Abstract 5969: Enhancing interobserver agreement in RENAL nephrometry scoring through the integration of center-based endophycity measurements
Abstract INTRODUCTION: Since its introduction, the RENAL nephrometry score has proven to be an effective tool in medical decision-making and surgical planning for renal tumors and has been shown to be predictive of several important oncologic and operative outcomes. However, some studies have reported high interobserver variability, particularly within the endophycity or “E” portion of RENAL score. This variability can undermine the utility of the RENAL score in clinical management, as it may lead to inconsistent risk stratification and treatment recommendations. Here, we propose the use of center-based endophycity (CBE) scoring in place of the “E” score within RENAL as a means of facilitating interobserver agreement while maintaining utility. METHODS: Medical records for patients who underwent radical or partial nephrectomy for suspected renal malignancy at a single institution over a 9-year span were retrospectively collected. Exclusion criteria included unavailable imaging, multifocal disease, and incomplete pathology data. Preoperative imaging was evaluated with six independent observers manually assigning RENAL scores to each case, and three observers assigning CBE scores. CBE scores were determined by approximating the center of the tumor and assessing whether it was interior or exterior to the corticomedullary boundary of the kidney. Exophytic and endophytic tumors were given a score of 1 and 2 respectively. These scores were then integrated in place of the “E” score of RENAL for analysis. Kappa coefficient was used to analyze interobserver agreement, and logistic regression was used for predicting outcomes. RESULTS: A total of 288 cases met the inclusion criteria for this study. CBE scoring (kappa coefficient 0.77) had significantly higher interobserver agreement than RENAL “E” (kappa coefficient 0.17, p < 0.0001). Both traditional RENAL and RENAL with CBE were predictive of ISUP grade (p < 0.0001, p < 0.0001), tumor necrosis (p < 0.0001, p < 0.0001), and perioperative blood loss (p = 0.001, p < 0.0001). Traditional RENAL was predictive of malignancy (p = 0.001), while RENAL with CBE was not (p = 0.058). However, CBE independently predicted malignancy (p = 0.039), whereas RENAL “E” did not (p = 0.365). CONCLUSIONS: These results suggest that the RENAL “E” score is prone to particularly low interobserver agreement. When integrated in place of RENAL “E”, CBE scoring represents a promising modification to the RENAL nephrometry score, offering a more reproducible method for assessing endophycity in renal tumors while preserving much of the predictive power of the original model. These findings have important implications for improving the accuracy and consistency of preoperative renal tumor assessments. Further studies are needed to elucidate currently insignificant yet promising predictive data points from this study and to confirm our findings in other cohorts. Citation Format: Keenan Moore, Nicholas Heller, Clara Goebel, Jayant Siva, Jamee Schoephoerster, Griffin Struyk, Nitin Venkatesh, Beatriz Lopez Morato, Christopher Weight. Enhancing interobserver agreement in RENAL nephrometry scoring through the integration of center-based endophycity measurements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5969.
Authors
- Nitin Venkatesh
- Nicholas Heller (ORCID: https://orcid.org/0000-0001-8516-8707)
- Keenan Moore
- Griffin Struyk
- Jamee Schoephoerster
- Christopher Weight (ORCID: https://orcid.org/0000-0001-9381-959X)
- Beatriz López Morato
- Jayant Siva
- Clara Goebel
Institutions
- University of Vermont (US)
- Regions Hospital (US)
- Cleveland Clinic (US)
- Oregon Health & Science University (US)
- Oakland University (US)
- Loyola University Chicago (US)
- Beaumont Hospital, Troy (US)
- HealthPartners (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-5969
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00