肾细胞癌复发、癌症特异性死亡率和全因死亡率预后模型的验证。

IF 5.9 2区 医学 Q1 UROLOGY & NEPHROLOGY
Journal of Urology Pub Date : 2025-04-01 Epub Date: 2024-12-02 DOI:10.1097/JU.0000000000004348
Anita Robert, Ranjeeta Mallick, Daniel I McIsaac, Luke T Lavallée, Bimal Bhindi, Daniel Heng, Lori A Wood, Ricardo Rendon, Simon Tanguay, Anthony Finelli, Rahul K Bansal, Aly-Khan Lalani, Naveen Basappa, Miles P Mannas, Jasmir G Nayak, Georg A Bjarnason, Jean-Baptiste Lattouf, Frédéric Pouliot, Patrick O Richard, Camilla Tajzler, Rodney H Breau
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引用次数: 0

摘要

目的:术后预后工具可以改善预测未来复发风险,患者咨询,评估辅助治疗的资格,并确保适当的随访监测。本分析的目的是验证现有的肾癌患者预后模型。材料和方法:加拿大肾癌信息系统(CKCis)是一项前瞻性队列研究,纳入了自2011年1月1日至今在14家机构管理的患者。CKCis用于评估15种肾癌复发预测模型,6种癌症特异性死亡率预测模型,4种手术(部分或根治性肾切除术)治疗的孤立性非转移性肾肿瘤患者的全因死亡率预测模型。在考虑辅助治疗时,采用c统计、5年校准图和术后5年决策曲线分析来衡量净收益。结果:共纳入7174例患者。对于肾癌复发,c-统计量从0.62到0.83不等,这取决于模型是否导出并应用于所有未进一步分层的患者、特定风险组或特定组织学亚型。癌症特异性死亡率模型的c统计量在0.60到0.89之间,全因死亡率模型的c统计量在0.60到0.73之间。利用透明细胞患者的决策曲线分析,选择辅助治疗以预防复发和癌症相关死亡的最佳模型是梅奥诊所预测模型。结论:模型性能差异较大,有些模型适合临床使用。如果使用预测模型来选择辅助治疗,梅奥诊所的模型在应用于大量加拿大患者的当代队列时是最好的。关键信息:肾癌预测模型的表现在应用于一个大的当代队列时差异很大。我们确定了最准确的模型,用于咨询患者的预后和辅助治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer-Specific Mortality, and All-Cause Mortality.

Purpose: Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, assessment of eligibility for adjuvant treatments, and appropriate follow-up surveillance. The purpose of this analysis was to validate prognostic models for patients with kidney cancer.

Materials and methods: The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using C statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy.

Results: Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, C statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histologic subtypes. Cancer-specific mortality models had C statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models.

Conclusions: Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.

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来源期刊
Journal of Urology
Journal of Urology 医学-泌尿学与肾脏学
CiteScore
11.50
自引率
7.60%
发文量
3746
审稿时长
2-3 weeks
期刊介绍: The Official Journal of the American Urological Association (AUA), and the most widely read and highly cited journal in the field, The Journal of Urology® brings solid coverage of the clinically relevant content needed to stay at the forefront of the dynamic field of urology. This premier journal presents investigative studies on critical areas of research and practice, survey articles providing short condensations of the best and most important urology literature worldwide, and practice-oriented reports on significant clinical observations.
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