The Predictive Value of a Nomogram Based on Ultrasound Radiomics, Clinical Factors, and Enhanced Ultrasound Features for Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.

Abstract

This study aims to establish and validate an ultrasound radiomics nomogram for preoperative prediction of central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) before operation. A retrospective analysis conducted on ultrasonic images and clinical features derived from 288 PTMC patients, who were divided into training cohorts (n = 201) and validating cohorts (n = 87) in a ratio of 7:3 base on the principle of random allocation. Radiomics features were extracted from the PTMC patients after ultrasonic examination, followed by dimension reduction and characteristic selection to construct the radiomics score (Radscore) using LASSO regression analysis. Subsequently, the models, ultrasound features plus clinical features (US-Clin), radiomics score model, and combined model of clinical features plus ultrasound features and Radscore (Combined-model) were built through multi-factor logistic regression analysis. After that, the nomograms were developed for visualization and presentation of these models. The discriminative power, calibration and clinical utility of the nomogram models were evaluated in the training and validating cohorts. The Radscore model comprised 12 carefully selected features. The independent risk factors for conventional ultrasound features and clinical features of PTMC in predicting CLNM included age <45 years, tumor envelope invasion, male gender and presence of microcalcifications, while the enhanced ultrasound features risk factor was extrathyroidal expansion. The combined model showed good performance in predicting PTMC CLNM, with AUCs of 0.921 and 0.889 in the training and validating cohorts, respectively. And DCA based on the prediction model showed good clinical utility. The nomogram developed based on preoperative clinical data, ultrasound features, and Radscore of PTMC patients can more accurately predict central lymph node metastasis (CLNM) in PTMC patients. However, it needs to be validated for clinical applicability in multicenter studies with larger sample sizes and combined with genomic mutation analyses of the tumors.