Open Access

ARTICLE

AI-driven radiogenomic analysis of clear cell renal cell carcinoma: perinephric adipose tissue stranding as a key feature of the NIPAL4-associated imaging pattern

Federico Greco^1,2,*, Marco Cataldo³, Valerio D’Andrea^2,4, Luca Pugliese⁵, Andrea Panunzio⁶, Alessandro Tafuri⁶, Bruno Beomonte Zobel^2,4, Carlo Augusto Mallio^2,4

1 Department of Radiology, Cittadella della Salute, Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi, 2, Lecce, 73100, Italy
2 Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, Roma, 00128, Italy
3 Apphia srl, via per Monteroni, Lecce, 73100, Italy
4 Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, Roma, 00128, Italy
5 Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Radiology Unit, Sant’Andrea University Hospital, Via di Grottarossa, 1035–1039, Roma, 00189, Italy
6 Department of Urology, “Vito Fazzi” Hospital, Piazza Filippo Muratore, 1, Lecce, 73100, Italy

* Corresponding Authors: Federico Greco. Email: ,

(This article belongs to the Special Issue: AI, Radiomics, and Radiogenomics in Urologic Oncology: Toward a New Era of Precision Imaging)

Canadian Journal of Urology 2025, 32(5), 433-443. https://doi.org/10.32604/cju.2025.068390

Received 28 May 2025; Accepted 22 August 2025; Issue published 30 October 2025

Abstract

Background: Radiogenomics offers a non-invasive approach to correlate imaging features with tumor molecular profiles. This study aims to identify computed tomography (CT) imaging characteristics associated with positive NIPA-like domain containing 4 (NIPAL4) expression in clear cell renal cell carcinoma (ccRCC) and to develop a radiogenomic predictive model to support personalized risk stratification. Methods: A retrospective analysis was conducted on 241 ccRCC patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases. Clinical, pathological, and CT features were compared between NIPAL4-positive and NIPAL4-negative groups. A penalized logistic regression model was built to predict NIPAL4 expression, and its performance was assessed using Receiver Operating Characteristic (ROC) and Decision Curve Analysis (DCA). Additionally, unsupervised K-means clustering was used to identify radiologic phenotypes, and a nomogram was developed to enable individualized risk estimation. Results: Among 241 ccRCC patients, 29 (12.03%) showed positive NIPAL4 expression. Compared to NIPAL4-negative cases, positive expression was significantly associated with larger tumor size (median 70.5 mm vs. 52 mm, p = 0.0371), ill-defined margins (61.5% vs. 32.4%, p = 0.0077), perinephric adipose tissue stranding (76.9% vs. 50.0%, p = 0.0114), renal vein thrombosis (24.0% vs. 4.7%, p = 0.021), Gerota’s fascia thickening (61.5% vs. 35.2%, p = 0.0163), and collecting system invasion (52.0% vs. 26.5%, p = 0.0171). A multivariate penalized logistic regression model incorporating these features achieved an AUC of 0.973% and 92.1% accuracy in predicting NIPAL4 positivity. Conclusions: Positive NIPAL4 expression in ccRCC is significantly associated with aggressive CT features—particularly perinephric adipose tissue stranding, ill-defined margins, and renal vein thrombosis. A radiogenomic model based on these features achieved excellent predictive performance (AUC = 0.973), supporting its potential role in non-invasive risk stratification and personalized clinical decision-making.

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