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

Abstract

BackgroundAmyotrophic 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.MethodsA 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.ResultsClinical 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.ConclusionQuantitative MRI measures of ALS-specific brain regions enhance survival prediction in ALS and should be incorporated in future clinical trial designs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-017-0854-x) contains supplementary material, which is available to authorized users.

Survival prediction models in motor neuron disease

“…Although the clinical model alone obtained a good prognostic accuracy across MND phenotypes, the combined clinical and MRI model yielded a significant improvement, reaching an AUC of 0.89. The presence of CST FA amongst survival predictors is consistent with previous studies in ALS patients and supports the translation of this measure of UMN damage to clinical practice not only as a diagnostic but also as a prognostic tool. Measures of GM and WM damage of extra‐motor fronto‐temporal regions also provided increased prognostic accuracy.…”

“…Cox regression analyses have identified FA of the CST and the N ‐acetylaspartate‐to‐choline ratio of the PMC as survival predictors. Recently, the combination of clinical features with volumetric and DT MRI data has been tested as a prognostic method to classify ALS patients as short and long survivors by means of deep learning , demonstrating a significant increase in prediction accuracy (range 79%–84%) compared with clinical characteristics alone (66%–69%). This approach allows a categorical classification into a priori defined survival classes, but does not estimate the expected survival time at a single‐patient level.…”

Connectome‐Based Propagation Model in Amyotrophic Lateral Sclerosis

“…Using the aggregation levels of the random walker as an additional layer for a previously published machine learning algorithm to predict survival increased classification accuracy significantly . Machine learning techniques have been increasingly applied to tackle the complexity and heterogeneity of ALS . Accurate prognostication enables patient stratification in clinical trials and may improve quality of life for patients and caregivers .…”

Patterned functional network disruption in amyotrophic lateral sclerosis

“…Structural T1-weighted MRI data were acquired on a 3 T Philips Achieva system with a gradient strength of 80 mT/m and slew rate of 200 T/m/s using an eight-channel receive-only head coil. They were obtained using a three-dimensional inversion recovery prepared spoiled gradient recalled echo sequence with field-of-view = 256 × 256 × 160 mm 3 , spatial resolution = 1 mm 3 (Schuster et al, 2016;Schuster, Hardiman, & Bede, 2017). MRI scans were individually screened for the presence of vascular alterations on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences and patients with co-morbid vascular white matter lesions were not included (Bede, Iyer, Finegan, Omer, & Hardiman, 2017).…”