相关实验视频

Updated: Sep 10, 2025

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

通过外科手术期的诺米图来预测经过激进囊切除术的膀癌患者的死亡率

Daniel-Vasile Dulf^1,2, Anamaria Larisa Burnar³, Patricia-Lorena Dulf⁴

¹10th Department-Oncology, "Iuliu Haţieganu" University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania.

Journal of clinical medicine

|August 28, 2025

概括

这项研究确定了淋巴血管侵袭和瘤阶段等关键因素, 一种新型的 nomogram 帮助分层患者的死亡风险, 以更好的治疗计划.

科学领域:

泌尿外科
癌症学
手术的结果

背景情况:

术后的因素极大地影响了急切切除囊术 (RC) 后的膀癌症结果.
确定整体存活率 (OS) 和无进展存活率 (PFS) 的预测因素对于患者的管理至关重要.
现有的预后工具可能与当地的临床实践不一致.

研究的目的:

在经过RC的膀癌患者中确定OS和PFS的关键外科预测因子.
根据国家标准制定个性化风险分层预测表.
加强高风险患者的临床决策.

主要方法:

在2014年至2024年期间接受RC的121名患者的回顾性分析.
对人口统计,并发症,瘤病理,新辅助治疗,手术内和术后数据的评估.
应用COX模型并构建一个3年OS预后名录.

主要成果:

淋巴血管侵袭 (LVI),较高的T阶段,N+状态和手术后并发症显著预测了更糟糕的OS和PFS.
开发的诺米图,包括T/N阶段,性别,等级,手术后并发症和早期复发,达到0.74的C指数.
诺莫图有效地确定了患有较高死亡风险的患者 (p < 0. 001).

结论:

开发的诺米图表作为一个有价值的工具,用于预测膀癌患者的死亡风险.
这种工具有助于个性化风险分层,有可能改善患者的治疗结果.
为了更广泛的适用性,建议进行外部验证.

关键词:

死亡率结果预后情况风险评分生存时间泌尿器癌

更多相关视频

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

相关概念视频

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...