Artificial intelligence analysis of nuclear morphological features and molecular abnormalities of papillary thyroid carcinoma

Abstract

Abstract Papillary thyroid carcinoma (PTC) is the most common type of thyroid carcinoma and has characteristic papillary-like 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 papillary-like 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/V600E mutation and RET/PTCrearrangement. Papillary-like 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 papillary-like nuclear features of PTC, logistic regression models could be used to predict gene abnormalities. Papillary-like 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/PTC rearrangements (p = 0.027, p = 0.043, respectively) than in cases that were negative for RET/PTC. RET/PTCrearrangements 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 papillary-like nuclear features. Genetic abnormalities had correlation with papillary-like nuclear features of PTC. Furthermore, our artificial intelligence model could significantly predict RET/PTC rearrangement of classic PTC.