医学
肾细胞癌
接收机工作特性
病态的
阶段(地层学)
多元分析
TNM分期系统
内科学
单变量分析
单变量
T级
癌
多元统计
癌症
肿瘤科
肿瘤分期
统计
数学
古生物学
生物
作者
Oreste Martella,Giuseppe Paradiso Galatioto,Stefano Necozione,Roberto Pomante,Carlo Vicentini
出处
期刊:PubMed
日期:2011-09-01
卷期号:83 (3): 121-7
被引量:1
摘要
The objective of the current study was to compare, in a single center experience, the discriminating accuracy of two prognostic models to predict the outcome of patients surgically treated for conventional renal cell carcinoma (RCC).We retrospectively evaluated the clinical and pathological data of 100 patients surgically treated for RCC between 1998-2008 at our institution. For each patient, prognostic scores were calculated according to two models: the University of California Los Angeles integrated staging system (UISS) and the Stage, Size, Grade, and Necrosis (SSIGN) developed at the Mayo Clinic. The prognostic predictive ability of models was evaluated using receiver operating characteristic (ROC) curves.The median follow-up was 62 months (range 12-120). All clinical and pathological features that compound the algorithms were significantly associated with death from RCC in univariate and multivariate setting. The 5-year cancer-specific survival (CSS) according to the SSIGN score were 95% in the '0-2' category, 88% in '3-4', 60% in '5-6', 37% in '7-9' and 0% in the '> or = 10' group (long-rank p value < 0.001); according to the UISS the 5 yr CSS probabilities in non-metastatic patients were 100% in low, 80% in intermediate and 54% in high-risk groups; in metastatic patients, the respectively CSS were 40% in low and 25% in high-risk groups (long-rank p value < 0.001). The area under the ROC curve was 0.815 for the SSIGN score and 0.843 for the UISS (p = 0.632).In our series the SSIGN and UISS discriminated well, without relevant differences. Currently both algorithms represent usefuls clinical tools that allow risk assessment after surgical treatment of RCC. We encourage the uro-oncologist to begin to routinely rely on them in real-life practice.
科研通智能强力驱动
Strongly Powered by AbleSci AI