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The role of cytokeratin 7/20 coordination revisited—Machine learning identifies improved interpretative algorithms for cell block immunohistochemistry in aspirates of metastatic carcinoma

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Fine‐needle aspiration (FNA) is a robust diagnostic technique often used for tissue diagnosis of metastatic carcinoma. For interpretation of FNA cytology, cell block immunohistochemistry (IHC) and clinicocytologic parameters are indispensable.… Click to show full abstract

Fine‐needle aspiration (FNA) is a robust diagnostic technique often used for tissue diagnosis of metastatic carcinoma. For interpretation of FNA cytology, cell block immunohistochemistry (IHC) and clinicocytologic parameters are indispensable. In this review of a large cohort, the current report: 1) describes clinicocytologic parameters and immunoprofiles of aspirates of metastatic carcinoma, 2) compares the predictivity of immunostains and classical approaches for IHC interpretation, and 3) describes machine learning‐based algorithms for IHC interpretation.

Keywords: block immunohistochemistry; machine learning; aspirates metastatic; cell block; metastatic carcinoma; carcinoma

Journal Title: Cancer Cytopathology
Year Published: 2022

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