The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
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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, BT 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
Huang, Zhiyue
Kiddle, Steven
Mukherjee, Sach
Rouanet, Anais
Taschler, Bernd
Tom, Brian DM
White, Simon R
Faux, Noel
Sedai, Suman
Oriol, Javier de Velasco
Clemente, Edgar EV
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
Publication Date
2020-02-09Journal Title
Machine Learning for Biomedical Imaging (MELBA), Dec 2021
ISSN
2041-1723
Publisher
Nature Research
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., et al. (2020). The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
Challenge: Results after 1 Year Follow-up. Machine Learning for Biomedical Imaging (MELBA), Dec 2021 https://doi.org/10.1038/s41467-019-09799-2
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.
Keywords
q-bio.PE, q-bio.PE, stat.AP
Relationships
Is supplemented by: https://doi.org/10.1038/s41467-019-09799-2
Sponsorship
MRC (unknown)
Embargo Lift Date
2025-01-31
Identifiers
External DOI: https://doi.org/10.1038/s41467-019-09799-2
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333479
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