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

Authors :
Marinescu, Razvan V.
Oxtoby, Neil P.
Young, Alexandra L.
Bron, Esther E.
Toga, Arthur W.
Weiner, Michael W.
Barkhof, Frederik
Fox, Nick C.
Eshaghi, Arman
Toni, Tina
Salaterski, Marcin
Lunina, Veronika
Ansart, Manon
Durrleman, Stanley
Lu, Pascal
Iddi, Samuel
Li, Dan
Thompson, Wesley K.
Donohue, Michael C.
Nahon, Aviv
Levy, Yarden
Halbersberg, Dan
Cohen, Mariya
Liao, Huiling
Li, Tengfei
Yu, Kaixian
Zhu, Hongtu
Tamez-Pena, Jose G.
Ismail, Aya
Wood, Timothy
Bravo, Hector Corrada
Nguyen, Minh
Sun, Nanbo
Feng, Jiashi
Yeo, B. T. Thomas
Chen, Gang
Qi, Ke
Chen, Shiyang
Qiu, Deqiang
Buciuman, Ionut
Kelner, Alex
Pop, Raluca
Rimocea, Denisa
Ghazi, Mostafa M.
Nielsen, Mads
Ourselin, Sebastien
Sorensen, Lauge
Venkatraghavan, Vikram
Liu, Keli
Rabe, Christina
Manser, Paul
Hill, Steven M.
Howlett, James
Huang, Zhiyue
Kiddle, Steven
Mukherjee, Sach
Rouanet, Anais
Taschler, Bernd
Tom, Brian D. M.
White, Simon R.
Faux, Noel
Sedai, Suman
Oriol, Javier de Velasco
Clemente, Edgar E. V.
Estrada, Karol
Aksman, Leon
Altmann, Andre
Stonnington, Cynthia M.
Wang, Yalin
Wu, Jianfeng
Devadas, Vivek
Fourrier, Clementine
Raket, Lars Lau
Sotiras, Aristeidis
Erus, Guray
Doshi, Jimit
Davatzikos, Christos
Vogel, Jacob
Doyle, Andrew
Tam, Angela
Diaz-Papkovich, Alex
Jammeh, Emmanuel
Koval, Igor
Moore, Paul
Lyons, Terry J.
Gallacher, John
Tohka, Jussi
Ciszek, Robert
Jedynak, Bruno
Pandya, Kruti
Bilgel, Murat
Engels, William
Cole, Joseph
Golland, Polina
Klein, Stefan
Alexander, Daniel C.
Source :
Machine Learning for Biomedical Imaging (MELBA), Dec 2021
Publication Year :
2020

Abstract

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.<br />Comment: Presents final results of the TADPOLE competition. 60 pages, 7 tables, 14 figures

Details

Database :
arXiv
Journal :
Machine Learning for Biomedical Imaging (MELBA), Dec 2021
Publication Type :
Report
Accession number :
edsarx.2002.03419
Document Type :
Working Paper