Survival prediction in Amyotrophic lateral sclerosis based on MRI measures and clinical characteristics

Background: Amyotrophic lateral sclerosis (ALS) a highly heterogeneous neurodegenerative condition. Accurate diagnostic, monitoring and prognostic biomarkers are urgently needed both for individualised patient care and clinical trials. A multimodal magnetic resonance imaging study is presented, where MRI measures of ALS-associated brain regions are utilised to predict 18-month survival.

Methods: A total of 60 ALS patients and 69 healthy controls were included in this study. 20% of the patient sample was utilised as an independent validation sample. Surface-based morphometry and diffusion tensor white matter parameters were used to identify anatomical patterns of neurodegeneration in 80% of the patient sample compared to healthy controls. Binary logistic ridge regressions were carried out to predict 18-month survival based on clinical measures alone, MRI features, and a combination of clinical and MRI data. Clinical indices included age at symptoms onset, site of disease onset, diagnostic delay from first symptom to diagnosis, and physical disability (ALSFRS-r). MRI features included the average cortical thickness of the precentral and paracentral gyri, the average fractional anisotropy, radial-, medial-, and axial diffusivity of the superior and inferior corona radiata, internal capsule, cerebral peduncles and the genu, body and splenium of the corpus callosum.

Results: Clinical data alone had a survival prediction accuracy of 66.67%, with 62.50% sensitivity and 70.84% specificity. MRI data alone resulted in a prediction accuracy of 77.08%, with 79.16% sensitivity and 75% specificity. The combination of clinical and MRI measures led to a survival prediction accuracy of 79.17%, with 75% sensitivity and 83.34% specificity.

Conclusion: Quantitative MRI measures of ALS-specific brain regions enhance survival prediction in ALS and should be incorporated in future clinical trial designs.