Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we showed the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage.

翻译：整合来自多个站点的公开MRI数据可构建大规模受试者群体，提升统计效力，并促进基于机器学习技术的数据复用。多中心数据的协调对于降低非生物因素导致的数据变异混杂效应至关重要。然而，若在机器学习之前对整个数据集进行协调，将导致数据泄露——因为训练集之外的信息可能影响模型构建，并可能虚增性能评估结果。本文提出：1) 数据协调有效性的测量方法；2) 协调转换器，即ComBat协调方法的实现方案，可将其封装于机器学习流水线的预处理步骤中，从而避免数据泄露。我们采用来自36个站点、1740名健康受试者的脑部T1加权MRI数据测试了这些工具。结果表明，协调后站点效应被消除或减弱，同时我们通过MRI数据预测个体年龄时展示了数据泄露效应，证实将协调转换器引入机器学习流水线可有效规避数据泄露。