ARTIFICIAL INTELLIGENCE IN EARLY DETECTION AND MANAGEMENT OF BENIGN UROLOGICAL TUMORS-A NARRATIVE REVIEW
DOI:
https://doi.org/10.71000/ehan4e80Keywords:
Artificial Intelligence, Benign Urological Tumors, Kidney Imaging, Bladder Cancer, Radiomics, Narrative ReviewAbstract
Background: Benign urological tumors of the kidney and bladder, such as renal oncocytomas, angiomyolipomas, and non-muscle invasive bladder lesions, are increasingly diagnosed due to widespread use of imaging modalities. Differentiating these lesions from malignant counterparts remains a clinical challenge, often leading to unnecessary interventions. Artificial intelligence (AI) has emerged as a transformative tool in urology, offering advanced diagnostic support through radiomics, machine learning, and image analysis.
Objective: This narrative review aims to explore the current applications, limitations, and future potential of AI in the early detection and management of benign kidney and bladder tumors, focusing on its integration within medical imaging and urological practice.
Main Discussion Points: Key themes include the role of AI-enhanced radiological imaging in differentiating benign from malignant renal masses, AI-guided cystoscopy and histopathology for improved bladder tumor assessment, predictive analytics for individualized surveillance, and intraoperative AI tools for surgical planning. The review also discusses limitations such as small sample sizes, methodological variability, and issues related to generalizability and data standardization.
Conclusion: AI shows promising potential in improving diagnostic accuracy and clinical decision-making in benign urological tumors. However, the evidence base is still developing and limited by methodological weaknesses. Larger, multicenter prospective studies are necessary to validate current findings and support safe, widespread clinical implementation.
References
Kowalewski K, Egen L, Fischetti C, Puliatti S, Juan GR, Taratkin M, et al. Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian Journal of Urology. 2022;9:243-52.
Xiong Y, Yao L, Lin J, Yao J, Bai Q, Huang Y, et al. Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses. Nat Commun. 2025;16(1):1425.
Zhu X, Qin R, Qu K, Wang Z, Zhao X, Xu W. Atomic force microscopy-based assessment of multimechanical cellular properties for classification of graded bladder cancer cells and cancer early diagnosis using machine learning analysis. Acta Biomater. 2023;158:358-73.
Jhumka K, Khan MH-M, Mungloo-Dilmohamud Z, Fedally S. Classification of kidney abnormalities using deep learning with explainable AI. 2023 Sixth International Conference of Women in Data Science at Prince Sultan University (WiDS PSU). 2023:133-7.
Chen S, Jiang L, Zheng X, Shao J, Wang T, Zhang E, et al. Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci. 2021;112(7):2905-14.
Wei Z, Xv Y, Liu H, Li Y, Yin S, Xie Y, et al. A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study. Int J Surg. 2024;110(5):2922-32.
Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng. 2021;5(6):555-70.
Dai C, Xiong Y, Zhu P, Yao L, Lin J, Yao J, et al. Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT. Radiology. 2024;311(2):e232178.
Koo KC, Yoo JW, Lee KS, Chung BH. Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method. European Urology. 2023.
Chen S, Wang X, Zhang J, Jiang L, Gao F, Xiang J, et al. Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images. Pathology. 2024;56(7):951-60.
Chen S, Gao F, Guo T, Jiang L, Zhang N, Wang X, et al. Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study. Int J Surg. 2024;110(5):2970-7.
Lenis AT, Litwin MS. Does Artificial Intelligence Meaningfully Enhance Cystoscopy? J Natl Cancer Inst. 2022;114(2):174-5.
Feretzakis G, Juliebø-Jones P, Tsaturyan A, Şener T, Verykios V, Karapiperis D, et al. Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review. Cancers. 2024;16.
Saliby RM, Labaki C, Jammihal TR, Xie W, Sun M, Shah V, et al. Impact of renal cell carcinoma molecular subtypes on immunotherapy and targeted therapy outcomes. Cancer Cell. 2024;42(5):732-5.
Li S, Zhou Z, Gao M, Liao Z, He K, Qu W, et al. Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study. Int J Surg. 2024;110(7):4221-30.
Cheng G, Zhou Z, Li S, Ye Z, Wang Y, Wen J, et al. Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma. Int J Med Sci. 2024;21(11):2215-32.
Song DM, Shen T, Feng K, He YB, Chen SL, Zhang Y, et al. LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing. Front Immunol. 2024;15:1419126.
Wang Z, Chen DN, Huang XY, Zhu JM, Lin F, You Q, et al. Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer. Int Immunopharmacol. 2024;138:112623.
Xiong S, Fu Z, Deng Z, Li S, Zhan X, Zheng F, et al. Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models. Med Phys. 2024;51(9):5965-77.
Meng XY, Zhou XH, Li S, Shi MJ, Li XH, Yang BY, et al. Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features. Clin Chem. 2024;70(12):1463-73.
Motzer RJ, Banchereau R, Hamidi H, Powles T, McDermott D, Atkins MB, et al. Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade. Cancer Cell. 2020;38(6):803-17.e4.
Huang KB, Gui CP, Xu YZ, Li XS, Zhao HW, Cao JZ, et al. A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma. Nat Commun. 2024;15(1):6215.
Roussel E, Capitanio U, Kutikov A, Oosterwijk E, Pedrosa I, Rowe SP, et al. Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review. Eur Urol. 2022;81(5):476-88.
Li H, Wang F, Huang W. A Novel, Simple, and Low-Cost Approach for Machine Learning Screening of Kidney Cancer: An Eight-Indicator Blood Test Panel with Predictive Value for Early Diagnosis. Curr Oncol. 2022;29(12):9135-49.
Zheng Z, Dai F, Liu J, Zhang Y, Wang Z, Wang B, et al. Pathology-based deep learning features for predicting basal and luminal subtypes in bladder cancer. BMC Cancer. 2025;25(1):310.
Timofeeva E, Azilgareeva С, Morozov A, Taratkin M, Enikeev D. Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer. Urology Herald. 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Zainab Yousaf, Ammar Khalil, Syed Gufran Sadiq Zaidi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
