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arxiv: 2002.03419 · v2 · pith:OLWQ3V5Vnew · submitted 2020-02-09 · 🧬 q-bio.PE · stat.AP

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

Razvan V. Marinescu , Neil P. Oxtoby , Alexandra L. Young , Esther E. Bron , Arthur W. Toga , Michael W. Weiner , Frederik Barkhof , Nick C. Fox
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Arman Eshaghi Tina Toni Marcin Salaterski Veronika Lunina Manon Ansart Stanley Durrleman Pascal Lu Samuel Iddi Dan Li Wesley K. Thompson Michael C. Donohue Aviv Nahon Yarden Levy Dan Halbersberg Mariya Cohen Huiling Liao Tengfei Li Kaixian Yu Hongtu Zhu Jose G. Tamez-Pena Aya Ismail Timothy Wood Hector Corrada Bravo Minh Nguyen Nanbo Sun Jiashi Feng B.T. Thomas Yeo Gang Chen Ke Qi Shiyang Chen Deqiang Qiu Ionut Buciuman Alex Kelner Raluca Pop Denisa Rimocea Mostafa M. Ghazi Mads Nielsen Sebastien Ourselin Lauge Sorensen Vikram Venkatraghavan Keli Liu Christina Rabe Paul Manser Steven M. Hill James Howlett Zhiyue Huang Steven Kiddle Sach Mukherjee Anais Rouanet Bernd Taschler Brian D. M. Tom Simon R. White Noel Faux Suman Sedai Javier de Velasco Oriol Edgar E. V. Clemente Karol Estrada Leon Aksman Andre Altmann Cynthia M. Stonnington Yalin Wang Jianfeng Wu Vivek Devadas Clementine Fourrier Lars Lau Raket Aristeidis Sotiras Guray Erus Jimit Doshi Christos Davatzikos Jacob Vogel Andrew Doyle Angela Tam Alex Diaz-Papkovich Emmanuel Jammeh Igor Koval Paul Moore Terry J. Lyons John Gallacher Jussi Tohka Robert Ciszek Bruno Jedynak Kruti Pandya Murat Bilgel William Engels Joseph Cole Polina Golland Stefan Klein Daniel C. Alexander
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keywords predictiondiseasealzheimerclinicalchallengediagnosisvolumealgorithms
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We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of biomarkers. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

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