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We present the findings of "The Alzheimer's Disease Prediction OfLongitudinal Evolution" (TADPOLE) Challenge, which compared the performance of92 algorithms from 33 international teams at predicting the future trajectoryof 219 individuals at risk of Alzheimer's disease. Challenge participants wererequired to make a prediction, for each month of a 5-year future time period,of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment ScaleCognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. Themethods used by challenge participants included multivariate linear regression,machine learning methods such as support vector machines and deep neuralnetworks, as well as disease progression models. No single submission was bestat predicting all three outcomes. For clinical diagnosis and ventricle volumeprediction, the best algorithms strongly outperform simple baselines inpredictive ability. However, for ADAS-Cog13 no single submitted predictionmethod was significantly better than random guesswork. Two ensemble methodsbased on taking the mean and median over all predictions, obtained top scoreson almost all tasks. Better than average performance at diagnosis predictionwas generally associated with the additional inclusion of features fromcerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On theother hand, better performance at ventricle volume prediction was associatedwith inclusion of summary statistics, such as the slope or maxima/minima ofbiomarkers. TADPOLE's unique results suggest that current prediction algorithmsprovide sufficient accuracy to exploit biomarkers related to clinical diagnosisand ventricle volume, for cohort refinement in clinical trials for Alzheimer'sdisease. However, results call into question the usage of cognitive test scoresfor patient selection and as a primary endpoint in clinical trials.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up