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Background and Objectives: Reproducibility is a major challenge in developingmachine learning (ML)-based solutions in computational pathology (CompPath).The NCI Imaging Data Commons (IDC) provides >120 cancer image collectionsaccording to the FAIR principles and is designed to be used with cloud MLservices. Here, we explore its potential to facilitate reproducibility inCompPath research.  Methods: Using the IDC, we implemented two experiments in which arepresentative ML-based method for classifying lung tumor tissue was trainedand/or evaluated on different datasets. To assess reproducibility, theexperiments were run multiple times with separate but identically configuredinstances of common ML services.  Results: The AUC values of different runs of the same experiment weregenerally consistent. However, we observed small variations in AUC values of upto 0.045, indicating a practical limit to reproducibility.  Conclusions: We conclude that the IDC facilitates approaching thereproducibility limit of CompPath research (i) by enabling researchers to reuseexactly the same datasets and (ii) by integrating with cloud ML services sothat experiments can be run in identically configured computing environments.

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology