Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer

TL;DR

This paper introduces a universal AI model for interpreting diverse immunohistochemistry images across various cancer types, improving accuracy and versatility in diagnostics and biomarker assessment.

Contribution

The development of a multi-cohort trained AI model that generalizes across different IHC stains and cancer types, outperforming traditional single-cohort models.

Findings

Outperforms single-cohort models in unseen IHC interpretation (Kappa 0.578 vs. 0.509)

Consistently superior across different positive staining cutoffs

Effectively clusters expression levels and assesses biomarkers like c-MET

Abstract

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.