Nityam Rathi1, Yosuke Yasuda1,2, Worapat Attawettayanon1,3, Diego A Palacios1, Yunlin Ye1,4, Jianbo Li5, Christopher Weight1, Mohammed Eltemamy1, Tarik Benidir1, Robert Abouassaly1, Steven C Campbell6. 1. Center for Urologic Oncology, Glickman Urological and Kidney Institute, Room Q10-120, 9500 Euclid Avenue, Cleveland Clinic, Cleveland, OH, USA. 2. Department of Urology, Tokyo Medical and Dental University, Tokyo, Japan. 3. Division of Urology, Department of Surgery, Faculty of Medicine, Songklanagarind Hospital, Prince of Songkla University, Songkhla, Thailand. 4. Department of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China. 5. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA. 6. Center for Urologic Oncology, Glickman Urological and Kidney Institute, Room Q10-120, 9500 Euclid Avenue, Cleveland Clinic, Cleveland, OH, USA. campbes3@ccf.org.
Abstract
INTRODUCTION: Radical nephrectomy (RN) is an important consideration for the management of localized renal-cell-carcinoma (RCC) whenever the tumor appears aggressive, although reduced renal function is a concern. Split-renal-function (SRF) in the contralateral kidney and postoperative renal functional compensation (RFC) are fundamentally important for the accurate prediction of new baseline GFR (NBGFR) post-RN. SRF can be estimated either from nuclear renal scans (NRS) or from preoperative imaging using parenchymal-volume-analysis (PVA). We compare two SRF-based models for predicting NBGFR after RN with a subjective prediction of NBGFR by an experienced urologic-oncologist. METHODS: 187 RCC patients managed with RN (2006-16) were included based on the availability of preoperative CT/MRI and NRS, and preoperative/postoperative eGFR. NBGFR was defined as the final GFR 3-12 months post-RN. For the SRF-based approaches, SRF was derived from either NRS or PVA, and RFC was estimated at 25% based on previous independent analyses. Thus, the formula (Global GFRPre-RN × SRFcontralateral) × 1.25 was used to predict NBGFR after RN. For subjective-assessment, a blinded, independent urologic oncologist provided NBGFR predictions based on preoperative eGFR, CT/MRI, and clinical/tumor characteristics. Predictive accuracies were assessed by correlation coefficients (r). RESULTS: The r values for subjective-assessment, NRS/SRF-based, and PVA/SRF-based approaches were 0.72/0.72/0.85, respectively (p < 0.05). The PVA/SRF-based model also demonstrated significant improvement across other performance parameters. CONCLUSIONS: The PVA/SRF-based model more accurately predicts NBGFR post-RN than NRS/SRF-based and Subjective Estimation. PVA software (Fujifilm-medical-systems) is readily available and affordable and provides accurate SRF estimations from routine preoperative imaging. This novel approach may inform clinical management regarding RN/PN for complex RCC cases.
INTRODUCTION: Radical nephrectomy (RN) is an important consideration for the management of localized renal-cell-carcinoma (RCC) whenever the tumor appears aggressive, although reduced renal function is a concern. Split-renal-function (SRF) in the contralateral kidney and postoperative renal functional compensation (RFC) are fundamentally important for the accurate prediction of new baseline GFR (NBGFR) post-RN. SRF can be estimated either from nuclear renal scans (NRS) or from preoperative imaging using parenchymal-volume-analysis (PVA). We compare two SRF-based models for predicting NBGFR after RN with a subjective prediction of NBGFR by an experienced urologic-oncologist. METHODS: 187 RCC patients managed with RN (2006-16) were included based on the availability of preoperative CT/MRI and NRS, and preoperative/postoperative eGFR. NBGFR was defined as the final GFR 3-12 months post-RN. For the SRF-based approaches, SRF was derived from either NRS or PVA, and RFC was estimated at 25% based on previous independent analyses. Thus, the formula (Global GFRPre-RN × SRFcontralateral) × 1.25 was used to predict NBGFR after RN. For subjective-assessment, a blinded, independent urologic oncologist provided NBGFR predictions based on preoperative eGFR, CT/MRI, and clinical/tumor characteristics. Predictive accuracies were assessed by correlation coefficients (r). RESULTS: The r values for subjective-assessment, NRS/SRF-based, and PVA/SRF-based approaches were 0.72/0.72/0.85, respectively (p < 0.05). The PVA/SRF-based model also demonstrated significant improvement across other performance parameters. CONCLUSIONS: The PVA/SRF-based model more accurately predicts NBGFR post-RN than NRS/SRF-based and Subjective Estimation. PVA software (Fujifilm-medical-systems) is readily available and affordable and provides accurate SRF estimations from routine preoperative imaging. This novel approach may inform clinical management regarding RN/PN for complex RCC cases.
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