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Predicting outcome in clinically isolated syndrome using machine learning

December 2015
NeuroImage: Clinical 7(C):281-7

DOI:10.1016/j.nicl.2014.11.021

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PubMed

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CC BY 3.0

Authors:

Viktor Wottschel

Amsterdam University Medical Center

Daniel C Alexander

University College London

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We aim to determine if machine learning techniques, such as support vector machines (SVMs), can predict the occurrence of a second clinical attack, which leads to the diagnosis of clinically-definite Multiple Sclerosis (CDMS) in patients with a clinically isolated syndrome (CIS), on the basis of single patient's lesion features and clinical/demographic characteristics. Seventy-four patients at onset of CIS were scanned and clinically reviewed after one and three years. CDMS was used as the gold standard against which SVM classification accuracy was tested. Radiological features related to lesional characteristics on conventional MRI were defined a priori and used in combination with clinical/demographic features in an SVM. Forward recursive feature elimination with 100 bootstraps and a leave-one-out cross-validation was used to find the most predictive feature combinations. 30 % and 44 % of patients developed CDMS within one and three years, respectively. The SVMs correctly predicted the presence (or the absence) of CDMS in 71.4 % of patients (sensitivity/specificity: 77 %/66 %) at 1 year, and in 68 % (60 %/76 %) at 3 years on average over all bootstraps. Combinations of features consistently gave a higher accuracy in predicting outcome than any single feature. Machine-learning-based classifications can be used to provide an “individualised” prediction of conversion to MS from subjects' baseline scans and clinical characteristics, with potential to be incorporated into routine clinical practice.

Performance of single features vs. feature combination. Bar plot showing the classification accuracy of all individual features vs. the best combination of features obtained with SVMs.

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Example of T2 and PD weighted images and corresponding binary lesion mask. Axial T2 weighted image (left), and proton density (PD) weighted image (centre), showing hyperintense white matter lesions; the corresponding binary lesion mask (right) was used to obtain the lesion features entered into the SVM analysis.

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Accuracies of forward RFE for 1-year prediction. Plot showing the development of accuracies after recursively adding features in order to find the most predictive combination for conversion within 1 year.

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Accuracies of forward RFE for 3-year prediction. Plot showing the development of accuracies after recursively adding features in order to find the most predictive combination for conversion within 3 years.

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Illustration of one permutation within a leave-one-out cross-validation using support vector machines. Training phase: data points with known labels are used to create an optimal separating hyperplane (OSH). Testing phase: previously unseen data point (grey) is assigned a label (converter) based on the position relative to the OSH.

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Present and future of the diagnostic work-up of multiple sclerosis: the imaging perspective

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Full-text available

Nov 2022
J NEUROL

In recent years, the use of magnetic resonance imaging (MRI) for the diagnostic work-up of multiple sclerosis (MS) has evolved considerably. The 2017 McDonald criteria show high sensitivity and accuracy in predicting a second clinical attack in patients with a typical clinically isolated syndrome and allow an earlier diagnosis of MS. They have been validated, are evidence-based, simplify the clinical use of MRI criteria and improve MS patients’ management. However, to limit the risk of misdiagnosis, they should be applied by expert clinicians only after the careful exclusion of alternative diagnoses. Recently, new MRI markers have been proposed to improve diagnostic specificity for MS and reduce the risk of misdiagnosis. The central vein sign and chronic active lesions (i.e., paramagnetic rim lesions) may increase the specificity of MS diagnostic criteria, but further effort is necessary to validate and standardize their assessment before implementing them in the clinical setting. The feasibility of subpial demyelination assessment and the clinical relevance of leptomeningeal enhancement evaluation in the diagnostic work-up of MS appear more limited. Artificial intelligence tools may capture MRI attributes that are beyond the human perception, and, in the future, artificial intelligence may complement human assessment to further ameliorate the diagnostic work-up and patients’ classification. However, guidelines that ensure reliability, interpretability, and validity of findings obtained from artificial intelligence approaches are still needed to implement them in the clinical scenario. This review provides a summary of the most recent updates regarding the application of MRI for the diagnosis of MS.

