Related Experiment Video
Updated: Apr 5, 2026
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
Utilizing the transformer mechanism to predict cervical lymph node metastasis in patients with papillary thyroid
Huiting Chen1,2, Fangqiu Ruan3, Li Zhu1
1Department of Ultrasound Imaging, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Rationale And Objective:
The status of cervical lymph node metastasis(LNM) in Papillary thyroid carcinoma(PTC) can affect the patient's treatment plan and prognosis. This study aims to develop and validate the application value of Vision transformer (ViT) model in preoperatively predicting cervical LNM in PTC.
Materials And Methods:
A total of 540 PTC patients were retrospectively reviewed from two hospitals from April 20,2022 to August 20,2023.The ViT model is built based on the two-dimensional rectangular ultrasound image of the primary thyroid tumor, and at the same time, to compare its performance, a deep learning model of the traditional Convolutional neural network (CNN) framework, a ultrasound radiomics combined model(Clinical-Rad model), and clinical model are built.
Results:
The ViT model demonstrated an AUC of 0.807 (95% CI: 0.709-0.905) in the internal validation cohort and 0.809 (95% CI: 0.720-0.900) in the external validation cohort. The ViT model's AUC ranged from 0.807-0.814 across all cohorts, significantly exceeding the clinical model (AUC: 0.595-0.669, P<0.001). While the AUC of the ViT model in the training cohort was slightly lower than that of the combined ultrasound radiomics model (0.814 vs 0.828, P=0.491), it showed significantly higher AUC values in the internal (0.807 vs 0.718, P=0.049) and external validation cohorts (0.809 vs 0.691, P<0.001). Compared to the clinical and combined radiomics models, the ViT model exhibited stable and superior predictive performance for PTC cervical lymph node metastasis.In the internal validation cohort, Doctor C's net reclassification improvement (NRI) with the ViT model was 0.106 (P=0.022), and the integrated discrimination improvement (IDI) was 0.106 (P=0.023). Doctor D showed NRI and IDI values of 0.113 (P=0.022) and 0.106 (P=0.024), respectively. In the external validation cohort, Doctor C's NRI and IDI were 0.090 (P=0.024) and 0.106 (P=0.024), while Doctor D had values of 0.011 (P=0.013) and 0.106 (P=0.013). The ViT model enhanced the diagnostic capabilities of both Doctor C, with less clinical experience, and Doctor D, with extensive experience.
Conclusion:
The deep learning model based on the Transformer mechanism shows good performance in predicting LNM in PTC patients, which is superior to the clinical model, Clinical-Rad model, and traditional CNN model.
