Deep Learning–Enhanced Biomarker Interpretation on Cytology Cell Blocks

Cytology cell block specimens are essential for diagnosing patients with advanced-stage malignancy and often represents the only available strategy for therapeutic biomarker evaluation. The use of cell blocks preserves tumor cells, captures high-grade or metastatic populations, and retains meaningful microenvironmental context, making them well suited for IHC analysis. With the rapid expansion of computational pathology, deep learning-assisted biomarker interpretation in cell blocks is emerging as a promising frontier for improving reproducibility, reducing interobserver variability, and enabling quantitative assessment of spatial tumor-immune interactions. Because many treatment-defining biomarkers are routinely assessed on cytology cell blocks, this mini-review highlights artificial intelligence-based applications for PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16 markers that directly inform decisions about immunotherapy, targeted therapy, and hormone therapy. Also reviewed are emerging predictive models that convey biomarker status directly from morphology, extending the utility of artificial intelligence beyond conventional IHC interpretation. Finally, spatial pathobiology-related opportunities afforded by cell block preparations are discussed, and future directions for integrating artificial intelligence-enabled analysis into cytology workflows are outlined. Together, these advances position cytology cell blocks as an important platform for computational biomarker interpretation and morphology-driven precision oncology.