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

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Oct 2022
HUM BRAIN MAPP

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical , neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis

Article

Full-text available

May 2023

Introduction Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In this study, we have compared three ML models to determine whether accounting for label uncertainty can improve a model’s predictions. Methods We used a dataset from a completed phase-III clinical trial that evaluated the efficacy of minocycline for delaying the conversion from clinically isolated syndrome to multiple sclerosis (MS), using the McDonald 2005 diagnostic criteria. There were a total of 142 participants, and at the 2-year follow-up 81 had converted to MS, 29 remained stable, and 32 had uncertain outcomes. In a stratified 7-fold cross-validation, we trained three random forest (RF) ML models using MRI volumetric features and clinical variables to predict the conversion outcome, which represented new disease activity within 2 years of a first clinical demyelinating event. One RF was trained using subjects with the uncertain labels excluded (RF exclude ), another RF was trained using the entire dataset but with assumed labels for the uncertain group (RF naive ), and a third, a probabilistic RF (PRF, a type of RF that can model label uncertainty) was trained on the entire dataset, with probabilistic labels assigned to the uncertain group. Results Probabilistic random forest outperformed both the RF models with the highest AUC (0.76, compared to 0.69 for RF exclude and 0.71 for RF naive ) and F1-score (86.6% compared to 82.6% for RF exclude and 76.8% for RF naive ). Conclusion Machine learning algorithms capable of modeling label uncertainty can improve predictive performance in datasets in which a substantial number of subjects have unknown outcomes.

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Article

Full-text available

Mar 2023

Introduction: Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns. Methods: In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself. Results and discussion: Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.

Multiple Sclerosis Disease Evolution Assessment in Brain MRI Lesions Based on Texture and Multi-Scale Amplitude Modulation-Frequency Modulation (AM-FM) Features

Article

Full-text available

Jan 2023

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background : Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). Methods : MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T 2 W) images obtained from serial brain MRI scans at the baseline (Time 0M ) and the repeat (Time 6-12M ) examinations. The subjects were separated into two different groups based on their EDSS: (G 1 : 1≤EDSS 2Y ≤3.5 (N=26) and G 2 : 3.5<EDSS 2Y ≤8.5 (N=12) and were monitored over ten years’ time (Time 10Y ). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time 0M and Time 6-12M . The extracted features were used to develop models that correlated with the disease progression in Time 10Y . Results : We found statistically significant differences for features extracted from the two different groups (G 1 vs G 2 at Time 10Y ) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G 1 vs G 2 subjects at Time 10Y included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). Conclusions : The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles

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Aug 2022

Multiple sclerosis (MS) is a heterogeneous autoimmune disease for which diagnosis continues to rely on subjective clinical judgment over a battery of tests. Proton magnetic resonance spectroscopy (1H MRS) enables the noninvasive in vivo detection of multiple small-molecule metabolites and is therefore in principle a promising means of gathering information sufficient for multiple sclerosis diagnosis and subtype classification. Here we show that supervised classification using 1H-MRSvisible normal-appearing frontal cortex small-molecule metabolites alone can indeed differentiate individuals with progressive MS from control (held-out validation sensitivity 79% and specificity 68%), as well as between relapsing and progressive MS phenotypes (held-out validation sensitivity 84% and specificity 74%). Post hoc assessment demonstrated the disproportionate contributions of glutamate and glutamine to identifying MS status and phenotype, respectively. Our finding establishes 1H MRS as a viable means of characterizing progressive multiple sclerosis disease status and paves the way for continued refinement of this method as an auxiliary or mainstay of multiple sclerosis diagnostics.

Prediction of Conversion from CIS to Clinically Definite Multiple Sclerosis Using Convolutional Neural Networks

Article

Full-text available

Jul 2022

Multiple sclerosis (MS) is a chronic neurological disease of the central nervous system (CNS). Early diagnosis of MS is highly desirable as treatments are more effective in preventing MS-related disability when given in the early stages of the disease. The main aim of this research is to predict the occurrence of a second MS-related clinical event, which indicates the conversion of clinically isolated syndrome (CIS) to clinically definite MS (CDMS). In this study, we apply a branch of artificial intelligence known as deep learning and develop a fully automated algorithm primed with convolutional neural network (CNN) that has the ability to learn from MRI scan features. The basic architecture of our algorithm is that of the VGG16 CNN model, but amended such that it can handle MRI DICOM images. A dataset comprised of scans acquired using two different scanners was used for the purposes of verification of the algorithm. A group of 49 patients had volumetric MRI scans taken at onset of the disease and then again one year later using one of the two scanners. In total, this yielded 7360 images which were then used for training, validation, and testing of the algorithm. Initially, these raw images were taken through 4 steps of preprocessing. In order to boost the efficiency of the process, we pretrained our algorithm using the publicly available ADNI dataset used to classify Alzheimer’s disease. Finally, we used our preprocessed dataset to train and test the algorithm. Clinical evaluation conducted a year after the first time point revealed that 26 of the 49 patients had converted to CDMS, while the remaining 23 had not. Results of testing showed that our algorithm was able to predict the clinical results with an accuracy of 88.8% and with an area under the curve (AUC) of 91%. A highly accurate algorithm was developed using CNN approach to reliably predict conversion of patients with CIS to CDMS using MRI data from two different scanners.

