预测高风险局限性和局部进展性肾细胞癌手术后的疾病复发、早期进展和总生存。

Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma.

机构信息

Department of Surgical Oncology, Fox Chase Cancer Center, Philadelphia, PA, USA.

ECOG-ACRIN Biostatistics Center, Dana-Farber Cancer Institute, Boston, MA, USA.

出版信息

Eur Urol. 2021 Jul;80(1):20-31. doi: 10.1016/j.eururo.2021.02.025. Epub 2021 Mar 9.

Abstract

BACKGROUND

Risk stratification for localized renal cell carcinoma (RCC) relies heavily on retrospective models, limiting their generalizability to contemporary cohorts.

OBJECTIVE

To introduce a contemporary RCC prognostic model, developed using prospective, highly annotated data from a phase III adjuvant trial.

DESIGN, SETTING, AND PARTICIPANTS: The model utilizes outcome data from the ECOG-ACRIN 2805 (ASSURE) RCC trial.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome for the model is disease-free survival (DFS), with overall survival (OS) and early disease progression (EDP) as secondary outcomes. Model performance was assessed using discrimination and calibration tests.

RESULTS AND LIMITATIONS

A total of 1735 patients were included in the analysis, with 887 DFS events occurring over a median follow-up of 9.6 yr. Five common tumor variables (histology, size, grade, tumor necrosis, and nodal involvement) were included in each model. Tumor histology was the single most powerful predictor for each model outcome. The C-statistics at 1 yr were 78.4% and 81.9% for DFS and OS, respectively. Degradation of the DFS, DFS validation set, and OS model's discriminatory ability was seen over time, with a global c-index of 68.0% (95% confidence interval or CI [65.5, 70.4]), 68.6% [65.1%, 72.2%], and 69.4% (95% CI [66.9%, 71.9%], respectively. The EDP model had a c-index of 75.1% (95% CI [71.3, 79.0]).

CONCLUSIONS

We introduce a contemporary RCC recurrence model built and internally validated using prospective and highly annotated data from a clinical trial. Performance characteristics of the current model exceed available prognostic models with the added benefit of being histology inclusive and TNM agnostic.

PATIENT SUMMARY

Important decisions, including treatment protocols, clinical trial eligibility, and life planning, rest on our ability to predict cancer outcomes accurately. Here, we introduce a contemporary renal cell carcinoma prognostic model leveraging high-quality data from a clinical trial. The current model predicts three outcome measures commonly utilized in clinical practice and exceeds the predictive ability of available prognostic models.

摘要

背景

局部肾细胞癌(RCC)的风险分层主要依赖于回顾性模型,这限制了它们在当代队列中的通用性。

目的

引入一种基于 III 期辅助试验前瞻性、高度注释数据的当代 RCC 预后模型。

设计、地点和参与者:该模型利用 ECOG-ACRIN 2805(ASSURE)RCC 试验的结果数据。

测量和统计分析

模型的主要结局是无病生存(DFS),总生存(OS)和早期疾病进展(EDP)为次要结局。使用区分和校准测试评估模型性能。

结果和局限性

共纳入 1735 例患者,中位随访 9.6 年后发生 887 例 DFS 事件。每个模型均包含 5 个常见肿瘤变量(组织学、大小、分级、肿瘤坏死和淋巴结受累)。肿瘤组织学是每个模型结局的最有力预测因素。1 年时 DFS 和 OS 的 C 统计量分别为 78.4%和 81.9%。DFS、DFS 验证集和 OS 模型的区分能力随着时间的推移而降低,整体 c 指数为 68.0%(95%置信区间或 CI [65.5, 70.4]),68.6%[65.1%, 72.2%]和 69.4%(95% CI [66.9%, 71.9%])。EDP 模型的 C 指数为 75.1%(95% CI [71.3, 79.0])。

结论

我们引入了一种基于临床试验前瞻性和高度注释数据构建并内部验证的当代 RCC 复发模型。当前模型的性能特征优于现有预后模型,并且具有包含组织学和 TNM 不可知的优点。

患者总结

包括治疗方案、临床试验资格和生活规划在内的重要决策都取决于我们准确预测癌症结果的能力。在这里,我们引入了一种基于临床试验高质量数据的当代肾细胞癌预后模型。当前模型预测了临床实践中常用的三种结局指标,并且超过了现有预后模型的预测能力。

相似文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索