Deep learning model based on ultrasound images predicts BRAF V600E mutation in papillary thyroid carcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
- School of Information Science and Technology, Fudan University, Shanghai, China.
- Department of Ultrasonography, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Branch of Fudan University Shanghai Cancer Center, Fuzhou, China.
- Department of Functional, Sun Yat-sen University Cancer Center Gansu Hospital, Gansu Provincial Cancer Hospital, Lanzhou, China.
Abstract
BRAF V600E mutation status detection facilitates prognosis prediction in papillary thyroid carcinoma (PTC). We developed a deep-learning model to determine the BRAF V600E status in PTC. PTC from three centers were collected as the training set (1341 patients), validation set (148 patients), and external test set (135 patients). After testing the performance of the ResNeSt-50, Vision Transformer, and Swin Transformer V2 (SwinT) models, SwinT was chosen as the optimal backbone. An integrated BrafSwinT model was developed by combining the backbone with a radiomics feature branch and a clinical parameter branch. BrafSwinT demonstrated an AUC of 0.869 in the external test set, outperforming the original SwinT, Vision Transformer, and ResNeSt-50 models (AUC: 0.782-0.824; <i>p</i> value: 0.017-0.041). BrafSwinT showed promising results in determining BRAF V600E mutation status in PTC based on routinely acquired ultrasound images and basic clinical information, thus facilitating risk stratification.