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Review
. 2023 Jul 6;13(13):2294.
doi: 10.3390/diagnostics13132294.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Alfredo Distante  1   2 , Laura Marandino  3 , Riccardo Bertolo  4 , Alexandre Ingels  5 , Nicola Pavan  6 , Angela Pecoraro  7 , Michele Marchioni  8 , Umberto Carbonara  9 , Selcuk Erdem  10 , Daniele Amparore  7 , Riccardo Campi  11 , Eduard Roussel  12 , Anna Caliò  13 , Zhenjie Wu  14 , Carlotta Palumbo  15 , Leonardo D Borregales  16 , Peter Mulders  2 , Constantijn H J Muselaers  2 ; EAU Young Academic Urologists (YAU) Renal Cancer Working Group
Affiliations
Review

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Alfredo Distante et al. Diagnostics (Basel). .

Abstract

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Keywords: artificial intelligence; kidney cancer; pathology; renal cell carcinoma.

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Conflict of interest statement

The authors confirm that there are no conflict of interest with any financial organization regarding the material discussed in this manuscript.

Figures

Figure 1
Figure 1
Pathway for the development of pathomics algorithms. After the sample is via by surgical resection or biopsy, the WSI is created and derived patches utilized through a digital scanner to train the algorithm to define diagnostic, prognostic, or predictive models. Supervised learning-based algorithms could carry the “black box” issue (see Section 6).
Figure 2
Figure 2
Challenges in clinical translation after the development of a new ML algorithm.
See this image and copyright information in PMC

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