Intelligent computer systems for multiple sclerosis diagnosis

Chapter

Feb 2022

Background The diagnosis of multiple sclerosis is complicated since it has multifarious signs and symptoms which are similar to the symptoms of other neurological diseases. Intelligent computer systems are used in diagnosing multiple sclerosis and help physicians in the accurate and timely diagnosis of the disease. Objective This study focuses on a review of different reasoning techniques and methods used in intelligent systems to diagnose MS and analyze the application and efficiency of different reasoning methods, too. The purpose of this study is to design and develop CDSS softwares to help physicians diagnose MS with relapsing-remitting phenotype. Methods A complete research was carried out on articles in various electronic databases based on Mesh vocabulary. A total of 85 articles of 614 articles published in English between 2000 and 2018 were analyzed. Besides, in this study, the two clinical decision support system software was developed in four stages: (1) requirement analysis, (2) system design, (3) system development, and (4) system evaluation. Results Results indicate that different reasoning methods are used in intelligent systems of MS diagnosis. In 27% of the studies, the rule-based method was used, in 20% the fuzzy logic method, in 18% the artificial neural network method, and in 35% other reasoning methods were used. The average sensitivity, specificity, and accuracy of reasoning methods were 0.91, 0.77, and 0.86, respectively. Conclusions Rule-based, fuzzy logic and artificial neural network methods have had more applications in intelligent systems for the diagnosis of MS. The highest rate of sensitivity and accuracy indexes is associated with the neural network reasoning method at 0.97 and 0.99, respectively. In the fuzzy logic and rule-based methods, the Kappa rate has been reported, which shows full conformity between software diagnosis and the physician’s decision.

Future activity prediction of multiple sclerosis with 3D MRI using 3D discrete wavelet transform

Article

Sep 2022
BIOMED SIGNAL PROCES

Multiple Sclerosis (MS) is a chronic and autoimmune neurological disease that is frequently seen especially in young people. MS lesions that can be seen with magnetic resonance imaging (MRI) findings are important biomarkers that provide information about the clinical prognosis and activity of the disease. The presence of new MS lesions is associated with future disease activity. This study aims to predict the future activity of MS using the 3D discrete wavelet transform (DWT) as a feature extraction method from 3D MRI. The 3D-DWT can be used as it provides spatial and spectral location features of MS lesions without losing their relationship between MRI slices. Ten different wavelet families of DWT are used individually, each of them is classified by six machine learning algorithms, and their feature extraction performances are compared. The highest F1-score, Precision, and Recall of 95.0% are obtained by the support vector machine algorithm on the SYM4, SYM8, and Haar wavelet families in the 3D MRI dataset consisting of 40 patients based on 5-fold cross validation. The results show that the 3D-DWT method is an effective method for feature extraction in predicting the future activity of MS.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

Article

Jul 2022

Introduction The applications of artificial intelligence, and in particular automatic learning or “machine learning” (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years. Objective We present a systematic review of the application of ML algorithms in MS. Materials and methods We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords “machine learning” and “multiple sclerosis.” We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected. Conclusions After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Article

Full-text available

Dec 2013

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

Spinal cord MRI should always be performed in clinically isolated syndrome patients: Commentary

Article

Full-text available

Oct 2014

Michael Hutchinson

Location of brain lesions predicts conversion of clinically isolated syndromes to multiple sclerosis

