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 BRAFV600E gene mutation, and identifying lymph node metastasis. 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 BRAFV600E gene mutation prediction, the ResNet50 model achieved a patch-level AUC of 0.831 and a WSI-level accuracy of 94.4% under the extended strategy. A ViT-based model for lymph node metastasis prediction obtained an AUC of 0.671 and accuracy of 76%. Conclusions: The study indicates that deep learning models can effectively classify benign/malignant thyroid frozen sections, predict BRAFV600E gene mutations, and predict lymph node metastasis status. It also emphasizes the effectiveness of weakly supervised strategies in thyroid lesion frozen sections, which could lessen reliance on pathologists' annotations.