Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Department of Urology and Winship Cancer Institute, Emory University, Atlanta, GA, USA.
Eur Urol Oncol. 2024 Apr;7(2):266-274. doi: 10.1016/j.euo.2023.06.008. Epub 2023 Jul 11.
Careful patient selection is critical when considering cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma (mRCC) but few studies have investigated the prognostic value of radiologic features that measure tumor burden.
To develop a prognostic model to improve CN selection with integration of common radiologic features with known prognostic factors associated with mortality in the first year following surgery.
DESIGN, SETTINGS, AND PARTICIPANTS: Data were analyzed for consecutive patients with mRCC treated with upfront CN at five institutions from 2006 to 2017. Univariable and multivariable models were used to evaluate radiographic features and known risk factors for associations with overall survival. Relevant factors were used to create the SCREEN model and compared to the International mRCC Database Consortium (IMDC) model for predictive accuracy and clinical usefulness.
A total of 914 patients with mRCC were treated with upfront CN during the study period. Seven independently predictive variables were used in the SCREEN score: three or more metastatic sites, total metastatic tumor burden ≥5 cm, bone metastasis, systemic symptoms, low serum hemoglobin, low serum albumin, and neutrophil/lymphocyte ratio ≥4. Predictive accuracy measured as the area under the receiver operating characteristic curves was 0.76 for the SCREEN score and 0.55 for the IMDC model. Decision curve analysis showed that the SCREEN model was useful beyond the IMDC classifier for threshold first-year mortality probabilities between 15% and 70%.
The SCREEN score had higher predictive accuracy for first-year mortality compared to the IMDC scheme in a multi-institutional cohort and may be used to improve CN selection.
This study provides a model to improve selection of patients with metastatic kidney cancer who may benefit from surgical removal of the primary kidney tumor. We found that radiographic measurements of the tumor burden predicted the risk of death in the first year after surgery. The model can be used to improve decision-making by these patients and their physicians.
在考虑细胞减灭性肾切除术 (CN) 治疗转移性肾细胞癌 (mRCC) 时，仔细选择患者至关重要，但很少有研究探讨测量肿瘤负担的影像学特征的预后价值。
开发一种预后模型，通过整合与术后第一年死亡率相关的常见影像学特征和已知预后因素，改善 CN 的选择。
设计、设置和参与者：对 2006 年至 2017 年期间在五个机构接受 upfront CN 治疗的 mRCC 连续患者进行了数据分析。使用单变量和多变量模型来评估与总生存相关的放射学特征和已知危险因素。使用相关因素创建了 SCREEN 模型，并将其与国际 mRCC 数据库联盟 (IMDC) 模型进行比较，以评估预测准确性和临床实用性。
在研究期间，共有 914 例 mRCC 患者接受 upfront CN 治疗。SCREEN 评分使用了七个独立的预测变量：三个或更多转移性部位、总转移性肿瘤负担≥5cm、骨转移、全身症状、低血清血红蛋白、低血清白蛋白和中性粒细胞/淋巴细胞比≥4。作为接收者操作特征曲线下面积的预测准确性，SCREEN 评分和 IMDC 模型分别为 0.76 和 0.55。决策曲线分析表明，在 15%至 70%的第一年内死亡率阈值下，SCREEN 模型比 IMDC 分类器更有用。
在多机构队列中，与 IMDC 方案相比，SCREEN 评分对第一年死亡率的预测准确性更高，可用于改善 CN 的选择。
本研究提供了一种模型，可改善可能受益于手术切除原发肾肿瘤的转移性肾肿瘤患者的选择。我们发现，肿瘤负担的放射学测量可预测术后第一年的死亡风险。该模型可用于改善这些患者及其医生的决策。
