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Publication Detail
TADPOLE challenge: Accurate alzheimer’s disease prediction through crowdsourced forecasting of future data
  • Publication Type:
    Conference
  • Authors:
    Marinescu RV, Oxtoby NP, Young AL, Bron EE, Toga AW, Weiner MW, Barkhof F, Fox NC, Golland P, Klein S, Alexander DC
  • Publisher:
    Springer
  • Publication date:
    10/10/2019
  • Pagination:
    1, 10
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    11843 LNCS
  • ISBN-13:
    9783030322809
  • Status:
    Published
  • Name of conference:
    Predictive Intelligence in Medicine Second International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019
  • Conference place:
    Shenzhen, China
  • Conference start date:
    13/10/2019
  • Conference finish date:
    13/10/2019
  • Print ISSN:
    0302-9743
Abstract
© Springer Nature Switzerland AG 2019. The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer’s disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, Alzheimer’s Disease Assessment Scale Cognitive Subdomain (ADAS-Cog 13), and total volume of the ventricles – which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants’ predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer’s disease prediction and for aiding patient stratification in clinical trials. The submission system remains open via the website: https://tadpole.grand-challenge.org/.
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