Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917737318580224 |
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| author | Brattoli, Biagio Mostafavi, Mohammad Lee, Taebum Jung, Wonkyung Ryu, Jeongun Park, Seonwook Park, Jongchan Pereira, Sergio Shin, Seunghwan Choi, Sangjoon Kim, Hyojin Yoo, Donggeun Ali, Siraj M. Paeng, Kyunghyun Ock, Chan-Young Cho, Soo Ick Kim, Seokhwi |
| author_facet | Brattoli, Biagio Mostafavi, Mohammad Lee, Taebum Jung, Wonkyung Ryu, Jeongun Park, Seonwook Park, Jongchan Pereira, Sergio Shin, Seunghwan Choi, Sangjoon Kim, Hyojin Yoo, Donggeun Ali, Siraj M. Paeng, Kyunghyun Ock, Chan-Young Cho, Soo Ick Kim, Seokhwi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer Brattoli, Biagio Mostafavi, Mohammad Lee, Taebum Jung, Wonkyung Ryu, Jeongun Park, Seonwook Park, Jongchan Pereira, Sergio Shin, Seunghwan Choi, Sangjoon Kim, Hyojin Yoo, Donggeun Ali, Siraj M. Paeng, Kyunghyun Ock, Chan-Young Cho, Soo Ick Kim, Seokhwi Computer Vision and Pattern Recognition 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. |
| title | Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.20643 |