<![CDATA[Background: The histopathology of renal cell carcinoma (RCC) encompasses a diverse array of subtypes, each characterized by distinct morphological and genetic features that have significant implications for clinical outcomes and treatment strategies. The evolution of our understanding of RCC has been reflected in a series of pivotal studies over the years, which have contributed to the molecular and histological classification of this malignancy. Literature Review: The exploration of ancillary techniques by (Pradhan et al., 2009) further enriched the discourse on RCC by emphasizing the significance of integrating histomorphology with molecular and cytogenetic analyses. This multifaceted approach is vital for improving diagnostic accuracy and tailoring treatment plans to individual patient needs ((Pradhan et al., 2009)). The comprehensive molecular characterization conducted by The Cancer Genome Atlas ((J. Ricketts et al., 2018)) revealed distinct genetic drivers associated with various RCC subtypes, emphasizing the importance of understanding these alterations for developing effective treatment strategies ((J. Ricketts et al., 2018)). Recent advancements in digital pathology, as discussed by (Zhu et al., 2020) and (Abu Haeyeh et al., 2022), highlight the potential of deep learning techniques to enhance diagnostic accuracy and streamline the classification of RCC subtypes, thus improving clinical outcomes ((Zhu et al., 2020); (Abu Haeyeh et al., 2022)). Conclusion: The analysis of papillary renal cell carcinoma by (S. Chawla et al., 2023) underscores the unique biological characteristics of this subtype and the challenges in advancing therapeutic strategies compared to clear cell RCC ((S. Chawla et al., 2023)). The literature collectively illustrates the dynamic and evolving nature of RCC histopathology, emphasizing the need for ongoing research to refine classification systems and enhance understanding of tumor biology, ultimately aiming to improve patient outcomes.]]>
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