Automatic Tumor Grading on Colorectal Cancer Whole-Slide Images: Semi-Quantitative Gland Formation Percentage and New Indicator Exploration - Chen, Shenlun; Zhang, Meng; Wang, Jiazhou; Xu, Midie; Hu, Weigang; Wee, Leonard; Dekker, Andre; Sheng, Weiqi; Zhang, Zhen (Frontiers in Oncology, 2022)
Tumor grading is an essential factor for cancer staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). We developed a fully automated approach for stratifying colorectal cancer (CRC) patients’ risk of mortality directly from histology WSI relating to gland formation. A tissue classifier was trained to categorize regions on WSI as gland, stroma, immune cells, background and other tissues. A gland formation classifier was trained on expert annotations to categorize regions as different degrees of tumor gland formation versus normal tissue. The glandular formation density can thus be estimated using the aforementioned tissue categorization and gland formation information. This estimation was called a semi-quantitative gland formation ratio (SGFR), which was used as a prognostic factor in survival analysis. We evaluated gland formation percentage and validated by comparing against the WHO cut-off point. Survival data and gland formation maps were then used to train a spatial pyramid pooling survival network (SPPSN) as a deep survival model. We compared the survival prediction performance of estimated gland formation percentage and the SSPN deep survival grade, and found the deep survival grade had improved discrimination. A univariable Cox model for survival yielded moderate discrimination with SGFR (c-index 0.62) and deep survival grade (c-index 0.64) in an independent institutional test set. Deep survival grade also showed better discrimination performance in multivariable Cox regression. The deep survival grade significantly increased the c-index of baseline Cox model but the inclusion of SGFR was unable to improve the Cox model.