Granular cell basal cell carcinoma: A case report.

Granular cell basal cell carcinoma (BCC) is a rare histopathological variant of BCC. Our review of the literature revealed only 17 previously identified cases. We report the case of a 47-year-old man who presented with an ulceration on his right upper lip which was subsequently removed. Histopathologic examination revealed that the tumor was composed solely of granular cells with numerous cytoplasmic eosinophilic round inclusion bodies. Mitotic figures ranged from 8 to 15 per 10 high-power fields, with a Ki-67 proliferative index of ~5%. Immunohistochemically, the granular cells showed strong and diffuse positivity for Ber-EP4, pan-cytokeratin, AE1/AE3, CK5/6 and p63 and focal positivity for lysozyme, CD68 (clone KP1) and Bcl-2.

A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China.

BACKGROUND: In China, rapid intraoperative diagnosis of frozen sections of thyroid nodules is used to guide surgery. However, the lack of subspecialty pathologists and delayed diagnoses are challenges in clinical treatment. This study aimed to develop novel diagnostic approaches to increase diagnostic effectiveness. METHODS: Artificial intelligence and machine learning techniques were used to automatically diagnose histopathological slides. AI-based models were trained with annotations and selected as efficientnetV2-b0 from multi-set experiments. RESULTS: On 191 test slides, the proposed method predicted benign and malignant categories with a sensitivity of 72.65%, specificity of 100.0%, and AUC of 86.32%. For the subtype diagnosis, the best AUC was 99.46% for medullary thyroid cancer with an average of 237.6 s per slide. CONCLUSIONS: Within our testing dataset, the proposed method accurately diagnosed the thyroid nodules during surgery.