Accurate personalized survival prediction for amyotrophic lateral sclerosis patients.

Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment.

Characterization of white matter alterations using diffusion kurtosis imaging in patients with amyotrophic lateral sclerosis.

BACKGROUND: To evaluate the degeneration of the corticospinal tract (CST) and corpus callosum (CC) in patients with motor neuron disease and upper motor neuron (UMN) dysfunction using diffusion kurtosis imaging (DKI). METHODS: Twenty-seven patients and 33 healthy controls underwent magnetic resonance imaging along with clinical and neuropsychological testing. Tractography of diffusion tensor images was performed to extract tracts of the bilateral CST and CC. Group mean differences both across the entire averaged tract and along each tract were assessed, including correlations between diffusion metrics and clinical measures. Tract-based spatial statistics (TBSS) was performed to evaluate the spatial distribution of whole-brain microstructural abnormalities in patients. RESULTS: In comparison to controls, patients had significantly higher mean and radial diffusivity and lower fractional anisotropy (FA), kurtosis anisotropy, mean kurtosis (MK), and radial kurtosis (RK) in the CST and CC (p < .017). Along-the-tract analysis revealed changes concentrated in the posterior limb of the internal capsule, corona radiata, and primary motor cortex (false-discovery rate p < .05). FA of the left CST correlated with disease progression rate, whereas MK of the bilateral CST correlated with UMN burden (p < .01). TBSS results corroborated along-tract analysis findings and additionally revealed reduced RK and MK in the fornix, where diffusion tensor imaging (DTI) changes were absent. CONCLUSION: DKI abnormalities in the CST and CC are present in patients with UMN dysfunction, potentially revealing complementary information to DTI regarding the pathology and microstructural alterations occurring in such patients. DKI shows promise as a potential in vivo biomarker for cerebral degeneration in amyotrophic lateral sclerosis.

Severity of in vivo corticospinal tract degeneration is associated with survival in amyotrophic lateral sclerosis: a longitudinal, multicohort study.

BACKGROUND AND PURPOSE: This study sought to evaluate the relationship of progressive corticospinal tract (CST) degeneration with survival in patients with amyotrophic lateral sclerosis (ALS). METHODS: Forty-one ALS patients and 42 healthy controls were prospectively recruited from the Canadian ALS Neuroimaging Consortium. Magnetic resonance imaging scanning and clinical evaluations were performed on participants at three serial visits with 4-month intervals. Texture analysis was performed on T1-weighted magnetic resonance imaging scans and the texture feature 'autocorrelation' was quantified. Whole-brain group-level comparisons were performed between patient subgroups. Linear mixed models were used to evaluate longitudinal progression. Region-of-interest and 3D voxel-wise Cox proportional-hazards regression models were constructed for survival prediction. For all survival analyses, a second independent cohort was used for model validation. RESULTS: Autocorrelation of the bilateral CST was increased at baseline and progressively increased over time at a faster rate in ALS short survivors. Cox proportional-hazards regression analyses revealed autocorrelation of the CST as a significant predictor of survival at 5 years follow-up (hazard ratio 1.28, p = 0.005). Similarly, voxel-wise whole-brain survival analyses revealed that increased autocorrelation of the CST was associated with shorter survival. ALS patients stratified by median autocorrelation in the CST had significantly different survival times using the Kaplan-Meier curve and log-rank tests (χ2  = 7.402, p = 0.007). CONCLUSIONS: Severity of cerebral degeneration is associated with survival in ALS. CST degeneration progresses faster in subgroups of patients with shorter survival. Neuroimaging holds promise as a tool to improve patient management and facilitation of clinical trials.