Ray-Aided Quadruple Affiliation Network for Calculating Tumor-Stroma Ratios in Breast Cancers

Kunping Yang, Linying Chen, Xi Zheng, Xuanping Li, Junhui Lan, Yi Wu, Julia Y. S. Tsang, Gary M. Tse

Abstract: Tumor-stroma ratio (TSR), which is the area ratio between two components within tumor beds, namely tumor cells and tumor stroma, has been suggested as a promising prognostic feature in breast cancers. However, due to imperfect datasets, and the similarity between tumor stroma and non-tumor stroma, previous algorithms struggle to delineate tumor beds, especially those of histomorphologies with a fibrotic focus. To overcome these limitations, we propose a novel ray-aided quadruple affiliation network (RQA-Net) for calculating TSRs in breast cancers. RQA-Net uses quadruple branches to segment tumor cells and tumor beds simultaneously, where a crisscross task subtraction module (CTS-Module) is designed to locate tumor stroma, grounded on its affiliation relationships with tumor beds. Moreover, we propose an affiliation loss (Aff-Loss) to force identified tumor beds to incorporate tumor cells to enhance their affiliation relationships. Furthermore, we propose a ray-based hypothesis testing (RH-Testing) to obtain line segments from ray equations in tumor beds that can decorate identified tumor beds by overlapping. In summary, RQA-Net precisely predicts tumor cells and tumor beds, and thus supports the calculation of TSRs. We also create a cancerous dataset (CrD-Set) containing 100 slides with an average resolution of $50,000\times 50,000$ pixels from real breast cancer cases, which is the first dataset with pixel-wise tumor bed annotations. Experimental results on existing datasets and CrD-Set demonstrate that compared with previous methods, RQA-Net better calculates breast cancer TSRs by precisely identifying tumor cells and tumor beds. The created CrD-Set and codes in this work will be available online at https://github.com/Kunpingyang1992/Breast-Cancer-TSR-Calculation