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

Abstract

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. METHODS: Aspirates of metastatic carcinoma that had IHC performed were retrieved. Clinicocytologic parameters, IHC results, the corresponding primary site, and histologic diagnoses were recorded. By using machine learning, decision trees for predicting the primary site were generated, their performance was compared with 2 human-designed algorithms, and the primary site was suggested in the historical diagnosis. RESULTS: In total, 1145 cases were identified. The 6 most populated groups were selected for machine learning and predictive analysis. With IHC input, the decision tree achieved a concordance rate of 94.5% and overall accuracy of 83.6%, which improved to 95.3% and 85.8%, respectively, when clinical data were incorporated and exceeded the human-designed IHC algorithms (P <.001). The historical diagnosis was more accurate unless indeterminate diagnoses were regarded as discordant (P <.001). CDX2 and TTF-1 immunostains had the highest weight in model accuracy, occupied the root of the decision trees, scored higher as features of importance, and outperformed the predictive power of cytokeratins 7 and 20. CONCLUSIONS: Cytokeratins 7 and 20 may be superseded in immunostaining panels, including organ-specific immunostains such as CDX2 and TTF-1. Machine learning generates algorithms that surpasses human-designed algorithms but is inferior to expert assessment integrating clinical and cytologic assessment.;.