Development of an Automated MRI-Based Diagnostic Protocol for Amyotrophic Lateral Sclerosis Using Disease-Specific Pathognomonic Features: A Quantitative Disease-State Classification Study

Abstract

BackgroundDespite significant advances in quantitative neuroimaging, the diagnosis of ALS remains clinical and MRI-based biomarkers are not currently used to aid the diagnosis. The objective of this study is to develop a robust, disease-specific, multimodal classification protocol and validate its diagnostic accuracy in independent, early-stage and follow-up data sets.Methods147 participants (81 ALS patients and 66 healthy controls) were divided into a training sample and a validation sample. Patients in the validation sample underwent follow-up imaging longitudinally. After removing age-related variability, indices of grey and white matter integrity in ALS-specific pathognomonic brain regions were included in a cross-validated binary logistic regression model to determine the probability of individual scans indicating ALS. The following anatomical regions were assessed for diagnostic classification: average grey matter density of the left and right precentral gyrus, the average fractional anisotropy and radial diffusivity of the left and right superior corona radiata, inferior corona radiata, internal capsule, mesencephalic crus of the cerebral peduncles, pontine segment of the corticospinal tract, and the average diffusivity values of the genu, corpus and splenium of the corpus callosum.ResultsUsing a 50% probability cut-off value of suffering from ALS, the model was able to discriminate ALS patients and HC with good sensitivity (80.0%) and moderate accuracy (70.0%) in the training sample and superior sensitivity (85.7%) and accuracy (78.4%) in the independent validation sample.ConclusionsThis diagnostic classification study endeavours to advance ALS biomarker research towards pragmatic clinical applications by providing an approach of automated individual-data interpretation based on group-level observations.

Multimodal structural MRI in the diagnosis of motor neuron diseases

“…Our MRI approach combining cortical thickness and microstructural integrity of MND-specific anatomical regions achieved high sensitivity and moderate to high specificity in distinguishing ALS and PUMN individual cases from healthy controls. Findings in ALS are in keeping with a recent classification study using a cross-validation binary logistic regression model ( Schuster et al, 2016 ), although the specificity of our approach was higher. This can be related to the different methods applied to obtain MRI features, particularly to the greater reliability of cortical thickness measures relative to GM density values in assessing the motor cortex involvement in MND ( Chiò et al, 2014 , Menke et al, 2017 ).…”

“…This disappointing finding may result from the heterogeneity of both the methodology and patient populations but it may also suggest that a single MR technique lacks sufficient diagnostic power. A multimodal neuroimaging approach may be a strategy to improve accuracy ( Douaud et al, 2011 , Foerster et al, 2014 , Schuster et al, 2016 ). A model incorporating the cortical thickness of the precentral gyrus and DT MRI measures of the CST and CC was able to discriminate ALS and healthy controls with good sensitivity (85.7%) and accuracy (78.4%) ( Schuster et al, 2016 ).…”

“…A multimodal neuroimaging approach may be a strategy to improve accuracy ( Douaud et al, 2011 , Foerster et al, 2014 , Schuster et al, 2016 ). A model incorporating the cortical thickness of the precentral gyrus and DT MRI measures of the CST and CC was able to discriminate ALS and healthy controls with good sensitivity (85.7%) and accuracy (78.4%) ( Schuster et al, 2016 ). However, almost all previous MRI studies have recruited healthy controls as reference group to identify ALS-specific abnormalities, while a comparison with ALS-mimic disorders is mandatory to test the specificity of these markers.…”

Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

“…Furthermore, we demonstrated a consistency between the microscopic pathological staging in ALS and its representation on the macroscopic level of brain regions. This overlap may potentially facilitate tracking disease spread and categorizing patients into disease stages in vivo based on MRI . This result brings the 2 fields of pathological staging and in vivo MRI staging closer together as a crucial step toward combining all available patient information to understand ALS disease progression.…”

Corticospinal tract degeneration in ALS unmasked in T1‐weighted images using texture analysis

“…A study combining DTI and MR spectroscopy reported 93% sensitivity and 85% specificity in discriminating patients from controls (Foerster et al, ). In another multimodal study, an 86% sensitivity and 78% accuracy was achieved in discriminating patients and controls using DTI metrics and gray matter densities from T1W images (Schuster, Hardiman, & Bede, ). Therefore, although studies support the diagnostic utility of multimodal MRI in ALS, texture analysis on T1W images alone has high diagnostic performance and with greater clinical feasibility as it does not require advanced MRI sequences with long acquisition times that may not be available on all scanners.…”