Related Experiment Video
Updated: May 22, 2025
Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer
Published on: June 9, 2023
Deep learning based on ultrasound images predicting cervical lymph node metastasis in postoperative patients with
Fengjing Fan1, Fei Li1, Yixuan Wang1
1Department of Medical Ultrasound, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China.
Objectives:
To develop a deep learning (DL) model based on ultrasound (US) images of lymph nodes for predicting cervical lymph node metastasis (CLNM) in postoperative patients with differentiated thyroid carcinoma (DTC).
Methods:
Retrospective collection of 352 lymph nodes from 330 patients with cytopathology findings between June 2021 and December 2023 at our institution. The database was randomly divided into the training and test cohort at an 8:2 ratio. The DL basic model of longitudinal and cross-sectional of lymph nodes was constructed based on ResNet50 respectively, and the results of the 2 basic models were fused (1:1) to construct a longitudinal + cross-sectional DL model. Univariate and multivariate analyses were used to assess US features and construct a conventional US model. Subsequently, a combined model was constructed by integrating DL and US.
Results:
The diagnostic accuracy of the longitudinal + cross-sectional DL model was higher than that of longitudinal or cross-sectional alone. The area under the curve (AUC) of the combined model (US + DL) was 0.855 (95% CI, 0.767-0.942) and the accuracy, sensitivity, and specificity were 0.786 (95% CI, 0.671-0.875), 0.972 (95% CI, 0.855-0.999), and 0.588 (95% CI, 0.407-0.754), respectively. Compared with US and DL models, the integrated discrimination improvement and net reclassification improvement of the combined models are both positive.
Conclusions:
This preliminary study shows that the DL model based on US images of lymph nodes has a high diagnostic efficacy for predicting CLNM in postoperative patients with DTC, and the combined model of US+DL is superior to single conventional US and DL for predicting CLNM in this population.
Advances In Knowledge:
We innovatively used DL of lymph node US images to predict the status of cervical lymph nodes in postoperative patients with DTC.
