Abstract
The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.
Highlights
Diagnostic delay in neurodegenerative conditions has a considerable literature
Recent evidence suggests that considerable presymptomatic disease burden can be readily detected long before symptom manifestation [8,9,10,11]
The distinction between ‘amyotrophic lateral sclerosis (ALS)’ and ‘healthy’ is seldom challenging; instead, the dilemma is typically whether subtle clinical changes represent incipient ALS or rather, the harbinger of an alternative neurodegenerative condition. Another common shortcoming of classification studies is the a priori selection of anatomical regions, often referred to as ‘regions of interest’ (ROIs) which are known to be affected in ALS, rather than performing formal feature selection analyses or ranking variables based on their discriminatory potential
Summary
Diagnostic delay in neurodegenerative conditions has a considerable literature. In ALS, the average interval from symptom onset to definite diagnosis is around 12 months [1, 2]. The distinction between ‘ALS’ and ‘healthy’ is seldom challenging; instead, the dilemma is typically whether subtle clinical changes represent incipient ALS or rather, the harbinger of an alternative neurodegenerative condition. Another common shortcoming of classification studies is the a priori selection of anatomical regions, often referred to as ‘regions of interest’ (ROIs) which are known to be affected in ALS, rather than performing formal feature selection analyses or ranking variables based on their discriminatory potential
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