Development and evaluation of a postoperative prognostic nomogram model for patients with poorly differentiated thyroid carcinoma
10.7659/j.issn.1005-6947.240286
- VernacularTitle:低分化甲状腺癌患者术后预后列线图模型的建立与评价
- Author:
Xianqing ZENG
1
;
Yunlong WANG
1
;
Jinfeng ZHANG
1
Author Information
1. 南阳医学高等专科学校第一附属医院 小儿外科甲状腺乳腺外科,河南 南阳 473000
- Publication Type:Journal Article
- Keywords:
Thyroid Neoplasms;
Prognosis;
Nomogram model
- From:
Chinese Journal of General Surgery
2025;34(6):1238-1245
- CountryChina
- Language:Chinese
-
Abstract:
Background and Aims:Poorly differentiated thyroid carcinoma(PDTC)is a relatively rare but highly aggressive type of thyroid malignancy.Its biological behavior lies between differentiated and undifferentiated thyroid carcinoma,and it is often characterized by early metastasis,high recurrence rates,and poor survival outcomes.At present,prognostic assessment for PDTC patients primarily relies on traditional indicators such as TNM staging,and there remains a lack of systematic,multi-factorial,and individualized predictive tools.As a visual and quantitative method,the nomogram model has been widely applied in the prognostic evaluation of various tumors;however,its use in PDTC remains limited.This study aims to identify key risk factors associated with poor prognosis in PDTC patients and to construct a risk prediction nomogram model based on multivariate analysis,in order to provide clinical support for individualized postoperative prognostic assessment.Methods:A total of 55 PDTC patients who underwent surgical treatment at our hospital from January 2015 to December 2020 were retrospectively enrolled and followed up for three years.Based on tumor recurrence,metastasis,and mortality during the follow-up period,patients were divided into a good prognosis group and a poor prognosis group.Univariate analysis was performed to screen for clinical features associated with prognosis,followed by multivariate logistic regression to identify independent risk factors.A nomogram risk prediction model was constructed using R software(version 3.5.3),and its predictive performance and calibration were evaluated by receiver operating characteristic(ROC)curve and Bootstrap validation.Results:During the 3-year follow-up,15 patients experienced tumor progression and 1 patient died,resulting in a poor prognosis rate of 29.1%.Univariate analysis showed statistically significant differences in tumor diameter,TNM stage,local invasion,surgical approach,vascular invasion,and nerve involvement between the two groups(all P<0.05).Multivariate logistic regression identified tumor diameter≥3 cm,advanced TNM stage,local invasion,subtotal thyroidectomy,vascular invasion,and nerve involvement as independent risk factors for poor prognosis(all P<0.05).The nomogram model constructed based on these variables demonstrated a C-index of 0.794(95%CI=0.725-0.846),an AUC of 0.817,sensitivity of 82.26%,and specificity of 86.35%,indicating good discriminatory ability and predictive accuracy.Conclusion:Tumor diameter,TNM stage,local invasion,surgical approach,vascular invasion,and nerve involvement are important factors influencing postoperative prognosis in PDTC patients.The nomogram model based on these variables exhibits strong predictive performance and may serve as a valuable tool for individualized risk assessment and therapeutic decision-making in clinical practice.
