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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
Sixty patients with ALS and 45 age-matched controls gave informed consent to participate in a prospective neuroimaging study in the Pitié-Salpêtrière Hospital in Paris. Participating patients had probable or definite ALS according to the revised El-Escorial criteria. Patients with cognitive impairment, relevant comorbidities and taking medications other than riluzole were excluded from the study.
Cervical cord data were acquired from C2 to C7 on a 3T MRI system (Siemens TIM Trio). A T2-weighted three-dimensional turbo spin echo protocol was used for structural imaging with an isotropic voxel size 0.9×0.9×0.9 mm3, field of view (FOV)=280×280 mm2, 52 sagittal slices, repetition time (TR)=1500 ms, echo time (TE)=120 ms and acceleration factor=3.
DTI data were acquired using a single-shot echo-planar imaging sequence. The acquisition was cardiac gated with a voxel size=1×1×5 mm3, FOV=128×128 mm2, TR=700 ms, TE=96 ms, acceleration factor=2, b value=1000 s/mm2, 64 diffusion encoding directions and four averages.
3D gradient echo images were used …
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