A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens.

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that cannot be performed by pathologists has not been fully evaluated. METHODS: The authors initially obtained low-power field cytology images from archived Papanicolaou-stained urinary cytology glass slides from 232 patients. To aid in the development of a diagnosis support system that could identify suspicious atypical cells, the images were divided into high-power field panel image sets for training and testing of the 16-layer Visual Geometry Group convolutional neural network. The DLS was trained using linked information pertaining to whether urothelial carcinoma (UC) in the corresponding histology specimen was invasive or noninvasive, or high-grade or low-grade, followed by an evaluation of whether the DLS could diagnose these characteristics. RESULTS: The DLS achieved excellent performance (eg, an area under the curve [AUC] of 0.9890; F1 score, 0.9002) when trained on high-power field images of malignant and benign cases. The DLS could diagnose whether the lesions were invasive UC (AUC, 0.8628; F1 score, 0.8239) or high-grade UC (AUC, 0.8661; F1 score, 0.8218). Gradient-weighted class activation mapping of these images indicated that the diagnoses were based on the color of tumor cell nuclei. CONCLUSIONS: The DLS could accurately screen UC cells and determine the malignant potential of tumors more accurately than classical cytology. The use of a DLS during cytopathology screening could help urologists plan therapeutic strategies, which, in turn, may be beneficial for patients.

FAM111B enhances proliferation of KRAS-driven lung adenocarcinoma by degrading p16.

Lung cancer is a common type of cancer that represents a health problem worldwide; lung adenocarcinoma (LUAD) is a major subtype of lung cancer. Although several treatments for LUAD have been developed, the mortality rate remains high because of uncontrollable progression. Further biological and clinicopathological studies are therefore needed. Here, we investigated the role of family with sequence similarity 111 member B (FAM111B), which is highly expressed in papillary-predominant LUAD; however, its role in cancer is unclear. An immunohistochemical analysis confirmed that papillary-predominant adenocarcinomas exhibited higher expression of FAM111B, compared with lepidic-predominant adenocarcinomas. Additionally, FAM111B expression was significantly correlated with clinical progression. In vitro functional analyses using FAM111B-knockout cells demonstrated that FAM111B plays an important role in proliferation and cell cycle progression of KRAS-driven LUAD under serum-starvation conditions. Furthermore, FAM111B regulated cyclin D1-CDK4-dependent cell cycle progression by degradation of p16. In summary, we revealed the clinical importance of FAM111B in human tumor tissues, as well as its function as a degradative enzyme. Therefore, FAM111B has potential as a clinicopathological prognostic marker for LUAD.