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Review
. 2025 Mar 26;14(7):2272.
doi: 10.3390/jcm14072272.

Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma

Mladen Doykov  1 , Stanislav Valkanov  1 , Usman Khalid  2 , Jasmin Gurung  2 , Gancho Kostov  3 , Bozhidar Hristov  4 , Petar Uchikov  3 , Maria Kraeva  5 , Krasimir Kraev  6 , Daniel Doykov  4 , Katya Doykova  7 , Siyana Valova  8 , Lyubomir Chervenkov  7 , Stefan Konsulov  5
Affiliations
Review

Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma

Mladen Doykov et al. J Clin Med. .

Abstract

Background: Advancements in artificial intelligence (AI) diagnostics for renal cell carcinoma (RCC) provide valuable information for classification and subtyping, which improve treatment options and patient care. RCC diagnoses are most commonly incidental due to a lack of specific characterizations of subtypes, often leading to overtreatment. Accurate diagnosis also allows for personalized patient management. Different diagnostic methods, such as histopathology, multi-omics, imaging, and perioperative diagnostics, show a lot of promise for AI. Objective: This literature review focuses on developments in RCC diagnostics and their outcomes, efficacy, and accuracy in classification. Method: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of RCC. The PubMed and Google Scholar databases were reviewed to extract relevant information. The literature shows that AI can help distinguish RCC from other kidney lesions and track tumor growth. The integration of radiomic features with clinical metadata further enhances the results. This enables clinicians to implement personalized treatment plans. The application of artificial intelligence in perioperative diagnostics enhances decision-making, improves patient safety, mitigates intraoperative complications, and accelerates recovery. Alongside the advancements in AI-assisted diagnostics, there are problems that need to be addressed, including selection bias, demand for larger and diverse datasets, and reliable validation. Conclusions: Despite the challenges, using AI to help with RCC diagnosis could lead to better patient outcomes, a new standard of care for RCC patients, and more personalized cancer management for each patient.

Keywords: artificial intelligence; diagnostics; histology; imaging; multi-omics; perioperative diagnostics; renal cell carcinoma.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Areas of AI-enhanced diagnostics for RCC identified in the reviewed literature.
See this image and copyright information in PMC

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