Generalization of tumor identification algorithms.

The morphological features that pathologists use to differentiate neoplasms from normal tissue are nonspecific to tissue type. For example, if given a Ki67 stained biopsy of neuroendocrine or breast tumor, a pathologist would be able to correctly identify morphologically abnormal cells in both samples but may struggle to identify the origin of both samples. This is also true for other pathological malignancies such as carcinomas, sarcomas, and leukemia. This implies that computer algorithms trained to recognize tumor from one site should be able to identify tumor from other sites with similar tumor subtypes. Here, we present the results of an experiment that supports this hypothesis. We train a deep learning system to distinguish tumor from non-tumor regions in Ki67 stained neuroendocrine tumor digital slides. Then, we test the same, unmodified, deep learning model to distinguish breast cancer from non-cancer regions. When applied to a sample of 96 high power fields, our system achieved a cumulative pixel-wise accuracy of 86% across these high-power fields. To our knowledge, our results are the first to formally demonstrate generalized segmentation of tumors from different sites of origin through image analysis. This paradigm has the potential to help with the design of tumor identification algorithms as well as the composition of the datasets they draw from.