Deep learning for multitask prediction on thyroid nodule frozen sections.

ABSTRACT:

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^V600E 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. T

SUBMITTER: Wang C 

PROVIDER: S-EPMC12835897 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature