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

Background: 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.