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Low grade oncocytic tumors of the kidney: a clinically relevant approach for the workup and accurate diagnosis.

Mahul B AminJesse K McKenneyGuido MartignoniSteven C CampbellSumanta Kumar PalSatish K Tickoo
Published in: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc (2022)
Renal oncocytoma and chromophobe renal cell carcinoma were accepted as unique renal tumors in the late 1990s. Since their formal description, criteria for diagnosis have evolved and additional distinct tumor subtypes originally considered as one these two entities are now recognized. The last two decades have witnessed unprecedented interest in the spectrum of low grade oncocytic renal neoplasms in three specific areas: (1) histologic characterization of tumors with overlapping morphologic features between oncocytoma and chromophobe renal cell carcinoma; (2) description of potentially unique entities within this spectrum, such as eosinophilic vacuolated tumor and low-grade oncocytic tumor; and (3) better appreciation of the association between a subset of low grade oncocytic tumors and hereditary renal neoplasia. While this important work has been academically rewarding, the proposal of several histologic entities with overlapping morphologic and immunophenotypic features (which may require esoteric adjunctive immunohistochemical and/or molecular techniques for confirmation) has created frustration in the diagnostic pathology and urology community as information evolves regarding classification within this spectrum of renal neoplasia. Pathologists, including genitourinary subspecialists, are often uncertain as to the "best practice" diagnostic approach to such tumors. In this review, we present a practical clinically relevant algorithmic approach to classifying tumors within the low grade oncocytic family of renal neoplasia, including a proposal for compressing terminology for evolving categories where appropriate without sacrificing prognostic relevance.
Keyphrases
  • low grade
  • high grade
  • renal cell carcinoma
  • healthcare
  • primary care
  • deep learning
  • mental health
  • machine learning
  • high resolution
  • single molecule