How far is AI from clinical application in automated renal cell carcinoma pathological diagnosis？

doi:10.3877/cma.j.issn.1674-3253.2024.06.001

Abstract

Abstract:

Renal cell carcinoma （RCC），one of the most prevalent malignancies in the urinary system，underscores the critical role of pathological diagnosis in disease management and treatment strategy formulation. With the vigorous development of artificial intelligence，particularly the notable breakthroughs of deep learning in medical image processing，intelligent pathological diagnosis for RCC has emerged as a forefront research area. This article systematically reviews the latest advancements in RCC intelligent pathological diagnosis，detailing the current applications of artificial intelligence in histological subtype discrimination，pathological type differentiation，nuclear grade determination，prognosis evaluation，and gene mutation prediction. Despite the achievements made，clinical adoption of RCC intelligent pathological diagnosis faces several challenges，including incomplete coverage of pathological types，limited dataset sizes，low data standardization，insufficient algorithm generalizability，and the absence of prospective external clinical validations. Moving forward，research should prioritize addressing these existing issues while focusing on enhancing model interpretability，building RCC pathological foundation models，and developing multimodal RCC-specific foundation models. These efforts aim to advance the maturity and extensive application of RCC intelligent pathological diagnosis technologies，ultimately providing more personalized，precise，and efficient diagnostic and treatment options for RCC patients.

Key words: Artificial intelligence, Renal cell carcinoma, Pathological diagnosis, Whole slide images, Foundation model

Ying Xiong, Jinglai Lin, Qi Bai, Jianming Guo, Shuo Wang. How far is AI from clinical application in automated renal cell carcinoma pathological diagnosis？[J]. Chinese Journal of Endourology(Electronic Edition), 2024, 18(06): 535-540.

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