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睾丸生殖细胞肿瘤伴淋巴管侵犯患者的临床特征及预后因素:基于监测、流行病学和最终结果(SEER)数据库的分析

Clinical features and prognostic factors in patients diagnosed with lymphovascular invasion of testicular germ-cell tumors: Analysis based on the SEER database.

作者信息

Ke Hu, Jiang Shengming, He Ziqi, Song Qianlin, Yang Dashuai, Song Chao, Dong Caitao, Liu Junwei, Su Xiaozhe, Zhou Jiawei, Xiong Yunhe

机构信息

Urology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China.

出版信息

Front Oncol. 2023 Mar 3;13:1142441. doi: 10.3389/fonc.2023.1142441. eCollection 2023.

DOI:10.3389/fonc.2023.1142441
PMID:36937437
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10020199/
Abstract

BACKGROUND

Lymphovascular invasion (LVI) is a high-risk factor for testicular germ-cell tumors (TGCT), but a prognostic model for TGCT-LVI patients is lacking. This study aimed to develop a nomogram for predicting the overall survival (OS) of TGCT-LVI patients.

METHODS

A complete cohort of 3288 eligible TGCG-LVI patients (training cohort, 2300 cases; validation cohort, 988 cases) were obtained from the Surveillance, Epidemiology, and End Results database. Variables screened by multivariate Cox regression analysis were used to construct a nomogram, which was subsequently evaluated using the consistency index (C-index), time-dependent receiver operating characteristic curve (ROC), and calibration plots. The advantages and disadvantages of the American Joint Committee on Cancer (AJCC) staging system and the nomogram were assessed by integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Decision-analysis curve (DCA) was used to measure the net clinical benefit of the nomogram versus the AJCC staging system. Finally, Kaplan-Meier curves were used to evaluate the ability to identify different risk groups between the traditional AJCC staging system and the new risk-stratification system built on the nomogram.

RESULTS

Nine variables were screened by multivariate Cox regression analysis to construct the nomogram. The C-index (training cohort, 0.821; validation cohort, 0.819) and time-dependent ROC of 3-, 5-, and 9-year OS between the two cohorts suggested that the nomogram had good discriminatory ability. Calibration curves showed good consistency of the nomogram. The NRI values of 3-, 5-, and 9-year OS were 0.308, 0.274, and 0.295, respectively, and the corresponding values for the validation cohort were 0.093, 0.093, and 0.099, respectively (P<0.01). Additionally, the nomogram had more net clinical benefit as shown by the DCA curves, and the new risk-stratification system provided better differentiation than the AJCC staging system.

CONCLUSIONS

A prognostic nomogram and new risk-stratification system were developed and validated to assist clinicians in assessing TGCT-LVI patients.

摘要

背景

淋巴管浸润(LVI)是睾丸生殖细胞肿瘤(TGCT)的一个高危因素,但目前缺乏针对TGCT-LVI患者的预后模型。本研究旨在开发一种列线图,用于预测TGCT-LVI患者的总生存期(OS)。

方法

从监测、流行病学和最终结果数据库中获取了3288例符合条件的TGCG-LVI患者的完整队列(训练队列,2300例;验证队列,988例)。通过多变量Cox回归分析筛选出的变量用于构建列线图,随后使用一致性指数(C指数)、时间依赖性受试者工作特征曲线(ROC)和校准图对其进行评估。通过综合判别改善(IDI)和净重新分类改善(NRI)评估美国癌症联合委员会(AJCC)分期系统和列线图的优缺点。决策分析曲线(DCA)用于衡量列线图与AJCC分期系统相比的净临床获益。最后,使用Kaplan-Meier曲线评估传统AJCC分期系统与基于列线图构建的新风险分层系统之间识别不同风险组的能力。

结果

通过多变量Cox回归分析筛选出9个变量来构建列线图。两个队列之间3年、5年和9年OS的C指数(训练队列,0.821;验证队列,0.819)和时间依赖性ROC表明列线图具有良好的区分能力。校准曲线显示列线图具有良好的一致性。3年、5年和9年OS的NRI值分别为0.308、0.274和0.295,验证队列的相应值分别为0.093、0.093和0.099(P<0.01)。此外,如DCA曲线所示,列线图具有更多的净临床获益,并且新的风险分层系统比AJCC分期系统提供了更好的区分。

结论

开发并验证了一种预后列线图和新的风险分层系统,以协助临床医生评估TGCT-LVI患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/46187236d81b/fonc-13-1142441-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/26e25650bf14/fonc-13-1142441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/5aca0d8378d3/fonc-13-1142441-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/b7455b5a098b/fonc-13-1142441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/a6388b1fda12/fonc-13-1142441-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/b4427fc58c79/fonc-13-1142441-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/2190812f2385/fonc-13-1142441-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/46187236d81b/fonc-13-1142441-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/26e25650bf14/fonc-13-1142441-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/5aca0d8378d3/fonc-13-1142441-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/52598eb40614/fonc-13-1142441-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/b7455b5a098b/fonc-13-1142441-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/a6388b1fda12/fonc-13-1142441-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/b4427fc58c79/fonc-13-1142441-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/2190812f2385/fonc-13-1142441-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cf4/10020199/46187236d81b/fonc-13-1142441-g008.jpg

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