Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction
Immuno-oncology and the advent of immunotherapies, in particular immune checkpoint inhibitors (ICIs), have fundamentally altered the way we treat cancer. Yet only a small subset of patients actually responds to ICIs, and many face significant adverse effects, making the accurate selection of patient...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2025-08-01
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| Series: | Journal for ImmunoTherapy of Cancer |
| Online Access: | https://jitc.bmj.com/content/13/8/e011346.full |
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| author | Yeseul Kim Eugene Kim Young Kwang Chae Jaeyoun Choi Chan Mi Jung Christmann Low Ju Young Lee Jonghanne Park Sukjoo Cho Lee Cooper Jessica Zhang Horyun Choi Allen Cho Emma J Yu Jeffrey H Chuang |
| author_facet | Yeseul Kim Eugene Kim Young Kwang Chae Jaeyoun Choi Chan Mi Jung Christmann Low Ju Young Lee Jonghanne Park Sukjoo Cho Lee Cooper Jessica Zhang Horyun Choi Allen Cho Emma J Yu Jeffrey H Chuang |
| author_sort | Yeseul Kim |
| collection | DOAJ |
| description | Immuno-oncology and the advent of immunotherapies, in particular immune checkpoint inhibitors (ICIs), have fundamentally altered the way we treat cancer. Yet only a small subset of patients actually responds to ICIs, and many face significant adverse effects, making the accurate selection of patients for ICIs essential to the work of immuno-oncology. Immune biomarkers, such as programmed death-ligand 1, microsatellite instability/defective mismatch repair, and tumor mutational burden have been developed for patient selection and stratification for ICIs, though their predictive abilities remain limited. This is due to several challenges: lack of adequate tissue sampling, the time-consuming and subjective nature of manual visual-based quantification techniques, and the growing recognition of the complexity of the tumor microenvironment, for which these tests cannot fully capture on their own. Meanwhile, emerging technologies in the field of artificial intelligence (AI), such as the performance of deep learning techniques in digital pathology, have garnered significant attention for their potential to be used in this space. Many have now turned their attention towards the immuno-oncology-related applications for digital pathology, particularly in analyzing whole-slide images of widely available H&E-stained slides to aid in immune biomarker detection and ICI response prediction. In this review, we discuss the current landscape of AI-based digital pathology in immuno-oncology, including its applications for identifying and measuring immune biomarkers and, importantly, its potential for predicting ICI response and survival outcomes. We will end by discussing the challenges and future directions of adopting AI technologies for clinical deployment. |
| format | Article |
| id | doaj-art-3edfd177f8bd4594884e58ecf042d81a |
| institution | DOAJ |
| issn | 2051-1426 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-3edfd177f8bd4594884e58ecf042d81a2025-08-20T03:02:10ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262025-08-0113810.1136/jitc-2024-011346Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response predictionYeseul Kim0Eugene Kim1Young Kwang Chae2Jaeyoun Choi3Chan Mi Jung4Christmann Low5Ju Young Lee6Jonghanne Park7Sukjoo Cho8Lee Cooper9Jessica Zhang10Horyun Choi11Allen Cho12Emma J Yu13Jeffrey H Chuang14Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USAThe JAX Cancer Center, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USADepartment of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, Florida, USADepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USADepartment of Medicine, University of Hawaii, Honolulu, Hawaii, USAInternal Medicine, Louis A Weiss Memorial Hospital, Chicago, Illinois, USADepartment of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USAThe JAX Cancer Center, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USAImmuno-oncology and the advent of immunotherapies, in particular immune checkpoint inhibitors (ICIs), have fundamentally altered the way we treat cancer. Yet only a small subset of patients actually responds to ICIs, and many face significant adverse effects, making the accurate selection of patients for ICIs essential to the work of immuno-oncology. Immune biomarkers, such as programmed death-ligand 1, microsatellite instability/defective mismatch repair, and tumor mutational burden have been developed for patient selection and stratification for ICIs, though their predictive abilities remain limited. This is due to several challenges: lack of adequate tissue sampling, the time-consuming and subjective nature of manual visual-based quantification techniques, and the growing recognition of the complexity of the tumor microenvironment, for which these tests cannot fully capture on their own. Meanwhile, emerging technologies in the field of artificial intelligence (AI), such as the performance of deep learning techniques in digital pathology, have garnered significant attention for their potential to be used in this space. Many have now turned their attention towards the immuno-oncology-related applications for digital pathology, particularly in analyzing whole-slide images of widely available H&E-stained slides to aid in immune biomarker detection and ICI response prediction. In this review, we discuss the current landscape of AI-based digital pathology in immuno-oncology, including its applications for identifying and measuring immune biomarkers and, importantly, its potential for predicting ICI response and survival outcomes. We will end by discussing the challenges and future directions of adopting AI technologies for clinical deployment.https://jitc.bmj.com/content/13/8/e011346.full |
| spellingShingle | Yeseul Kim Eugene Kim Young Kwang Chae Jaeyoun Choi Chan Mi Jung Christmann Low Ju Young Lee Jonghanne Park Sukjoo Cho Lee Cooper Jessica Zhang Horyun Choi Allen Cho Emma J Yu Jeffrey H Chuang Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction Journal for ImmunoTherapy of Cancer |
| title | Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction |
| title_full | Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction |
| title_fullStr | Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction |
| title_full_unstemmed | Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction |
| title_short | Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction |
| title_sort | artificial intelligence based digital pathology using h e stained whole slide images in immuno oncology from immune biomarker detection to immunotherapy response prediction |
| url | https://jitc.bmj.com/content/13/8/e011346.full |
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