Marinescu Răzvan V, Oxtoby Neil P, Young Alexandra L, Bron Esther E, Toga Arthur W, Weiner Michael W, Barkhof Frederik, Fox Nick C, Golland Polina, Klein Stefan, Alexander Daniel C
Computer Science and Artificial Intelligence Laboratory, MIT, USA.
Centre for Medical Image Computing, University College London, UK.
Predict Intell Med. 2019 Oct;11843:1-10. doi: 10.1007/978-3-030-32281-6_1. Epub 2019 Oct 10.
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 ), 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 ( ), 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/.
阿尔茨海默病纵向演变预测（TADPOLE）挑战赛比较了算法在预测阿尔茨海默病风险个体未来演变方面的性能。TADPOLE挑战赛的参与者使用来自阿尔茨海默病神经影像学倡议（ADNI）研究的历史数据来训练他们的模型和算法。然后，参与者需要对ADNI-3延期参与者的三个关键结果进行预测：临床诊断、阿尔茨海默病评估量表认知子域（ADAS-Cog 13）和脑室总体积——然后将这些预测结果与未来的测量结果进行比较。该挑战赛的优点在于，预测时测试数据并不存在（是之后获取的），并且它通过识别快速进展者来关注临床试验中具有挑战性的队列选择问题。TADPOLE的提交阶段于2017年11月15日截止；从那时起，直到2019年4月，从219名受试者那里获取了数据，进行了223次临床访视和150次磁共振成像（MRI）扫描，这些数据用于评估参与者的预测。33个团队参与，共提交了92份结果。没有一份提交结果在预测所有三个结果方面都是最佳的。对于诊断预测，基于梯度提升的最佳预测结果（团队Frog）在多分类受试者工作特征曲线下面积（MAUC）为0.931，而对于脑室预测，基于疾病进展建模和样条回归的最佳预测结果（团队 ）的平均绝对误差为总颅内体积（ICV）的0.41%。对于ADAS-Cog 13，没有任何预测结果比在提交截止日期前提供给参与者的基准混合效应模型（ ）好很多。进一步的分析有助于了解哪些输入特征和算法最适合阿尔茨海默病预测以及辅助临床试验中的患者分层。提交系统仍通过网站https://tadpole.grand-challenge.org/开放。
