Use of Artificial Intelligence for the Interpretable Prediction of the Pathologic Diagnosis and Molecular Abnormalities of Flat Urothelial Lesions.

Flat urothelial lesions are important because of their potential for carcinogenesis and development into invasive urothelial carcinomas. However, it is difficult for pathologists to detect early flat urothelial changes and accurately diagnose flat urothelial lesions. To predict the pathologic diagnosis and molecular abnormalities of flat urothelial lesions from pathologic images, artificial intelligence with an interpretable method was used. Next-generation sequencing on 110 hematoxylin and eosin-stained slides of normal urothelium and flat urothelial lesions, including atypical urothelium, dysplasia, and carcinoma in situ, detected 17 types of molecular abnormalities. To generate an interpretable prediction, a new method for segmenting urothelium and a new pathologic criteria-based artificial intelligence (PCB-AI) model was developed. κ Statistics and accuracy measurements were used to evaluate the ability of the model to predict the pathologic diagnosis. The likelihood ratio test was performed to evaluate the logistic regression models for predicting molecular abnormalities. The diagnostic prediction of the PCB-AI model was almost in perfect agreement with the pathologists' diagnoses (weighted κ = 0.98). PCB-AI significantly predicted some molecular abnormalities in an interpretable manner, including abnormalities of TP53 (P = 0.02), RB1 (P = 0.04), and ERCC2 (P = 0.04). Thus, this study developed a new method of obtaining accurate urothelial segmentation, interpretable prediction of pathologic diagnosis, and interpretable prediction of molecular abnormalities.

Artificial Intelligence Detected the Relationship Between Nuclear Morphological Features and Molecular Abnormalities of Papillary Thyroid Carcinoma.

Papillary thyroid carcinoma (PTC) is the most common type of thyroid carcinoma and has characteristic nuclear features. Genetic abnormalities of PTC affect recent molecular target therapeutic strategy towards RET-altered cases, and they affect clinical prognosis and progression. However, there has been insufficient objective analysis of the correlation between genetic abnormalities and nuclear features. Using our newly developed methods, we studied the correlation between nuclear morphology and molecular abnormalities of PTC with the aim of predicting genetic abnormalities of PTC. We studied 72 cases of PTC and performed genetic analysis to detect BRAF p.V600E mutation and RET fusions. Nuclear features of PTC, such as nuclear grooves, pseudo-nuclear inclusions, and glassy nuclei, were also automatically detected by deep learning models. After analyzing the correlation between genetic abnormalities and nuclear features of PTC, logistic regression models could be used to predict gene abnormalities. Nuclear features were accurately detected with over 0.90 of AUCs in every class. The ratio of glassy nuclei to nuclear groove and the ratio of pseudo-nuclear inclusion to glassy nuclei were significantly higher in cases that were positive for RET fusions (p = 0.027, p = 0.043, respectively) than in cases that were negative for RET fusions. RET fusions were significantly predicted by glassy nuclei/nuclear grooves, pseudo-nuclear inclusions/glassy nuclei, and age (p = 0.023). Our deep learning models could accurately detect nuclear features. Genetic abnormalities had a correlation with nuclear features of PTC. Furthermore, our artificial intelligence model could significantly predict RET fusions of classic PTC.