Efficient and cost-effective breast cancer risk stratification using whole slide histopathology images

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Efficient and cost-effective breast cancer risk stratification using whole-slide histopathology images Breast cancer prognosis depends highly on receptor status, as optimal treatment depends on the presence or absence of overexpression of estrogen, progesterone, or HER-2/neu receptors. To prevent over-treating patients with chemotherapy, it is crucial to quantify the risk of recurrence for estrogen receptor (ER) positive (ER+), HER2 negative (HER2-) breast cancer. A common assessment method to meet this need is the Oncotype DX (ODX) Recurrence Score. Unfortunately, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. As an alternative, we propose estimating the ODX recurrence score using routine, ubiquitous, and inexpensive hematoxylin and eosin (H&E) staining of biopsies. There are other efforts to predict ODX recurrence risk from H&E. These automated methods detect histological primitives (e.g., nuclei) often in specific, also automatically detected, anatomical regions (e.g., ducts, tubules, lumen, epithelium, and stroma). Classification is performed into two or three risk categories, often collapsing two categories into one. The performance of these models is promising but still modest. One way to improve the performance of the models is to train on larger datasets; however, annotating larger datasets is challenging. Here, we propose an automated method to predict ODX recurrence risk without annotations. If successful, this method would have a wide range of applications, including but not limited to the availability of an inexpensive, web-based tool to predict ODX in developing countries or rural areas with internet access where standard Oncotype Dx assay would be cost-prohibitive or take too long to obtain. Furthermore, our method would find use in clinical research where valuable tumor tissue could be saved by obtaining correlative research data based on standard H&E-stained slides.