摘要
背景与目的:低分化甲状腺癌(PDTC)是一种临床上较为少见但具有高度侵袭性的甲状腺恶性肿瘤类型,其生物学行为介于分化型与未分化型甲状腺癌之间,常表现为早期转移、复发率高和生存率低。目前,针对PDTC患者预后的评估主要依赖TNM分期等传统指标,尚缺乏系统化、多因素整合的个体化预测工具。列线图模型作为一种可视化、量化的预测方法,已广泛应用于多种肿瘤的预后研究,但在PDTC中的应用尚不充分。本研究旨在识别影响PDTC患者预后的关键危险因素,并基于多因素分析构建风险列线图模型,以期为临床精准判断PDTC患者术后预后提供辅助决策支持。方法:纳入2015年1月─2020年12月在南阳医学高等专科学校第一附属医院接受手术治疗的55例PDTC患者,随访期为3年。根据随访期间肿瘤复发、转移及死亡情况,将患者分为预后良好组与预后不良组。通过单因素分析筛选与预后相关的临床特征,进一步采用多因素Logistic回归分析确定独立危险因素。基于多因素分析结果,使用R 3.5.3软件构建列线图风险预测模型,并通过ROC曲线和Bootstrap方法评估模型的预测性能和校准能力。结果:55例患者中,随访期间有15例出现肿瘤进展,1例死亡,预后不良率为29.1%。单因素分析显示,肿瘤直径、TNM分期、局部侵犯、手术方式、脉管侵犯及神经受累在两组间差异具有统计学意义(均P<0.05)。多因素Logistic回归分析表明,肿瘤直径≥3 cm、晚期TNM分期、局部侵犯、次全切术、存在脉管侵犯和神经受累是PDTC患者预后不良的独立危险因素(均P<0.05)。以此为基础构建的列线图模型C-指数为0.794(95%CI=0.725~0.846),ROC曲线下面积为0.817,敏感度为82.26%,特异度为86.35%,模型表现出良好的区分度和准确率。结论:肿瘤直径、TNM分期、局部侵犯、手术方式、脉管侵犯和神经受累是影响PDTC患者术后预后的重要因素。基于上述变量构建的列线图模型具有较好的预测能力,能够为PDTC的个体化风险评估和治疗策略制定提供有利参考。
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.
作者
曾宪清
王云龙
张金锋
ZENG Xianqing;WANG Yunlong;ZHANG Jinfeng(Department of Pediatric Surgery/Thyroid and Breast Surgery,Nanyang Medical College First Affiliated Hospital,Nanyang,Henan 473000,China)
出处
《中国普通外科杂志》
北大核心
2025年第6期1238-1245,共8页
China Journal of General Surgery
关键词
甲状腺肿瘤
预后
列线图模型
Thyroid Neoplasms
Prognosis
Nomogram model
