ArticleNPJ precision oncology2024
A universal immunohistochemistry analyzer for generalizing AI-driven assessment of immunohistochemistry across immunostains and cancer types.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Artificial intelligence-guided analysis of the tumor microenvironment predicts response to pembrolizumab in rare tumors.Journal for immunotherapy of cancer · 2026Trial
- Comprehensive molecular profiling of combined hepatocellular carcinoma and cholangiocarcinoma reveals distinct Notch signaling subgroups with prognostic significance.Virchows Archiv : an international journal of pathology · 2026Article
- What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.The Journal of pathology · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.Iranian journal of basic medical sciences · 2026Review
- Systems-level analyses and clinical validation highlight CD53 as a diagnostic and prognostic marker in lung adenocarcinoma.Frontiers in cell and developmental biology · 2026Article
- Engineering the Future of ADCs in Non-Small Cell Lung Cancer.Oncology research · 2026Review
- LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.PloS one · 2026Article
- The Role of PD-L1 in Lung Cancer: From Biology to Clinical Application.ImmunoTargets and therapy · 2026Review
- AI-Assisted Diagnostic Evaluation of IHC in Forensic Pathology: A Comparative Study with Human Scoring.Diagnostics (Basel, Switzerland) · 2025Article
- Modeling metastasis - leveraging novel tools to streamline discovery in advanced cancer.Disease models & mechanisms · 2025Review
- Multi-cancer analysis of histopathologic MSI screening based on digital histology image.PloS one · 2025Article
- Applications of artificial intelligence in cancer immunotherapy: a frontier review on enhancing treatment efficacy and safety.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.