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

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.

Automated summary

Renal cell carcinoma (RCC) presents histopathological challenges for accurate diagnosis and prognosis. This literature review explores recent advancements in AI applications in RCC pathology, with the aim of improving precision, efficiency, and objectivity in histopathological analysis. AI-powered approaches demonstrate effective identification and classification abilities, enabling accurate diagnosis, grading, and prognosis prediction. Challenges include standardization, generalizability, performance benchmarking, and data integration into clinical workflows. Accurate interpretation of AI decisions by pathologists and robust validation workflows are crucial for enhancing patient care.