Frontiers in endocrinology · 2026
A clinical nomogram for evaluating occult central lymph node metastasis in clinically low-risk, unifocal papillary thyroid microcarcinoma.
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Qin Linan, Liu Yunhe, Zhang Xiwei, Ni Song, Liu Wensheng, Huang Hui, Liu Shaoyan
Аннотация
BACKGROUND: This study aimed to develop and validate a clinical nomogram to predict the risk of central lymph node metastasis (CLNM) in patients with clinically low-risk papillary thyroid microcarcinoma (PTMC).
METHODS: Patients with clinically low-risk PTMC were enrolled in this retrospective cohort study. Potential predictive factors include sex, age, tumor size, tumor location, and Hashimoto's thyroiditis (HT). Independent predictive factors were identified using multivariate logistic regression and a nomogram was developed using the finalized model. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).
RESULTS: This study included 4,201 patients with clinically low-risk unifocal PTMC, of whom 1,553 (37.0%) presented with CLNM. Multivariate logistic regression analysis revealed four independent predictive factors: sex, age, tumor size, and tumor location. The constructed nomogram demonstrated moderate predictive performance, with an area under the ROC curve (AUC) of 0.700 (95% confidence interval [CI]: 0.680-0.719) in the training cohort and 0.697 (95% CI: 0.667-0.727) in the validation cohort. Furthermore, the fit of the calibration curves was good, and the DCA results suggest potential clinical utility for patient benefit.
CONCLUSION: A clinical nomogram incorporating sex, age, tumor size, and tumor location was developed and internally validated. This tool achieved moderate predictive accuracy for estimating the risk of CLNM in patients with clinically low-risk unifocal PTMC. This screening-level model may provide auxiliary clinical value for outpatient consultation. It can aid clinicians in patient counselling and facilitate preliminary risk stratification and clinician-patient communication for this patient population.
METHODS: Patients with clinically low-risk PTMC were enrolled in this retrospective cohort study. Potential predictive factors include sex, age, tumor size, tumor location, and Hashimoto's thyroiditis (HT). Independent predictive factors were identified using multivariate logistic regression and a nomogram was developed using the finalized model. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).
RESULTS: This study included 4,201 patients with clinically low-risk unifocal PTMC, of whom 1,553 (37.0%) presented with CLNM. Multivariate logistic regression analysis revealed four independent predictive factors: sex, age, tumor size, and tumor location. The constructed nomogram demonstrated moderate predictive performance, with an area under the ROC curve (AUC) of 0.700 (95% confidence interval [CI]: 0.680-0.719) in the training cohort and 0.697 (95% CI: 0.667-0.727) in the validation cohort. Furthermore, the fit of the calibration curves was good, and the DCA results suggest potential clinical utility for patient benefit.
CONCLUSION: A clinical nomogram incorporating sex, age, tumor size, and tumor location was developed and internally validated. This tool achieved moderate predictive accuracy for estimating the risk of CLNM in patients with clinically low-risk unifocal PTMC. This screening-level model may provide auxiliary clinical value for outpatient consultation. It can aid clinicians in patient counselling and facilitate preliminary risk stratification and clinician-patient communication for this patient population.