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.

Classification of radiologically isolated syndrome and clinically isolated syndrome with machine‐learning techniques

“…Feature extraction methods, feature selection or classification tools, image quality, number of subjects, demographics and clinical diagnosis criteria are also important considerations. Despite all of this, the scores obtained in the current study agree with previous scores based on machine-learning techniques in MS, where the accuracy tends to be around 70% [12,13]. However, the studies published to date in this field have mainly focused on predicting disease course in patients with MS and, in the current study, we are dealing with individuals affected by the earliest forms of MS, i.e.…”

“…Despite the fact that machine‐learning techniques have been widely used for MRI images in MS for predicting disease course , classifying between different MS disease courses or even predicting CIS conversion to MS , no study to date has been conducted to differentiate between patients with CIS and those with RIS. The aim of this study was therefore to test and evaluate the effectiveness of machine‐learning schemes for single‐subject level classification of individuals affected by the earliest forms of MS (CIS and RIS).…”

Quantitative assessment of informative immunophenotypic markers increases the diagnostic value of immunophenotyping in mature CD5‐positive B‐cell neoplasms

“…Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52). Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52).…”

“…Our study provides evidence for the clinical benefits of quantitative immunophenotyping by a rational and effective marker panel followed by the use of a predictive model (diagnostic classifier), minimizing the subjectivity of commonly used expert-based assessment. Machine learning methods based on predictive (classification) models using RF have recently been widely applied in many diagnostic, prognostic and therapeutic studies (40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52). Hereby we also showed its utility for the evaluation of flow cytometry data.…”

Machine Learning in Acute Ischemic Stroke Neuroimaging

“…Machine learning systems are now being implemented in the clinical neurosciences to devise imaging-based diagnostic and classification systems of neoplasms of the brain ( 4 – 6 ), certain psychiatric disorders ( 7 – 11 ), epilepsy ( 12 , 13 ), neurodegenerative disorders ( 14 – 20 ), and demyelinating disorders ( 21 – 23 ). In this review, we discuss the present-day role of ML focusing on acute ischemic stroke (AIS), discussing its potential and limitations.…”