Multimodal spinal cord MRI offers accurate diagnostic classification in ALS

Introduction

Amyotrophic lateral sclerosis (ALS) has distinctive and well-established radiological signatures which are increasingly utilised in advanced machine-learning algorithms heralding exciting novel diagnostic applications.1 2

Quantitative spinal cord (SC) imaging provides a unique opportunity to evaluate both upper (UMN) and lower motor neuron (LMN) involvement, and recent studies have showcased its biomarker potential in ALS through reliable cross-sectional area (CSA) measurements, evaluation of diffusion tensor imaging (DTI) parameters, correlations with clinical measures and survival.3 4 Nevertheless, no studies have evaluated the diagnostic accuracy of SC metrics in ALS to date. Accordingly, the objective of this study was to evaluate the effectiveness of multimodal cervical imaging in distinguishing ALS from healthy controls (HC) using a random forest (RF) classification algorithm.5

Methods

Sixty patients with ALS and 45 age-matched controls gave informed consent to participate in a prospective neuroimaging study in the Pitié-Salpêtrière Hospital...

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