour les 90 patients et de 1,55%, 1,51%, 0,84%, 1,21% respectivement pour les patients CIS, RR, SP et PP. A l’avenir l’évaluation de la perte de volume cérébral impose des défis d’ordre technique afin d’améliorer la fiabilité des algorithmes actuels.Brain volume loss is currently a MRI marker of neurodegeneration in MS. The available algorithms for its quantification perfom either direct measurements, or indirect measurements. Their reliability remains difficult to assess especially since there is no gold standard technique. This work consisted first, in a reproducibility study performed on nine patients’ biannual MRI acquisitions (3 time points). These acquisitions were performed on two different MRI systems. Post-processing was applied using seven algorithms: BBSI, FreeSurfer, Jacobian Integration, KNBSI, an algorithm based on segmentation/classification, SIENA and SIENAX. Second, a longitudinal and prospective study was performed in 90 MS patients. The study of inter-technique and inter-site variabilities showed that direct measurement techniques and SIENAX provided heterogeneous values of atrophy. In contrast, indirect measurement algorithms such as BBSI, KNBSI, Jacobian Integration and to a lesser extent SIENA obtained reproducible results. However BBSI, KNBSI and Jacobian Integration algorithms showed lower percentages, suggesting a possible underestimation of atrophy. The evaluation of brain volume loss by Jacobian Integration has shown an atrophy rate of 1.21% over 2 ½ years of the 90 patients’ follow up, and of 1.55%, 1.51%, 0.84%, 1.21% for CIS, RR, SP and PP patients respectively. Jacobian Integration showed its importance in individual monitoring. In the future, assessing brain volume loss requires overcoming of some technical challenges to improve the reliability of the currently available algorithms

Graph Theory-Based Brain Connectivity for Automatic Classification of Multiple Sclerosis Clinical Courses

Purpose: In this work, we introduce a method to classify Multiple Sclerosis (MS) patients into four clinical profiles using structural connectivity information. For the first time, we try to solve this question in a fully automated way using a computer-based method. The main goal is to show how the combination of graph-derived metrics with machine learning techniques constitutes a powerful tool for a better characterization and classification of MS clinical profiles.Materials and methods: Sixty-four MS patients (12 Clinical Isolated Syndrome (CIS), 24 Relapsing Remitting (RR), 24 Secondary Progressive (SP), and 17 Primary Progressive (PP)) along with 26 healthy controls (HC) underwent MR examination. T1 and diffusion tensor imaging (DTI) were used to obtain structural connectivity matrices for each subject. Global graph metrics, such as density and modularity, were estimated and compared between subjects’ groups. These metrics were further used to classify patients using tuned Support Vector Machine (SVM) combined with Radial Basic Function (RBF) kernel.Results: When comparing MS patients to HC subjects, a greater assortativity, transitivity and characteristic path length as well as a lower global efficiency were found. Using all graph metrics, the best F-Measures (91.8%, 91.8%, 75.6% and 70.6%) were obtained for binary (HC-CIS, CIS-RR, RR-PP) and multi-class (CIS-RR-SP) classification tasks, respectively. When using only one graph metric, the best F-Measures (83.6%, 88.9% and 70.7%) were achieved for modularity with previous binary classification tasks.Conclusion: Based on a simple DTI acquisition associated with structural brain connectivity analysis, this automatic method allowed an accurate classification of different MS patients’ clinical profiles

Explainable Monotonic Networks and Constrained Learning for Interpretable Classification and Weakly Supervised Anomaly Detection

International audienceDeep networks interpretability is fundamental in critical domains like medicine: using easily explainable networks with decisions based on radiological signs and not on spurious confounders would reassure the clinicians. Confidence is reinforced by the integration of intrinsic properties and characteristics of monotonic networks could be used to design such intrinsically explainable networks. As they are considered as too constrained and difficult to train, they are often very shallow and rarely used for image applications. In this work, we propose a procedure to transform any architecture into a trainable monotonic network, identifying the critical importance of weights initialization, and highlight the interest of such networks for explicability and interpretability. By constraining the features and the gradients of a healthy vs pathological images classifier, we show, using counterfactual examples, that the network decision is more based on the radiological signs of the pathology and outperforms state-of-the-art methods for weakly supervised anomaly detection.</div

A More Interpretable Classifier for Multiple Sclerosis

International audienceOver the past years, deep learning proved its effectiveness in medical imaging for diagnosis or segmentation. Nevertheless, to be fully integrated in clinics, these methods must both reach good performances and convince area practitioners about their interpretability. Thus, an interpretable model should make its decision on clinical relevant information as a domain expert would. With this purpose, we propose a more interpretable classifier focusing on the most widespread autoimmune neuroinflammatory disease: multiple sclerosis. This disease is characterized by brain lesions visible on MRI (Magnetic Resonance Images) on which diagnosis is based. Using Integrated Gradients attributions, we show that the utilization of brain tissue probability maps instead of raw MR images as deep network input reaches a more accurate and interpretable classifier with decision highly based on lesions