Gland surgery · 2026
A nomogram for predicting high-volume central lymph node metastasis in unilateral clinically node-negative papillary thyroid microcarcinoma.
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Yu Ping, Chen Zhan, Ji Hongxiang, Su Xinliang
Аннотация
BACKGROUND: Papillary thyroid microcarcinoma (PTMC) generally exhibits an excellent prognosis. However, a subset of patients develops high-volume (>5 nodes) central lymph node metastasis (CLNM), which is associated with increased recurrence risk and may influence surgical decision-making. Reliable preoperative risk stratification for high-volume CLNM in clinically node-negative (cN0) PTMC remains challenging. Therefore, this study aimed to develop and validate a nomogram for predicting high-volume CLNM in unilateral cN0 PTMC.
METHODS: In this retrospective study conducted at a single center, 1,500 patients with unilateral cN0 PTMC who received surgical treatment were included. Patients were randomly allocated to training cohort and validation cohort at a 7:3 ratio. Independent predictors of high-volume CLNM were identified using multivariate logistic regression analysis. A nomogram was constructed using the training cohort and subsequently validated.
RESULTS: Multivariate logistic analysis identified male sex, age ≤50 years, maximal tumor diameter >8 mm, and tumor multifocality as independent predictors for high-volume CLNM. The nomogram showed good discrimination performance, with a concordance index (C-index) exceeding 0.70 in both the training and validation cohorts. Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis indicated favorable clinical applicability.
CONCLUSIONS: A practical nomogram was developed to predict high-volume CLNM in patients with unilateral cN0 PTMC. This model may facilitate individualized preoperative risk stratification and assist clinicians in optimizing surgical strategies, particularly in the context of increasingly conservative management of PTMC.
METHODS: In this retrospective study conducted at a single center, 1,500 patients with unilateral cN0 PTMC who received surgical treatment were included. Patients were randomly allocated to training cohort and validation cohort at a 7:3 ratio. Independent predictors of high-volume CLNM were identified using multivariate logistic regression analysis. A nomogram was constructed using the training cohort and subsequently validated.
RESULTS: Multivariate logistic analysis identified male sex, age ≤50 years, maximal tumor diameter >8 mm, and tumor multifocality as independent predictors for high-volume CLNM. The nomogram showed good discrimination performance, with a concordance index (C-index) exceeding 0.70 in both the training and validation cohorts. Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis indicated favorable clinical applicability.
CONCLUSIONS: A practical nomogram was developed to predict high-volume CLNM in patients with unilateral cN0 PTMC. This model may facilitate individualized preoperative risk stratification and assist clinicians in optimizing surgical strategies, particularly in the context of increasingly conservative management of PTMC.