Immunohistochemistry

A framework for designing digital pathology systems that balance throughput, budgets, and turnaround times

Review findings from a proof-of-concept study evaluating the performance of an end-to-end digital pathology workflow for high-volume labs

Reduce turnaround time, enhance diagnostic accuracy, and minimize manual labor in immunohistochemistry

Evaluating the evidence for, and against, distinguishing between HER2-low and HER2-zero breast cancers

Deep learning and AI-based decisions are becoming increasingly reliable but remain far from being adapted to cancer diagnostics

Harnessing the power of multiplexing and RNA–protein studies could help design targeted cancer treatments

Pathologist and assistant professor Rebecca Waters, DO, on finding “zebras” and her osteopathic medicine background

AI models uncover patterns across two-dimensional images of tumor samples that could help predict response to immunotherapy

Interlaboratory inconsistency in IHC adversely affects the quality and standard of patient care