Predicting MCI-to-Alzheimer’s Conversion: A Systematic Benchmark of Classical, Machine Learning, and Deep Survival Models

Background/Objectives: Predicting time-to-conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is a critical clinical challenge. Most survival analyses rely on static baseline variables, ignoring the temporal dynamics of serial neuroimaging. We benchmark seven survival model families using a novel temporal MRI descriptor pipeline on ADNI.

Methods

A cohort of 661 MCI patients (250 conversion events, 37.8%, median follow-up 42.0 months) was constructed from ADNI. FreeSurfer volumetrics of six brain regions were summarised into three time-based descriptors each (atrophy rate, trajectory variability σ, and time-weighted cumulative mean μ) under strict anti-leakage constraints, yielding 18 neuroimaging features which, combined with six clinical variables (age, sex, education, MMSE, ADAS-13, and APOE-ε4), form a 24-dimensional covariate vector. Cox PH, Weibull AFT, Exponential, RSF, GBST, DeepSurv, and DeepHit were evaluated under stratified 5-fold cross-validation and assessed on an independent OASIS-3 cohort (n=78) as an exploratory analysis.