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基于网络的老年透明细胞肾细胞癌患者癌症特异性生存预测模型:一项基于人群的研究。

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Clear Cell Renal Cell Carcinoma: A Population-Based Study.

作者信息

Zhanghuang Chenghao, Wang Jinkui, Zhang Zhaoxia, Jin Liming, Tan Xiaojun, Mi Tao, Liu Jiayan, Li Mujie, He Dawei

机构信息

Department of Urology, Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing Key Laboratory of Pediatrics, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.

Yunnan Key Laboratory of Children's Major Disease Research, Department of Urology, Kunming Children's Hospital (Children's Hospital Affiliated to Kunming Medical University), Kunming, China.

出版信息

Front Public Health. 2022 Mar 3;9:833970. doi: 10.3389/fpubh.2021.833970. eCollection 2021.

DOI:10.3389/fpubh.2021.833970
PMID:35310783
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8929444/
Abstract

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is expected in the elderly and poor prognosis. We aim to explore prognostic factors of ccRCC in the elderly and construct a nomogram to predict cancer-specific survival (CSS) in elderly patients with ccRCC.

METHODS

Clinicopathological information for all elderly patients with ccRCC from 2004 to 2018 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) program. All patients were randomly assigned to a training cohort (70%) or a validation cohort (30%). Univariate and multivariate Cox regression models were used to identify the independent risk factors for CSS. A new nomogram was constructed to predict CSS at 1-, 3-, and 5 years in elderly patients with ccRCC based on independent risk factors. Subsequently, we used the consistency index (C-index), calibration curves, and the area under the receiver operating curve (AUC) and decision curve analysis (DCA) to test the prediction accuracy of the model.

RESULTS

A total of 33,509 elderly patients with ccRCC were enrolled. Univariate and multivariate Cox regression analyses results showed that age, sex, race, marriage, tumor size, histological grade, tumor, nodes, and metastases (TNM) stage, and surgery were independent risk factors for CSS in elderly patients with ccRCC. We constructed a nomogram to predict CSS in elderly patients with ccRCC. The C-index of the training cohort and validation cohort was 0.81 (95% CI: 0.802-0.818) and 0.818 (95% CI: 0.806-0.830), respectively. The AUC of the training cohort and validation cohort also suggested that the prediction model had good accuracy. The calibration curve showed that the observed value of the prediction model was highly consistent with the predicted value. DCA showed good clinical application value of the nomogram.

CONCLUSION

In this study, we explored prognostic factors in elderly patients with ccRCC. We found that age, sex, marriage, TNM stage, surgery, and tumor size were independent risk factors for CSS. We constructed a new nomogram to predict CSS in elderly patients with ccRCC with good accuracy and reliability, providing clinical guidance for patients and physicians.

摘要

背景

透明细胞肾细胞癌(ccRCC)在老年人中较为常见且预后较差。我们旨在探讨老年ccRCC患者的预后因素,并构建列线图以预测老年ccRCC患者的癌症特异性生存(CSS)情况。

方法

从监测、流行病学和最终结果(SEER)计划中下载了2004年至2018年所有老年ccRCC患者的临床病理信息。所有患者被随机分配到训练队列(70%)或验证队列(30%)。采用单因素和多因素Cox回归模型来确定CSS的独立危险因素。基于独立危险因素构建了一个新的列线图,以预测老年ccRCC患者1年、3年和5年的CSS情况。随后,我们使用一致性指数(C指数)、校准曲线、受试者操作特征曲线下面积(AUC)和决策曲线分析(DCA)来检验模型的预测准确性。

结果

共纳入33509例老年ccRCC患者。单因素和多因素Cox回归分析结果显示,年龄、性别、种族、婚姻状况、肿瘤大小、组织学分级、肿瘤、淋巴结和转移(TNM)分期以及手术是老年ccRCC患者CSS的独立危险因素。我们构建了一个列线图来预测老年ccRCC患者的CSS情况。训练队列和验证队列的C指数分别为0.81(95%CI:0.802 - 0.818)和0.818(95%CI:0.806 - 0.830)。训练队列和验证队列的AUC也表明预测模型具有良好的准确性。校准曲线显示预测模型的观察值与预测值高度一致。DCA显示列线图具有良好的临床应用价值。

结论

在本研究中,我们探讨了老年ccRCC患者的预后因素。我们发现年龄、性别、婚姻状况、TNM分期、手术和肿瘤大小是CSS的独立危险因素。我们构建了一个新的列线图来预测老年ccRCC患者的CSS情况,具有良好的准确性和可靠性,为患者和医生提供了临床指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/ec510ccc883e/fpubh-09-833970-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/b96835ea7a87/fpubh-09-833970-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/3b34aea88731/fpubh-09-833970-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/6daf1e0091c3/fpubh-09-833970-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/c53fa5cd8f2d/fpubh-09-833970-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/277013af265a/fpubh-09-833970-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/090333bf1593/fpubh-09-833970-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/ec510ccc883e/fpubh-09-833970-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/b96835ea7a87/fpubh-09-833970-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/3b34aea88731/fpubh-09-833970-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/6daf1e0091c3/fpubh-09-833970-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/c53fa5cd8f2d/fpubh-09-833970-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/277013af265a/fpubh-09-833970-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/090333bf1593/fpubh-09-833970-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e520/8929444/ec510ccc883e/fpubh-09-833970-g0007.jpg

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