Deep learning for multitask prediction on thyroid nodule frozen sections

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^V600E gene mutation, and identifying lymph node metastasis.

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.

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^V600E 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%.

The study indicates that deep learning models can effectively classify benign/malignant thyroid frozen sections, predict BRAF^V600E 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.