Preoperative prediction of occult high-volume central lymph node metastasis in clinically node-negative papillary thyroid carcinoma using a multimodal practical model: development, temporal validation, and external validation.
Authors
Affiliations (3)
Affiliations (3)
- Department of Ultrasonography, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian, China.
- Department of Breast Surgery, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian, China.
- Department of Pathology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fujian, China.
Abstract
Preoperative risk estimation of occult high-volume central lymph node metastasis (CLNM) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) remains challenging. We aimed to develop and validate a practical multimodal model integrating conventional ultrasound and radiomics for individualized preoperative risk stratification. In this retrospective, two-center, multicohort study, 814 patients with cN0 PTC were included and assigned to a development cohort (n = 470), a temporal validation cohort (n = 202), and an external validation cohort (n = 142). Preoperative clinical variables and grayscale ultrasound radiomics features were evaluated. Least absolute shrinkage and selection operator regression was used for feature selection. Four candidate machine-learning algorithms were compared in the development cohort, and the selected classifier was used to construct clinical, radiomics, and fusion models. Model performance was assessed in terms of discrimination, calibration, and clinical utility. SHapley Additive exPlanations (SHAPs) analysis and a web-based calculator were used to improve interpretability and applicability. Three clinical predictors and 20 radiomics features were retained. Random forest was selected for final model construction. In the temporal validation cohort, the clinical, radiomics, and fusion models achieved area under the curve (AUC) values of 0.703, 0.755, and 0.845, respectively; in the external validation cohort, the corresponding AUC values were 0.676, 0.730, and 0.817. For the fusion model, sensitivity, specificity, and accuracy were 0.837, 0.695, and 0.755 in the temporal validation cohort and 0.769, 0.761, and 0.764 in the external validation cohort, respectively. SHAPs analysis identified radiomics score and maximum tumor diameter as the major contributors to model output. A practical multimodal model integrating conventional ultrasound and radiomics showed favorable performance for preoperative risk estimation of occult high-volume CLNM in cN0 PTC and may support individualized risk stratification, pending further prospective validation. This multimodal model may refine preoperative risk estimation for patients with cN0 PTC who harbor occult high-volume CLNM, thereby providing an adjunctive tool for individualized perioperative risk assessment.