PURPOSE Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, and assessment of eligibility for adjuvant treatments and ensure appropriate follow-up surveillance. The purpose of this analysis was to validate existing 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 histological 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.

中文翻译：

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验证肾细胞癌复发、癌症特异性死亡率和全因死亡率的预后模型。


目的 术后预后工具可以改进对未来复发风险的预测、患者咨询和辅助治疗资格评估，并确保适当的随访监测。该分析的目的是验证肾癌患者现有的预后模型。材料和方法 加拿大肾癌信息系统是自 2011 年 1 月 1 日至今在 14 个机构管理的患者前瞻性队列。加拿大肾癌信息系统用于评估接受手术治疗（部分或根治性肾切除术）的孤立性、非转移性肾肿瘤患者的 15 种肾癌复发预测模型、6 种癌症特异性死亡率预测模型和 4 种全因死亡率预测模型。在考虑辅助治疗时，使用 c 统计量、用于校准的 5 年校准图和术后 5 年决策曲线分析来衡量净获益的鉴别力。结果 共纳入 7,174 例患者。对于肾癌复发，c 统计量范围为 0.62 至 0.83，具体取决于模型是否派生并应用于所有患者，而无需进一步分层、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 统计量范围为 0.60 至 0.89，全因死亡率模型的 c 统计量范围为 0.60 至 0.73。在透明细胞肾细胞癌患者中使用决策曲线分析，选择辅助治疗以防止复发和癌症相关死亡的最佳模型是 Mayo Clinic 预测模型。结论 模型性能差异很大，有些适合临床使用。 如果使用预测模型来选择辅助治疗，则 Mayo Clinic 模型在应用于大量当代加拿大患者时效果最佳。