Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Babak Ehteshami Bejnordi; Jimmy Linz; Ben Glass; Maeve Mullooly; Gretchen L Gierach; Mark E Sherman; Nico Karssemeijer; Jeroen van der Laak; Andrew H Beck

arXiv · 1702.05803

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

Babak Ehteshami Bejnordi·Jimmy Linz·Ben Glass·Maeve Mullooly·Gretchen L Gierach·Mark E Sherman·Nico Karssemeijer·Jeroen van der Laak·Andrew H Beck

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Abstract

Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor-associated stroma as a diagnostic biomarker.

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Babak Ehteshami Bejnordi, Jimmy Linz, Ben Glass, Maeve Mullooly, Gretchen L Gierach, Mark E Sherman, Nico Karssemeijer, Jeroen van der Laak, Andrew H Beck. 2017-02-19. Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images. https://arxiv.org/abs/1702.05803

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