Gland surgery · 2026
Development and validation of an ultrasound-based radiomics nomogram for predicting metastatic central cervical lymph nodes in clinically lymph node-negative patients with papillary thyroid microcarcinoma.
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Wu Liuxi, Xue Haiyan, Deng Hongyan, Ye Xinhua, Gong Li
Аннотация
BACKGROUND: The rate of cervical central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC) patients is relatively high. It is controversial that patients with clinically lymph node (LN)-negative entail prophylactic central neck dissection (pCND). The objective of this study was to develop and validate a predictive nomogram incorporating both radiomics signatures and ultrasonic features to aid in the individualized, preoperative assessment of central cervical LN metastasis in patients with clinically node-negative PTMC.
METHODS: A total of 266 patients, each with a single malignant thyroid nodule, were enrolled and randomly split into a training set and a validation set at a ratio of 7:3. Radiomics signatures were constructed by applying the least absolute shrinkage and selection operator (LASSO) regression to features extracted from two-dimensional (2D) ultrasound (US) images. We then built three logistic regression models using clinical and US features (model 1), the radiomics signatures (model 2), and a combination of both (model 3). Finally, we presented the combined model as a nomogram and assessed its clinical utility with decision curve analysis (DCA).
RESULTS: The highest diagnostic performance was achieved by model 3, which showed robust discrimination in the training [area under the curve (AUC) =0.892], internal validation (AUC =0.772), and external validation (AUC =0.842) sets, outperforming model 1 and model 2.
CONCLUSIONS: A nomogram combining radiomics analysis based on US images of PTMC with clinical and imaging features showed a better diagnostic performance than conventional US imaging features alone in predicting metastatic central cervical LNs preoperatively in clinically LN-negative patients with PTMC and could assist in making advice associated with treatment plan.
METHODS: A total of 266 patients, each with a single malignant thyroid nodule, were enrolled and randomly split into a training set and a validation set at a ratio of 7:3. Radiomics signatures were constructed by applying the least absolute shrinkage and selection operator (LASSO) regression to features extracted from two-dimensional (2D) ultrasound (US) images. We then built three logistic regression models using clinical and US features (model 1), the radiomics signatures (model 2), and a combination of both (model 3). Finally, we presented the combined model as a nomogram and assessed its clinical utility with decision curve analysis (DCA).
RESULTS: The highest diagnostic performance was achieved by model 3, which showed robust discrimination in the training [area under the curve (AUC) =0.892], internal validation (AUC =0.772), and external validation (AUC =0.842) sets, outperforming model 1 and model 2.
CONCLUSIONS: A nomogram combining radiomics analysis based on US images of PTMC with clinical and imaging features showed a better diagnostic performance than conventional US imaging features alone in predicting metastatic central cervical LNs preoperatively in clinically LN-negative patients with PTMC and could assist in making advice associated with treatment plan.