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.

Small sized centroblasts as poor prognostic factor in follicular lymphoma - Based on artificial intelligence analysis.

Histological assessment of centroblasts is an important evaluation in the diagnosis of follicular lymphoma, but there is substantial observer variation in assessment among hematopathologists. We aimed to perform quantitative morphological analysis of centroblasts in follicular lymphoma using new artificial intelligence technology in relation to the clinical prognosis. Hematoxylin and eosin slides of lesions were prepared from 36 cases of follicular lymphoma before initial chemotherapy. Cases were classified into three groups by clinical course after initial treatment. The 'excellent prognosis' group were without recurrence or progression of follicular lymphoma within 60 months, the 'poor prognosis' group were those that had relapse, exacerbation, or who died due to the follicular lymphoma within 60 months, and the 'indeterminate prognosis' group were those without recurrence or progression but before the passage of 60 months. We created whole slide images and image patches of hematoxylin and eosin sections for all cases. We designed an object detection model specialized for centroblasts by fine-tuning YOLOv5 and segmented all centroblasts in whole slide images. The morphological characteristics of centroblasts in relation to the clinical prognosis of follicular lymphoma were analyzed. Centroblasts in follicular lymphoma of the poor prognosis group were significantly smaller in nuclear size than those in follicular lymphoma of the excellent prognosis group in the following points: median of nuclear area (p = 0.013), long length (p = 0.042), short length (p = 0.007), nuclear area of top 10 % cells (p = 0.024) and short length of top 10 % cells (p = 0.020). Cases with a mean nuclear area of <55 µm2 had poorer event-free survival than those with a mean nuclear area of ≥55 µm2 (p < 0.0123). AI methodology is suggested to be able to surpass pathologist's observation in capturing morphological features. Small-sized centroblasts will likely become a new prognostic factor of follicular lymphoma.