Article

Full-text available

Dec 2012
NEUROLOGY

Objectives: To assess in a large population of patients with clinically isolated syndrome (CIS) the relevance of brain lesion location and frequency in predicting 1-year conversion to multiple sclerosis (MS). Methods: In this multicenter, retrospective study, clinical and MRI data at onset and clinical follow-up at 1 year were collected for 1,165 patients with CIS. On T2-weighted MRI, we generated lesion probability maps of white matter (WM) lesion location and frequency. Voxelwise analyses were performed with a nonparametric permutation-based approach (p < 0.05, cluster-corrected). Results: In CIS patients with hemispheric, multifocal, and brainstem/cerebellar onset, lesion probability map clusters were seen in clinically eloquent brain regions. Significant lesion clusters were not found in CIS patients with optic nerve and spinal cord onset. At 1 year, clinically definite MS developed in 26% of patients. The converting group, despite a greater baseline lesion load compared with the nonconverting group (7 ± 8.1 cm3 vs. 4.6 ± 6.7 cm3, p < 0.001), showed less widespread lesion distribution (18% vs. 25% of brain voxels occupied by lesions). High lesion frequency was found in the converting group in projection, association, and commissural WM tracts, with larger clusters being in the corpus callosum, corona radiata, and cingulum. Conclusions: Higher frequency of lesion occurrence in clinically eloquent WM tracts can characterize CIS subjects with different types of onset. The involvement of specific WM tracts, in particular those traversed by fibers involved in motor function and near the corpus callosum, seems to be associated with a higher risk of clinical conversion to MS in the short term.

Pattern Recognition and Machine Learning

Book

Jan 2006

Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis

Article

Nov 1997
BRAIN

Frederik Barkhof

We compared MRI criteria used to predict conversion of suspected multiple sclerosis to clinically definite multiple sclerosis. Seventy-four patients with clinically isolated neurological symptoms suggestive of multiple sclerosis were studied with MRI. Logistic regression analysis was used to remove redundant information, and a diagnostic model was built after each MRI parameter was dichotomized according to maximum accuracy using receiver operating characteristic analysis. Clinically definite multiple sclerosis developed in 33 patients (prevalence 45%). The optimum cut-off point (number of lesions) was one for most MRI criteria (including gadolinium-enhancement and juxta-cortical lesions), but three for periventricular lesions, and nine for the total number of T2-lesions. Only gadolinium-enhancement and juxta-cortical lesions provided independent information. A final model which, in addition, included infratentorial and periventricular lesions, had an accuracy of 80%, and having more abnormal criteria, predicted conversion to clinically definite multiple sclerosis strongly. The model performed better than the criteria of Paty et al. (Neurology 1988; 38: 180-5) and of Fazekas et al. (Neurology 1988; 38: 1822-5). We concluded that a four-parameter dichotomized MRI model including gadolinium-enhancement, juxtacortical, infratentorial and periventricular lesions best predicts conversion to clinically definite multiple sclerosis.

Book Review: The Nature of Statistical Learning Theory

Article

Nov 1996

Stephan Sain

Pattern Recognition and Machine Learning Errata

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Jan 2006

Christopher M. Bishop

The Nature Of Statistical Learning Theory

Book

Jan 1995

Vladimir N. Vapnik

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

Statistical Learning Theory

Article

Jan 1998

Vapnik VN

Spinal cord lesions in patients with clinically isolated syndrome A powerful tool in diagnosis and prognosis

Article

Dec 2012
NEUROLOGY

Objective: Spinal cord (SC) lesions are frequently found in multiple sclerosis (MS), but are rare in healthy aging and cerebrovascular patients. Our aim was to analyze the contribution of SC involvement in clinically isolated syndrome (CIS) in diagnosing MS according the McDonald 2010 criteria and in predicting conversion to clinically definite MS (CDMS). Methods: We prospectively followed monofocal, relapsing onset CIS patients with either SC or brain symptom onset (including optic neuritis). MRI of the brain and SC were performed shortly after onset and patients were followed for 24 to 119 months (median 64 months). SC MRI findings were assessed for their contribution to the McDonald 2010 diagnostic criteria and their effect on conversion to CDMS. Results: One hundred twenty-one patients were included (63 spinal CIS). Based on the brain scan only, 36 patients fulfilled the McDonald criteria; by including SC findings, 6 additional patients fulfilled these criteria. To diagnose 1 additional nonspinal CIS patient, the number needed to scan is 7. In nonspinal CIS patients that did not fulfill McDonald brain MRI criteria (n = 42), presence of an SC lesion was associated with a higher risk of conversion to CDMS (odds ratio: 14.4; 95% confidence interval: 2.6-80.0) and shorter time to conversion to CDMS (hazard ratio: 51.4; 95% confidence interval: 5.5-476.3). Conclusions: Presence of SC lesions facilitates diagnosing MS and is predictive for conversion to CDMS, especially in patients with nonspinal CIS who do not fulfill brain MRI criteria. We therefore recommend performing an SC scan in patients with nonspinal CIS who do not fulfill McDonald brain MRI criteria.