Deep Learning-Enhanced Biomarker Interpretation on Cytology Cell Blocks: Foundations and Emerging Opportunities in Spatial Pathobiology

Clinical Snapshot

Key Findings

• P Value: Not reported at the review level

• Effect Size: Not applicable — no primary data collected; no pooled effect sizes reported. Individual primary studies discussed within the review report variable model performance metrics (e.g., concordance with pathologist scoring, AUC values) but these are not aggregated

• Primary Outcome: Narrative synthesis of deep learning applications for biomarker interpretation (PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, p16) on cytology cell block specimens, with discussion of spatial pathobiology opportunities

• Nnt Or Sensitivity: Not applicable at the review level. Individual studies discussed may report sensitivity/specificity for diagnostic biomarker tasks, but no aggregate diagnostic accuracy metrics are synthesised

• Confidence Interval: Not reported — no meta-analytic synthesis performed

Clinical Application

Clinical implementation of deep learning biomarker interpretation on cell blocks remains largely in the research and validation phase. Feasibility depends on availability of whole-slide imaging infrastructure, validated and regulatory-approved software platforms, standardised cell block preparation protocols, and trained computational pathology support. Workflow integration into existing laboratory information systems presents a practical barrier in most centres. The review does not provide implementation roadmaps or cost-effectiveness data In Australia, cytology cell blocks are routinely used in the workup of advanced malignancies, particularly for PD-L1 testing to guide pembrolizumab eligibility (PBS-listed for NSCLC, TNBC, cervical cancer, and other indications) and HER2 testing for trastuzumab and pertuzumab eligibility. The TGA has not yet approved specific deep learning biomarker interpretation tools for cytology cell blocks as of the publication date, though the TGA's Software as a Medical Device (SaMD) framework would govern such approvals. The RCPA (Royal College of Pathologists of Australasia) has published guidelines on digital pathology implementation, and the Australian Digital Health Agency's frameworks would be relevant to clinical deployment. Regional and rural pathology services in Australia, which may have limited subspecialty cytopathology expertise, represent a particularly compelling use case for validated deep learning biomarker assistance. PBS reimbursement structures do not currently accommodate computational pathology add-on services, representing a health economics barrier to adoption. Patients with advanced-stage solid malignancies (including lung, breast, melanoma, cervical, and other cancers) where cytology cell block specimens represent the primary or sole tissue source for therapeutic biomarker evaluation. Particularly relevant for patients with metastatic disease, pleural effusions, ascites, or endobronchial ultrasound-guided fine needle aspiration samples

Abstract

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.

References

1. 1.Xia, R., Littlefield, N. G., Park, C. Y., Bao, R., Cangiarella, J., Simsir, A., & Gu, Q. (2026). Deep learning-enhanced biomarker interpretation on cytology cell blocks: Foundations and emerging opportunities in spatial pathobiology. The American Journal of Pathology. Advance online publication. https://doi.org/10.1016/j.ajpath.2026.02.004