Deep-learning powered decision support for breast cancer grading
Breast cancer is a heterogeneous disease with varied morphological appearances, and response to therapy. Current routine clinical management relies on the availability of robust clinical and pathological prognostic and predictive factors, one of the best being histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. The Nottingham Grading System (NGS), is the grading system recommended by various professional bodies internationally namely the World Health Organization, the American Joint Committee on Cancer, the European Union, and the Royal College of Pathologists.
The NGS places an emphasis on the percentage of tubule formation on the tumor, the mitotic count and the degree of nuclear pleomorphism, arriving at a ‘combined histological grade. These characteristics are determined by visual examination of tumor sections by expert histopathologists over a microscope. This practice presents two problems. The first one is that the histological grade is subjectively determined, where the main sources of variance are the histopathologist’s experience and fatigue levels as well as the fact that the tissue section is only partly examined. The subjectivity problem has been demonstrated by numerous cross-center studies where the overall average cross-expert correlation was found to be approximately 50%. To solve this problem we use deep-learning in a two-way approach. In the first we train our models to efficiently detect cell-nuclei, and then perform accurate mitotic counting and pleomorphism characterisation. In the second, the models are trained to accurately quantify the presence of tubules throughput the whole tumor tissue area.