Yi Jianying, Chen Jie, Cao Xi, Pi Lili, Zhou Chunlei, Liu Zhili, Mu Hong
Department of Clinical Laboratory, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, China.
Department of Clinical Laboratory, The Third Central Hospital, Tianjin, China; Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin, China; Artificial Cell Engineering Technology Research Center, Tianjin, China; Tianjin Institute of Hepatobiliary Disease, Tianjin, China.
Biomol Biomed. 2025 Apr 3;25(5):1099-1110. doi: 10.17305/bb.2024.11217.
In this study, we established and validated a competing risk nomogram for predicting the cumulative incidence of cervical adenosquamous carcinoma (ASC)-specific death in patients undergoing radical hysterectomy. Patients diagnosed with ASC between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results database. The cumulative incidence function for various variables influencing ASC-specific mortality was computed. A Fine-Gray competing risk model was used to identify independent predictors, formulating a competing risk nomogram. A multivariate Cox proportional hazards model was also applied for comparative analysis. The performance of the nomogram was assessed using metrics, such as the concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis. A corresponding risk classification system was constructed based on nomogram-derived scores. Factors, such as advanced age, racial background (Black race), higher tumor grade, increased tumor size, advanced TNM stage, and receipt of radiotherapy without chemotherapy, were found to be positively associated with elevated ASC-specific mortality. Additionally, age, T stage, M stage, and chemotherapy were identified as independent predictors correlated with ASC-specific mortality. The established nomogram exhibited accurate discriminatory capabilities and superior net benefits compared to the traditional TNM staging system. Additionally, the high-risk group consistently demonstrated higher probabilities of ASC-specific death in both the training and validation sets. The developed nomogram proficiently quantified the incidence of ASC-specific death in patients subjected to radical hysterectomy for ASC. This tool could help clinicians in formulating personalized treatment strategies and devising follow-up protocols.
在本研究中，我们建立并验证了一种竞争风险列线图，用于预测接受根治性子宫切除术患者宫颈腺鳞癌（ASC）特异性死亡的累积发生率。从监测、流行病学和最终结果数据库中检索出2010年至2019年期间诊断为ASC的患者。计算了影响ASC特异性死亡率的各种变量的累积发生率函数。使用Fine-Gray竞争风险模型识别独立预测因素，构建竞争风险列线图。还应用多变量Cox比例风险模型进行比较分析。使用一致性指数、受试者操作特征曲线、校准曲线和决策曲线分析等指标评估列线图的性能。基于列线图得出的分数构建了相应的风险分类系统。发现高龄、种族背景（黑人种族）、肿瘤分级较高、肿瘤大小增加、TNM分期较晚以及接受放疗但未接受化疗等因素与ASC特异性死亡率升高呈正相关。此外，年龄、T分期、M分期和化疗被确定为与ASC特异性死亡率相关的独立预测因素。与传统的TNM分期系统相比，所建立的列线图具有准确的辨别能力和更高的净效益。此外，在训练集和验证集中，高危组的ASC特异性死亡概率始终较高。所开发的列线图能够有效地量化接受根治性子宫切除术治疗ASC患者的ASC特异性死亡发生率。该工具可帮助临床医生制定个性化治疗策略并设计随访方案。
