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Context:Histopathological characterization of colorectal polyps is critical for determining the risk of colorectal cancer and future rates of surveillance for patients. However, this characterization is a challenging task and suffers from significant inter- and intra-observer variability.Aims:We built an automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis.Setting and Design:Our 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.Subjects and Methods:Our method covers five common types of polyps (i.e., hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous) that are included in the US Multisociety Task Force guidelines for colorectal cancer risk assessment and surveillance. We developed multiple deep-learning approaches by leveraging a dataset of 2074 crop images, which were annotated by multiple domain expert pathologists as reference standards.Statistical Analysis:We evaluated our method on an independent test set of 239 whole-slide images and measured standard machine-learning evaluation metrics of accuracy, precision, recall, and F1 score and their 95% confidence intervals.Results:Our evaluation shows that our method with residual network architecture achieves the best performance for classification of colorectal polyps on whole-slide images (overall accuracy: 93.0%, 95% confidence interval: 89.0%–95.9%).Conclusions:Our method can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization of colorectal polyps and in subsequent risk assessment and follow-up recommendations.

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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 microM, which correlated with comparable colony formation of MCF-7 cells at 10 microM. 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 ER alpha (IC(50) = 13.8 nM) and ER beta (IC(50) = 9.1 nM). In addition, ligand receptor modeling (docking) studies were performed and glycinol was shown to bind similarly to both ER alpha and ER beta. 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.

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