Metabolic insights and novel risk score for adherent perinephric fat in partial nephrectomy: results from a prospective study

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Authors: Łukasz Mielczarek, Omar Tayara, Wojciech Malewski, Przemysław Szostek, Paweł Rajwa, Riccardo Giuseppe Bertolo, F. Zattoni, Carlo Prevato, Sławomir Poletajew, Łukasz Nyk, Piotr Kryst
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References: 18

Abstract

PURPOSE: This study aimed to identify preoperative metabolic and radiological predictors of adherent perinephric fat (APF) and to develop a predictive scoring system for its assessment. METHODS: We conducted a prospective study of consecutive patients with renal tumors undergoing open or minimally invasive partial nephrectomy (PN). APF was intraoperatively defined as the need for subcapsular renal dissection to isolate the tumor. Patient characteristics were compared according to APF presence. Multivariable logistic regression analysis was performed, and the resulting model was used to develop a predictive scoring system. RESULTS: A total of 200 patients were included in the analysis, of whom 34 (17%) had APF. On multivariable analysis, presence of perinephric fat stranding (p = 0.003), posterior perinephric fat thickness ≥ 25 mm (p < 0.001), serum urea ≥ 33 mg/dl (p = 0.004), albumin ≤ 4.3 g/dl (p = 0.007), and HDL cholesterol ≤ 53 mg/dl (p = 0.019) were predictors of APF. A model incorporating these five variables achieved an area under the receiver operating characteristic curve of 0.92. These parameters were subsequently integrated into the novel SHARP-U (Stranding, HDL cholesterol, Albumin, Renal Perinephric fat thickness, Urea) score, ranging from 0 to 7, to predict the presence of APF. CONCLUSION: The SHARP-U score provides a simple and reliable tool for preoperative prediction of APF in patients undergoing partial nephrectomy. Early identification of individuals at risk may aid surgical planning and patient counseling. External prospective validation of the SHARP-U score is warranted to confirm its clinical applicability.

Fields of Study & Subjects

Renal cell carcinoma treatment Radiomics and Machine Learning in Medical Imaging Adrenal and Paraganglionic Tumors

Publication details

Journal / source
International Urology and Nephrology
Volume
58
Issue
8
Pages
2973–2981
Publication date
6 February 2026
Language
EN