Oral oncology · 2025
Development and validation of a risk nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhang Weidong, Wu Xianjiang, Wang Yingchun, Le Qi, Xie Yue, Yu Kejie
Аннотация
BACKGROUND: The global incidence of papillary thyroid microcarcinoma (PTMC) continues to rise, and the optimal management strategy remains controversial due to its generally favorable prognosis. Central lymph node metastasis (CLNM) is an important predictor of recurrence and survival and therefore plays a critical role in guiding therapeutic decision-making. This study aimed to develop and validate a nomogram to preoperatively predict the risk of CLNM in PTMC patients.
METHODS: Patients from ward A (n = 877) were assigned to the training cohort, and those from ward B (n = 637) formed the validation cohort. Clinical characteristics and ultrasonographic variables were evaluated. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of CLNM, which were subsequently incorporated into a predictive nomogram. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Univariate analysis identified gender, age, maximum tumor diameter (MTD), multifocality, calcification and capsule status as significantly associated with CLNM (P < 0.05). Multivariate logistic regression analysis revealed gender, age, MTD, multifocality, and capsule status as independent predictors. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 0.724 in the training cohort and 0.708 in the validation cohort. Calibration curves showed excellent agreement between predicted and observed outcomes, and DCA confirmed favorable clinical utility.
CONCLUSION: We successfully developed and validated a clinically applicable nomogram that integrates preoperative clinical and ultrasonographic features to predict CLNM in PTMC patients. This model may assist clinicians in tailoring surgical strategies and optimizing individualized management.
METHODS: Patients from ward A (n = 877) were assigned to the training cohort, and those from ward B (n = 637) formed the validation cohort. Clinical characteristics and ultrasonographic variables were evaluated. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of CLNM, which were subsequently incorporated into a predictive nomogram. Model performance was assessed using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
RESULTS: Univariate analysis identified gender, age, maximum tumor diameter (MTD), multifocality, calcification and capsule status as significantly associated with CLNM (P < 0.05). Multivariate logistic regression analysis revealed gender, age, MTD, multifocality, and capsule status as independent predictors. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 0.724 in the training cohort and 0.708 in the validation cohort. Calibration curves showed excellent agreement between predicted and observed outcomes, and DCA confirmed favorable clinical utility.
CONCLUSION: We successfully developed and validated a clinically applicable nomogram that integrates preoperative clinical and ultrasonographic features to predict CLNM in PTMC patients. This model may assist clinicians in tailoring surgical strategies and optimizing individualized management.