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基于SEER数据库构建远处转移性子宫内膜癌的生存预测模型
Construction of survival prediction model for metastatic advanced endometrial cancer based on the SEER database
- 陆军军医大学学报 2024年46卷第5期 页码：491-499
- 作者机构：
1.400016 重庆，重庆医科大学附属第一医院妇产科
2.400016 重庆，重庆医科大学生命科学研究院
3.400016 重庆，重庆医科大学感染性疾病分子生物学教育部重点实验室
- 作者简介：
[ 郑云峰，E-mail：773820559@qq.com ZHENG Yunfeng, E-mail: 773820559@qq.com
- 基金信息：
DOI：10.16016/j.2097-0927.202301098
中图分类号： R195.4;R730.7;R737.33收稿：2023-01-22，
修回：2023-05-10，
纸质出版：2024-03-15
- 稿件说明：
移动端阅览
郑云峰, 杨帆. 基于SEER数据库构建远处转移性子宫内膜癌的生存预测模型[J]. 陆军军医大学学报, 2024,46(5):491-499.
Yunfeng ZHENG, Fan YANG. Construction of survival prediction model for metastatic advanced endometrial cancer based on the SEER database[J]. Journal of Army Medical University, 2024, 46(5): 491-499.
郑云峰, 杨帆. 基于SEER数据库构建远处转移性子宫内膜癌的生存预测模型[J]. 陆军军医大学学报, 2024,46(5):491-499. DOI： 10.16016/j.2097-0927.202301098.
Yunfeng ZHENG, Fan YANG. Construction of survival prediction model for metastatic advanced endometrial cancer based on the SEER database[J]. Journal of Army Medical University, 2024, 46(5): 491-499. DOI： 10.16016/j.2097-0927.202301098.
摘要
目的
2
探究远处转移性子宫内膜癌(endometrial cancer
EC)患者的临床特征与生存之间的相关性，并构建生存预测模型以指导临床实践。
方法
2
从SEER数据库中回顾性收集2010-2017年间伴有远处转移EC患者的相关临床病理资料。通过单因素和多因素Cox回归分析确定独立预后因素，并基于预测因素的贡献程度建立列线图模型；采用C指数、ROC曲线、校准曲线和临床决策曲线(decision curve analysis
DCA)等方法对模型的预测价值进行评价和内部验证。另外，收集2013年10月至2019年12月就诊于重庆医科大学附属第一医院妇科的远处转移性EC患者资料，对模型进行外部验证。
结果
2
共纳入SEER数据库中1 807例符合纳入排除标准的远处转移性EC患者。多因素Cox分析结果显示，肿瘤大小、肿瘤分级和组织学亚型是远处转移性EC的独立危险因素；种族、手术和化疗是远处转移性EC的独立保护因素，并用于构建列线图。在训练队列中，模型的C指数为0.711(95%
CI
：0.695~0.727)；在内部验证队列中，模型的C指数为0.744(95%
CI
：0.721~0.768)；ROC曲线显示本模型具有良好的预测精准度和区分度；校准曲线显示模型的预测生存率与实际生存率具有良好的一致性；与此同时，DCA结果显示模型具有良好的临床应用潜能。共纳入21例本中心远处转移性EC患者作为外部验证数据集，结果显示模型预测能力良好。
结论
2
本研究建立了远处转移性EC的生存预测模型，可用于协助临床医师评估患者预后及制定个性化的治疗决策。
Abstract
Objective
2
To investigate the association between clinical features and survival in distant metastatic endometrial carcinoma (EC) patients
and to construct a prognostic risk model to guide the clinical practice.
Methods
2
The clinical data of EC patients with distant metastases were retrospectively collected from the SEER database between 2010 and 2017. Univariate and multivariate Cox regression analyses were applied to determine the independent prognostic factors of metastatic EC patients
and then a nomogram model was constructed based on these predictive factors. The predictive performance of the model was evaluated and internally validated using C-index
receiver operating characteristic (ROC) curve
calibration curve and decision curve analysis (DCA). In addition
the model was externally validated using the data collected from the EC patients with distant metastases admitted to the First Affiliated Hospital of Chongqing Medical University between October 2013 and December 2019.
Results
2
A total of 1 807 EC patients with distant metastases who met the inclusion and exclusion criteria in the SEER database were enrolled in this study. Multivariate Cox analysis revealed that tumor size
grade
and histological type were independent risk factors for distant metastatic EC; and race
surgery
and chemotherapy were independent protective factors for metastatic distant EC. In the training cohort
the C-index of the constructed nomogram model was 0.711 (95%
CI
: 0.695~0.727); in the internal validation cohort
the C-index of the model was 0.744 (95%
CI
: 0.721~0.768); ROC curve analysis indicated that the model had good prediction and discrimination; calibration curve showed that there was a high consistency between the predicted survival probability and the actual survival probability; meanwhile
the results of DCA curves revealed that the model had great potential for clinical utility. Furthermore
based on the 21 EC patients with distant metastases included as an external validation dataset
the results showed that the model had good predictive ability.
Conclusion
2
A survival prediction model for distant metastatic EC is established
and it can assist clinicians in evaluating the prognosis of patients and performing personalized clinical decisions for this specific population.
关键词
Keywords
references
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