  Despite advancements in methodologies, immunohistochemistry (IHC) remains the
most utilized ancillary test for histopathologic and companion diagnostics in
targeted therapies. However, objective IHC assessment poses challenges.
Artificial intelligence (AI) has emerged as a potential solution, yet its
development requires extensive training for each cancer and IHC type, limiting
versatility. We developed a Universal IHC (UIHC) analyzer, an AI model for
interpreting IHC images regardless of tumor or IHC types, using training
datasets from various cancers stained for PD-L1 and/or HER2. This multi-cohort
trained model outperforms conventional single-cohort models in interpreting
unseen IHCs (Kappa score 0.578 vs. up to 0.509) and consistently shows superior
performance across different positive staining cutoff values. Qualitative
analysis reveals that UIHC effectively clusters patches based on expression
levels. The UIHC model also quantitatively assesses c-MET expression with MET
mutations, representing a significant advancement in AI application in the era
of personalized medicine and accumulating novel biomarkers.
