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Agarwal DK, Mulholland C, Koye DN, Sathianathen N, Yao H, Dundee P, Moon D, Furrer M, Giudice C, Wang W, Simpson JA, Kearsley J, Norris B, Zargar H, Pan HY, Agarwal A, Lawrentschuk N, Corcoran NM. RPN (Radius, Position of tumour, iNvasion of renal sinus) Classification and Nephrometry Scoring System: An Internationally Developed Clinical Classification To Describe the Surgical Difficulty for Renal Masses for Which Robotic Partial Nephrectomy Is Planned. EUR UROL SUPPL 2023; 54:33-42. [PMID: 37545848 PMCID: PMC10397239 DOI: 10.1016/j.euros.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 08/08/2023] Open
Abstract
Background The surgical difficulty of partial nephrectomy (PN) varies depending on the operative approach. Existing nephrometry classifications for assessment of surgical difficulty are not specific to the robotic approach. Objective To develop an international robotic-specific classification of renal masses for preoperative assessment of surgical difficulty of robotic PN. Design setting and participants The RPN classification (Radius, Position of tumour, iNvasion of renal sinus) considers three parameters: tumour size, tumour position, and invasion of the renal sinus. In an international survey, 45 experienced robotic surgeons independently reviewed de-identified computed tomography images of 144 patients with renal tumours to assess surgical difficulty of robot-assisted PN using a 10-point Likert scale. A separate data set of 248 patients was used for external validation. Outcome measurements and statistical analysis Multiple linear regression was conducted and a risk score was developed after rounding the regression coefficients. The RPN classification was correlated with the surgical difficulty score derived from the international survey. External validation was performed using a retrospective cohort of 248 patients. RPN classification was also compared with the RENAL (Radius; Exophytic/endophytic; Nearness; Anterior/posterior; Location), PADUA (Preoperative Aspects and Dimensions Used for Anatomic), and SPARE (Simplified PADUA REnal) scoring systems. Results and limitation The median tumour size was 38 mm (interquartile range 27-49). The majority (81%) of renal tumours were peripheral, followed by hilar (12%) and central (7.6%) locations. Noninvasive and semi-invasive tumours accounted for 37% each, and 26% of the tumours were invasive. The mean surgical difficulty score was 5.2 (standard deviation 1.9). Linear regression analysis indicated that the RPN classification correlated very well with the surgical difficulty score (R2 = 0.80). The R2 values for the other scoring systems were: 0.66 for RENAL, 0.75 for PADUA, and 0.70 for SPARE. In an external validation cohort, the performance of all four classification systems in predicting perioperative outcomes was similar, with low R2 values. Conclusions The proposed RPN classification is the first nephrometry system to assess the surgical difficulty of renal masses for which robot-assisted PN is planned, and is a useful tool to assist in surgical planning, training and data reporting. Patient summary We describe a simple classification system to help urologists in preoperative assessment of the difficulty of robotic surgery for partial kidney removal for kidney tumours.
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Affiliation(s)
- Dinesh K. Agarwal
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
- Department of Urology, Western Health, Melbourne, Australia
- Department of Urology, Mercy Health, Melbourne, Australia
| | - Clancy Mulholland
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Digsu N. Koye
- Centre for Epidemiology and Biostatics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | | | - Henry Yao
- Department of Urology, Western Health, Melbourne, Australia
| | - Philip Dundee
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Daniel Moon
- University of Melbourne, Royal Melbourne Clinical School, Melbourne, Australia
| | - Marc Furrer
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
- Urology Centre, Guy’s and St. Thomas’ Hospitals NHS Trust, London, UK
- Urology Unit, Die Berner Urologen AG, Bern, Switzerland
| | - Christina Giudice
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Wayland Wang
- Department of Radiology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Julie A. Simpson
- Centre for Epidemiology and Biostatics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Jamie Kearsley
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Briony Norris
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Homi Zargar
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Henry Y.C. Pan
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Ashwin Agarwal
- St. Vincent’s Clinical School, University of Melbourne, Melbourne, Australia
| | - Nathan Lawrentschuk
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
- University of Melbourne, Royal Melbourne Clinical School, Melbourne, Australia
- Victorian Comprehensive Cancer Centre, Melbourne, Australia
| | - Niall M. Corcoran
- Department of Urology, The Royal Melbourne Hospital, Melbourne, Australia
- Department of Urology, Western Health, Melbourne, Australia
- University of Melbourne, Royal Melbourne Clinical School, Melbourne, Australia
- Victorian Comprehensive Cancer Centre, Melbourne, Australia
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Gallo F, Sforza S, Mari A, Luciani L, Schenone M, Minervini A. Robotic Partial Nephrectomy for Bilateral Renal Masses. Curr Urol Rep 2023; 24:157-163. [PMID: 36538282 DOI: 10.1007/s11934-022-01143-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2022] [Indexed: 12/25/2022]
Abstract
PURPOSE OF REVIEW There are very few data on patients undergoing robot-assisted partial nephrectomy (RAPN) for bilateral renal masses. The aim of this review is to update the literature and discuss the controversial points on this topic. RECENT FINDINGS Nine papers have been published regarding RAPN for bilateral renal masses. In particular, five papers were case reports while the remaining four reported patient series. Concerning the outcomes, all these papers highlighted the safety, feasibility, and efficacy of bilateral RAPN for bilateral renal masses. The literature confirmed RAPN as an optimal procedure for the treatment of bilateral renal masses. However, these outcomes mainly derived from selected group of patients who underwent complex surgical procedures by expert robotic surgeons at high volume centers and cannot be generalizable to all categories of patients or centers. The simultaneous bilateral approach resulted feasible showing some advantages and without higher complications than a staged procedure in particular when clampless or selective clamping techniques were performed.
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Affiliation(s)
- Fabrizio Gallo
- Department of Urology, San Paolo Hospital, Savona, Italy.
| | - Simone Sforza
- Department of Urology, Careggi University Hospital, Florence, Italy
| | - Andrea Mari
- Department of Urology, Careggi University Hospital, Florence, Italy
| | | | | | - Andrea Minervini
- Department of Urology, Careggi University Hospital, Florence, Italy
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Chen Y, Ma T, Cong L, Xu J, Huang C, Ma Q, Hua Q, Li X, Huang Z, Wang X. Computed tomography-based radiomics nomogram model for predicting adherent perinephric fat. J Cancer Res Ther 2022; 18:336-344. [DOI: 10.4103/jcrt.jcrt_1425_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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