The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Creators: name: Marinescu, Razvan V.
name: Oxtoby, Neil P.
name: Young, Alexandra L.
name: Bron, Esther E.
name: Toga, Arthur W.
name: Weiner, Michael W.
name: Barkhof, Frederik
name: Fox, Nick C.
name: Eshaghi, Arman
name: Toni, Tina
name: Salaterski, Marcin
name: Lunina, Veronika
name: Ansart, Manon
name: Durrleman, Stanley
name: Lu, Pascal
name: Iddi, Samuel
name: Li, Dan
name: Thompson, Wesley K.
name: Donohue, Michael C.
name: Nahon, Aviv
name: Levy, Yarden
name: Halbersberg, Dan
name: Cohen, Mariya
name: Liao, Huiling
name: Li, Tengfei
name: Yu, Kaixian
name: Zhu, Hongtu
name: Tamez-Pena, Jose G.
name: Ismail, Aya
name: Wood, Timothy
name: Bravo, Hector Corrada
name: Nguyen, Minh
name: Sun, Nanbo
name: Feng, Jiashi
name: Yeo, B. T. Thomas
name: Chen, Gang
name: Qi, Ke
name: Chen, Shiyang
name: Qiu, Deqiang
name: Buciuman, Ionut
name: Kelner, Alex
name: Pop, Raluca
name: Rimocea, Denisa
name: Ghazi, Mostafa M.
name: Nielsen, Mads
name: Ourselin, Sebastien
name: Sorensen, Lauge
name: Venkatraghavan, Vikram
name: Liu, Keli
name: Rabe, Christina
name: Manser, Paul
name: Hill, Steven M.
name: Howlett, James
name: Huang, Zhiyue
name: Kiddle, Steven
name: Mukherjee, Sach
name: Rouanet, Anais
name: Taschler, Bernd
name: Tom, Brian D. M.
name: White, Simon R.
name: Faux, Noel
name: Sedai, Suman
name: Oriol, Javier de Velasco
name: Clemente, Edgar E. V.
name: Estrada, Karol
name: Aksman, Leon
name: Altmann, Andre
name: Stonnington, Cynthia M.
name: Wang, Yalin
name: Wu, Jianfeng
name: Devadas, Vivek
name: Fourrier, Clementine
name: Raket, Lars Lau
name: Sotiras, Aristeidis
name: Erus, Guray
name: Doshi, Jimit
name: Davatzikos, Christos
name: Vogel, Jacob
name: Doyle, Andrew
name: Tam, Angela
name: Diaz-Papkovich, Alex
name: Jammeh, Emmanuel
name: Koval, Igor
name: Moore, Paul
name: Lyons, Terry J.
name: Gallacher, John
name: Tohka, Jussi
name: Ciszek, Robert
name: Jedynak, Bruno
name: Pandya, Kruti
name: Bilgel, Murat
name: Engels, William
name: Cole, Joseph
name: Golland, Polina
name: Klein, Stefan
name: Alexander, Daniel C.
Publisher: name: arXiv
Year: 2020
Subjects: subject: Populations and Evolution (q-bio.PE)
subject: Applications (stat.AP)
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: 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.
description: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures