The value of a multimodal ultrasound radiomics-based nomogram in predicting central lymph node metastasis of
Minhao Lin1, Xiaohong Xu1, Jianling Peng2
1Department of Ultrasound Medicine, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, Guangdong, China.
Objectives:
This study aimed to develop a multimodal ultrasound-based nomogram integrating radiomic features from grayscale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) with clinical-ultrasound factors for the noninvasive prediction of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC), and to evaluate its predictive performance to support clinical decision-making.
Methods:
This retrospective cohort study included 449 pathologically confirmed PTMC patients from June 2023 to December 2024, randomly divided into training (n = 314) and validation (n = 135) cohorts. Radiomic features were extracted using PyRadiomics software, and feature selection was performed through Spearman correlation analysis and LASSO regression. Multivariate regression analysis identified independent clinical risk factors for CLNM. A multimodal ultrasound combined model was then developed, serving as the basis for the nomogram. The model's discriminative ability, calibration performance, and clinical utility were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
Results:
Multivariate analysis identified male sex, age < 40 years, and capsular invasion as independent risk factors for CLNM. Single-modal models (Clinical, GSUS, CEUS) achieved AUCs ranging from 0.654 to 0.787 in the validation cohort. The Combined model integrating these features significantly outperformed all single-modal ones, with AUCs of 0.925 and 0.885 in the training and validation cohorts. Calibration curves and DCA confirmed its good fit and high clinical net benefit.
Conclusion:
We successfully developed and validated a nomogram model based on multimodal ultrasound features for accurately predicting CLNM risk in PTMC patients, highlighting the value of radiomics in clinical risk assessment.
