Deep learning for multitask prediction on thyroid nodule frozen sections.

Background: Preoperative ambiguous thyroid nodules often depend on intraoperative frozen sections for surgical planning, but misdiagnosis can occur due to low-quality frozen sections, limited diagnostic time, and a shortage of pathologists. Deep learning models and conventional radiomics have shown potential in improving diagnostic accuracy in thyroid nodules, yet their integration remains under-explored. This study aimed to develop deep-learning-based models to assist in the intraoperative pathological diagnosis of thyroid nodules by classifying benign/malignant cases, predicting BRAF Methods: A total of 436 Whole-Slide Images (WSIs) of thyroid frozen sections were analyzed using deep learning techniques. The analysis included image preprocessing, feature extraction, and classifier training. Patch-to-WSI feature aggregation was done via Patch Likelihood Histogram (PLH) and Bag of Words (BoW) methods. Results: On the test set, the InceptionV3 model performed best in benign/malignant classification with an AUC of 0.998 and accuracy of 0.988, where weakly supervised strategies surpassed supervised ones. For BRAF Conclusions: The study indicates that deep learning models can effectively classify benign/malignant thyroid frozen sections, predict BRAF