BACKGROUND: Outcomes after surgical removal of localized renal cell carcinoma (RCC) are variable. There have been multiple prognostic nomograms and risk groups developed for estimation of survival outcomes, with different models in use for evaluating patient eligibility in ongoing trials of adjuvant therapy. The authors aimed to establish the most useful prognostic model for patients with localized RCC to guide trial design, biomarker research, and clinical counseling. METHODS: A total of 390 consecutive patients who underwent nephrectomy for sporadic localized RCC in a tertiary institution (1990-2006) with 65 months median follow-up were retrospectively evaluated. The Karakiewicz nomogram, the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model were compared in predicting survival outcomes (overall survival, cancer-specific survival, and freedom from recurrence) using likelihood analysis, adequacy indices, decision curve analysis, calibration, and concordance indices. RESULTS: Overall, the Karakiewicz nomogram outperformed the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model. Highly improved accuracy was seen using the Karakiewicz nomogram in survival prediction, using likelihood ratio analysis in bivariate models including the competing prognostic models. The Karakiewicz nomogram showed higher adequacy and concordance indices and improved clinical benefit relative to all other nomograms. All 4 models were reasonably calibrated. Exploratory comparisons of prespecified discretized Karakiewicz nomograms and the SORCE trial recruitment criteria (a discretized Leibovich model) of high-risk patients favored the discretized Karakiewicz nomograms. CONCLUSIONS: The Karakiewicz nomogram was shown to be superior to the other tested nomograms and risk groups in predicting survival outcomes in localized RCC. Routine integration of this model into trial design and biomarker research should be considered.
BACKGROUND: Outcomes after surgical removal of localized renal cell carcinoma (RCC) are variable. There have been multiple prognostic nomograms and risk groups developed for estimation of survival outcomes, with different models in use for evaluating patient eligibility in ongoing trials of adjuvant therapy. The authors aimed to establish the most useful prognostic model for patients with localized RCC to guide trial design, biomarker research, and clinical counseling. METHODS: A total of 390 consecutive patients who underwent nephrectomy for sporadic localized RCC in a tertiary institution (1990-2006) with 65 months median follow-up were retrospectively evaluated. The Karakiewicz nomogram, the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model were compared in predicting survival outcomes (overall survival, cancer-specific survival, and freedom from recurrence) using likelihood analysis, adequacy indices, decision curve analysis, calibration, and concordance indices. RESULTS: Overall, the Karakiewicz nomogram outperformed the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model. Highly improved accuracy was seen using the Karakiewicz nomogram in survival prediction, using likelihood ratio analysis in bivariate models including the competing prognostic models. The Karakiewicz nomogram showed higher adequacy and concordance indices and improved clinical benefit relative to all other nomograms. All 4 models were reasonably calibrated. Exploratory comparisons of prespecified discretized Karakiewicz nomograms and the SORCE trial recruitment criteria (a discretized Leibovich model) of high-risk patients favored the discretized Karakiewicz nomograms. CONCLUSIONS: The Karakiewicz nomogram was shown to be superior to the other tested nomograms and risk groups in predicting survival outcomes in localized RCC. Routine integration of this model into trial design and biomarker research should be considered.
Authors: Stefan Zastrow; Sabine Brookman-May; Thi Anh Phuong Cong; Stanislaw Jurk; Immanuel von Bar; Vladimir Novotny; Manfred Wirth Journal: World J Urol Date: 2014-05-22 Impact factor: 4.226
Authors: A Ari Hakimi; Irina Ostrovnaya; Boris Reva; Nikolaus Schultz; Ying-Bei Chen; Mithat Gonen; Han Liu; Shugaku Takeda; Martin H Voss; Satish K Tickoo; Victor E Reuter; Paul Russo; Emily H Cheng; Chris Sander; Robert J Motzer; James J Hsieh Journal: Clin Cancer Res Date: 2013-04-25 Impact factor: 12.531
Authors: Christian Doehn; Viktor Grünwald; Thomas Steiner; Markus Follmann; Heidrun Rexer; Susanne Krege Journal: Dtsch Arztebl Int Date: 2016-09-05 Impact factor: 5.594
Authors: E Jason Abel; Tyler M Bauman; Madelyn Weiker; Fangfang Shi; Tracy M Downs; David F Jarrard; Wei Huang Journal: Hum Pathol Date: 2014-01-28 Impact factor: 3.466