Abstract Details

Title Limited Value of Radiomics Compared to Quantitative MRI Measures for Predicting 10-year Disability in Newly Diagnosed Multiple Sclerosis: A Real-world Data Exploratory Study

Topic Multiple Sclerosis

Presentation(s) P11 - Poster Session 11 (5:30 PM-6:30 PM)

Poster/Presentation
Number 6-018

Background MRI measures (lesions, linear-atrophy) correlate with MS severity, however their predictive value for long-term prognosis is limited. Machine learning (ML) classifiers perform well for cross-sectional disability prediction, but their value for long-term EDSS-prediction is unclear.

Objective

To compare the predictive value of radiomic features versus quantitative MRI for long-term disability in newly-diagnosed people with Multiple Sclerosis (MS).

Design/Methods

158 MRI (sagittal T2-FLAIR and T1-weighted spin-echo sequences) and clinical data-sets of eighty-one patients with MS from the Nottingham MS Clinic [52 women;35.4(±10.3)y; diagnosis, five- and ten-years data] were used. We measured the T2-FLAIR-lesion(≥3mm)number/volumes, and linear-atrophy (third-ventricular width, medullary width, corpus callosum index and inter-caudate diameter) using 3DSlicer4.13.0.

107 radiomics features were extracted from the T2-FLAIR images using Pyradiomics package. A Multilayer-Perceptron (MLP) model was trained on clinical data, with/without the radiomic features, to forecast the likelihood of EDSS score ≥6 at 10y. Due to the limited amount of data, a feature-ranking strategy was executed using Random Forest. With a fine-tuning on a small validation set, the number of features was reduced to <10 to reduce noise and prevent overfitting.

Results The MLP classifiers were tested on the whole dataset using 5-fold cross-validation approach. The accuracy for predicting 10y EDSS ≥6 before/after feature selection was 0.56/0.77 for the set of features including clinical/demographic and quantitative MRI data. Baseline(diagnosis) clinical/demographic features alone had a comparable accuracy (0.74). Adding radiomic features obtained from the clinical scans at diagnosis did not significantly improve accuracy (0.56/0.79). Adding 5y-follow-up data slightly improved accuracy (0.62/0.85).

Conclusions Within the limitation of the small sample-size, the use of radiomic features from first (diagnostic) MS clinical scan does not significantly improve the prediction of long-term disability accumulation compared to quantitative MRI. Mechanisms underlying disability progression in MS are complex, and predictive models should account for the relative weight of various factors beyond routine brain imaging.

Authors/Disclosures
Radu Tanasescu, MD, PhD (Clinical Neurology) PRESENTER	Dr. Tanasescu has received personal compensation for serving as an employee of Merck. Dr. Tanasescu has received personal compensation for serving as an employee of Novartis. Dr. Tanasescu has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Dr. Tanasescu has received research support from UK MRC grant MR/T024402/1.
Ruizhe Li	No disclosure on file
Amjad Altokhis	No disclosure on file
Paul Morgan	No disclosure on file
Arman Eshaghi	No disclosure on file
Xin Chen (University of Nottingham)	No disclosure on file
Nikolaos Evangelou, PhD (Nottingham University, QMC campus)	Dr. Evangelou has received personal compensation in the range of $0-$499 for serving as a Consultant for Novartis . Dr. Evangelou has received personal compensation in the range of $0-$499 for serving as a Consultant for Novartis . The institution of Dr. Evangelou has received personal compensation in the range of $100,000-$499,999 for serving as a Consultant for Roche. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Merck . Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Novartis. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen . Dr. Evangelou has received personal compensation in the range of $0-$499 for serving on a Scientific Advisory or Data Safety Monitoring board for Novartis . Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for biogen. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Novartis. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for UK crown prosecution service. Dr. Evangelou has received personal compensation in the range of $500-$4,999 for serving as an Expert Witness for Novartis .