Roy Mano1, Cihan Duzgol2, Maz Ganat3, Debra A Goldman4, Kyle A Blum5, Andrew W Silagy6, Aleksandra Walasek7, Alejandro Sanchez8, Renzo G DiNatale7, Julian Marcon9, Mahyar Kashan10, Maria F Becerra11, Nicole Benfante7, Jonathan A Coleman7, Michael W Kattan12, Paul Russo7, Oguz Akin2, Irina Ostrovnaya13, A Ari Hakimi14. 1. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine, Tel-Aviv University, Tel Aviv-Yafo, Israel. 2. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY. 3. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Surgery, Division of Urologic Oncology, Englewood Health, Englewood, NJ. 4. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY. 5. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, University of Texas Health Science Center at Houston, Houston, TX. 6. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Surgery, University of Melbourne, Austin Hospital, Melbourne, Australia. 7. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY. 8. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Division of Urology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT. 9. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Ludwig-Maximilians-Universität München, Munich, Germany. 10. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, SUNY Downstate Medical Center, Brooklyn, NY. 11. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY; Department of Urology, Miller School of Medicine, University of Miami, Miami, FL. 12. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH. 13. Department of Surgery, Division of Urologic Oncology, Englewood Health, Englewood, NJ. 14. Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY. Electronic address: hakimia@mskcc.org.
Abstract
OBJECTIVES: Preoperative models, based on patient and tumor characteristics, predict risk for adverse outcomes after nephrectomy. Changes in renal tumor characteristics over the last decades, warrant further evaluation using contemporary cohorts. We aimed to validate a previously published preoperative nomogram predicting 12-year metastasis-free probability after nephrectomy for localized renal tumors in a contemporary cohort. PATIENTS AND METHODS: After obtaining institutional review board approval, data of 1,760 patients who underwent nephrectomy for a localized renal mass between 2005 and 2011 were reviewed. Preoperative images were evaluated for the presence of tumor necrosis, lymphadenopathy, and tumor size. The study outcome was metastatic-free probability. Model discrimination was assessed with Gönen and Heller's concordance probability estimate, and calibration was evaluated. RESULTS: The cohort included 1,102 male and 658 female patients with a median age of 60 years. Most patients presented incidentally (84%). On imaging, 3% had evidence of lymphadenopathy, 55% had necrosis and median tumor diameter was 3.7 cm (interquartile range [IQR]: 2.5, 5.5). Median follow-up in non-metastatic patients was 7.7 years (IQR: 5.3, 9.7). Estimated 12-year metastatic-free probability was 88% (86%-90%). The model showed strong discrimination (concordance probability estimate [CPE]: 0.77), and fair calibration. The time-dependent receiver operating characteristic (ROC) curves showed strong discrimination at all-time points and the area under the curve (AUC) for year 12 was 0.83 (95% Confidence Interval: 0.78-0.89). CONCLUSIONS: We validated the preoperative nomogram of 12-year metastasis-free probability in a contemporary cohort despite different tumor characteristics. Future studies should evaluate the role of preoperative risk stratification in patient selection for neoadjuvant treatment.
OBJECTIVES: Preoperative models, based on patient and tumor characteristics, predict risk for adverse outcomes after nephrectomy. Changes in renal tumor characteristics over the last decades, warrant further evaluation using contemporary cohorts. We aimed to validate a previously published preoperative nomogram predicting 12-year metastasis-free probability after nephrectomy for localized renal tumors in a contemporary cohort. PATIENTS AND METHODS: After obtaining institutional review board approval, data of 1,760 patients who underwent nephrectomy for a localized renal mass between 2005 and 2011 were reviewed. Preoperative images were evaluated for the presence of tumor necrosis, lymphadenopathy, and tumor size. The study outcome was metastatic-free probability. Model discrimination was assessed with Gönen and Heller's concordance probability estimate, and calibration was evaluated. RESULTS: The cohort included 1,102 male and 658 female patients with a median age of 60 years. Most patients presented incidentally (84%). On imaging, 3% had evidence of lymphadenopathy, 55% had necrosis and median tumor diameter was 3.7 cm (interquartile range [IQR]: 2.5, 5.5). Median follow-up in non-metastatic patients was 7.7 years (IQR: 5.3, 9.7). Estimated 12-year metastatic-free probability was 88% (86%-90%). The model showed strong discrimination (concordance probability estimate [CPE]: 0.77), and fair calibration. The time-dependent receiver operating characteristic (ROC) curves showed strong discrimination at all-time points and the area under the curve (AUC) for year 12 was 0.83 (95% Confidence Interval: 0.78-0.89). CONCLUSIONS: We validated the preoperative nomogram of 12-year metastasis-free probability in a contemporary cohort despite different tumor characteristics. Future studies should evaluate the role of preoperative risk stratification in patient selection for neoadjuvant treatment.
Authors: Maria I Carlo; Kyrollis Attalla; Yousef Mazaheri; Sounak Gupta; Onur Yildirim; Samuel J Murray; Devyn T Coskey; Ritesh Kotecha; Chung-Han Lee; Darren R Feldman; Paul Russo; Sujata Patil; Robert J Motzer; Jonathan A Coleman; Jeremy C Durack; Ying-Bei Chen; Oguz Akin; A Ari Hakimi; Martin H Voss Journal: Eur Urol Date: 2022-02-17 Impact factor: 24.267