Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

Identification of the Potent Phytoestrogen Glycinol in Elicited Soybean (Glycine max)

The primary induced isoflavones in soybean, the glyceollins, have been shown to be potent estrogen antagonists in vitro and in vivo. The discovery of the glyceollins’ ability to inhibit cancer cell proliferation has led to the analysis of estrogenic activities of other induced isoflavones. In this study, we investigated a novel isoflavone, glycinol, a precursor to glyceollin that is produced in elicited soy. Sensitive and specific in vitro bioassays were used to determine that glycinol exhibits potent estrogenic activity. Estrogen-based reporter assays were performed, and glycinol displayed a marked estrogenic effect on estrogen receptor (ER) signaling between 1 and 10 μm, which correlated with comparable colony formation of MCF-7 cells at 10 μm. Glycinol also induced the expression of estrogen-responsive genes (progesterone receptor and stromal-cell-derived factor-1). Competitive binding assays revealed a high affinity of glycinol for both ERα (ΙC50 = 13.8 nm) and ERβ (ΙC50 = 9.1 nm). In addition, ligand receptor modeling (docking) studies were performed and glycinol was shown to bind similarly to both ERα and ERβ. Taken together, these results suggest for the first time that glycinol is estrogenic and may represent an important component of the health effects of soy-based foods