A universal immunohistochemistry analyzer for generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types
Immunohistochemistry (IHC) is the common companion diagnostics in targeted therapies. However, quantifying protein expressions in IHC images present a significant challenge, due to variability in manual scoring and inherent subjective interpretation. Deep learning (DL) offers a promising approach to address these issues, though current models require extensive training for each cancer and IHC type, limiting the practical application. We developed a Universal IHC (UIHC) analyzer, a DL-based tool that quantifies protein expression across different cancers and IHC types. This multi-cohort trained model outperformed conventional single-cohort models in analyzing unseen IHC images (Kappa score 0.578 vs. up to 0.509) and demonstrated consistent performance across varying positive staining cutoff values. In a discovery application, the UIHC model assigned higher tumor proportion scores to MET amplification cases, but not MET exon 14 splicing or other non-small cell lung cancer cases. This UIHC model represents a novel role for DL that further advances quantitative analysis of IHC.
Authors
- Taebum Lee (ORCID: https://orcid.org/0000-0002-9472-0644)
- Sergio Pereira
- Seokhwi Kim (ORCID: https://orcid.org/0000-0001-7646-5064)
- Wonkyung Jung (ORCID: https://orcid.org/0000-0002-2778-8710)
- Chan‐Young Ock (ORCID: https://orcid.org/0000-0002-6755-9728)
- Soo Ick Cho (ORCID: https://orcid.org/0000-0003-3414-9869)
- Seonwook Park (ORCID: https://orcid.org/0000-0001-7992-3876)
- Sangjoon Choi (ORCID: https://orcid.org/0000-0003-2108-0575)
- Kyunghyun Paeng
- Biagio Brattoli (ORCID: https://orcid.org/0000-0002-8482-3375)
- Siraj M. Ali (ORCID: https://orcid.org/0000-0001-7477-4023)
- Hyojin Kim (ORCID: https://orcid.org/0000-0001-9201-8328)
- Jongchan Park (ORCID: https://orcid.org/0000-0001-9808-6823)
- Seung-Hwan Shin (ORCID: https://orcid.org/0000-0002-5664-8848)
- Jeongun Ryu
- Mohammad Mostafavi
- Donggeun Yoo
Institutions
- Samsung Medical Center (KR)
- Seoul National University Bundang Hospital (KR)
- Ajou University (KR)
- Sungkyunkwan University (KR)
- Lac Courte Oreilles Ojibwe University (US)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1038/s41698-024-00770-z
- Citations
- 12
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.85
Funders
- Lunit
- National Research Foundation
- Ajou University
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea