Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model

© 2022 John Wiley & Sons Ltd.Background: Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncology. Frozen section slides and cytological preparations provide architectural and cellular information that is analysed by pathologists to reach an intraoperative diagnosis. Progress in the fields of artificial intelligence and machine learning means that AI systems have significant potential for the provision of highly accurate real-time diagnosis in cytopathology. Objective: To investigate the efficiency of machine-learning models in the intraoperative cytological diagnosis of CNS tumours. Materials and Methods: We trained a deep neural network to classify biopsy material for intraoperative tissue diagnosis of four major brain lesions. Overall, 205 medical images were obtained from squash smear slides of histologically correlated cases, with 18 high-grade and 11 low-grade gliomas, 17 metastatic carcinomas, and 9 non-neoplastic pathological brain tissue samples. The neural network model was trained and evaluated using 5-fold cross-validation. Results: The model achieved 95% and 97% diagnostic accuracy in the patch-level classification and patient-level classification tasks, respectively. Conclusions: We conclude that deep learning-based classification of cytological preparations may be a promising complementary method for the rapid and accurate intraoperative diagnosis of CNS tumours

Subacute THYROiditis Related to SARS-CoV-2 VAccine and Covid-19 (THYROVAC Study): A Multicenter Nationwide Study.

Context The aims of the study are to compare characteristics of subacute thyroiditis (SAT) related to different etiologies, and to identify predictors of recurrence of SAT and incident hypothyroidism. Methods This nationwide, multicenter, retrospective cohort study included 53 endocrinology centers in Turkey. The study participants were divided into either COVID-19-related SAT (Cov-SAT), SARS-CoV-2 vaccine-related SAT (Vac-SAT), or control SAT (Cont-SAT) groups. Results Of the 811 patients, 258 (31.8%) were included in the Vac-SAT group, 98 (12.1%) in the Cov-SAT group, and 455 (56.1%) in the Cont-SAT group. No difference was found between the groups with regard to laboratory and imaging findings. SAT etiology was not an independent predictor of recurrence or hypothyroidism. In the entire cohort, steroid therapy requirement and younger age were statistically significant predictors for SAT recurrence. C-reactive protein measured during SAT onset, female sex, absence of antithyroid peroxidase (TPO) positivity, and absence of steroid therapy were statistically significant predictors of incident (early) hypothyroidism, irrespective of SAT etiology. On the other hand, probable predictors of established hypothyroidism differed from that of incident hypothyroidism. Conclusion Since there is no difference in terms of follow-up parameters and outcomes, COVID-19- and SARS-CoV-2 vaccine-related SAT can be treated and followed up like classic SATs. Recurrence was determined by younger age and steroid therapy requirement. Steroid therapy independently predicts incident hypothyroidism that may sometimes be transient in overall SAT and is also associated with a lower risk of established hypothyroidism