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Predicting disability progression in secondary progressive multiple sclerosis by machine learning : a comparison of common methods and analysis of data limitations

University of British Columbia

Secondary progressive MS (SPMS) is a late stage neurological disease characterized by chronic worsening. Enhanced prediction of SPMS progression could improve clinical trial design and may inform patient/physician treatment decisions, but the task is difficult since MS is characterized by heterogeneity in terms of clinical features, genetics, pathogenesis, and treatment response. The Expanded Disability Status Scale (EDSS), is a nominal MS disability scale for describing physical disability that is often incorrectly treated as a continuous variable. Machine learning (ML) models identify relationships between features and outcome, while deep learning (DL) adds on automatic feature extraction from low-level data. Although both have been applied to MS classification and early-stage transition prediction, late-stage MS disability progression prediction is lacking. The contributions of this thesis are the design, implementation, and evaluation of 1) ML using user-defined features (UDF), 2) DL using automatically extracted brain lesion mask features (BLM) for predicting SPMS disability progression, and 3) an evaluation of the impact on performance when EDSS is misused as a continuous variable. SPMS participants (n=485) in a 2-year placebo-controlled (negative) trial of MBP8298 were labelled progressors if a 6-month-sustained increase in EDSS (≥1.0 and ≥0.5 for a baseline of ≤5.5 and ≥6.0 respectively) was observed within 24 months. UDF included EDSS, Multiple Sclerosis Functional Composite component scores, T₂ lesion volume, brain parenchymal fraction, disease duration, age, and sex. Logistic regression (LR), ensemble support vector machines (enSVM), random forest (RF), and AdaBoost decision trees (AdBDT) were trained using UDF only. DL networks were trained to extract BLM features and predict progression with and without UDF. The primary outcome was the area under the receiver operating characteristic curve (AUC). Of the 485 participants, 115 progressed. When using continuous EDSS, AdBDT and RF had a greater AUC (60.3% and 56.2%) than enSVM (52.1%) and LR (44.7%), and DL using only BLM features outperformed LR using UDF (55.0% vs. 45.0%). UDF did not improve DL. RF and AdBDT were robust to EDSS treatment. SPMS trial cohorts selected by ML, DL, or both, could identify those at highest risk for progression, enabling smaller, shorter studies.

Text

2020-07-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/70914

Biomedical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/