Machine learning-based radiomics for amyotrophic lateral sclerosis diagnosis

Timely diagnosis and accurate phenotyping of amyotrophic lateral sclerosis (ALS) is of paramount importance for the clinical management of patients. Magnetic Resonance Imaging (MRI) plays a key role in the clinical work-up of ALS. In this study we investigated the usefulness of radiomics analysis on T1-weighted MRI to define a machine learning-based classification pipeline.We collected 53 controls and 84 patients with ALS from three different scanners. Following dataset harmonization, radiomics analysis was conducted using different features selection and machine learning algorithms to identify the best combination in distinguishing ALS patients from controls and Classic from non-Classical ALS motor phenotypes.The combined Least Absolute Shrinkage and Selection Operator with Support Vector Machine (SVM) algorithm classified ALS patients with an accuracy of 81.1%. The Maximum Relevance Minimum Redundancy with SVM pipeline was able to distinguish Classic from non-Classical motor phenotypes with 92.9% accuracy.Radiomics is a promising approach to characterize brain abnormalities in patients with ALS. Radiomics could help to improve diagnosis and may prove useful to assess disease severity and longitudinally monitor ALS patients along the disease course.

Automated summary

This study investigates the usefulness of radiomics analysis on T1-weighted MRI in diagnosing and phenotyping ALS patients. Machine learning algorithms were used to distinguish ALS patients from controls and Classic from non-Classical ALS motor phenotypes with high accuracy.