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Cited by 593AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 138 of the 139 references it cites were found in a registry or an index.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Open access
Baxi, Vipul, Edwards, Robin, Montalto, Michael, Saha, Saurabh·2022·Modern Pathology
DOI 10.1038/s41379-021-00919-2PMC PMC8491759
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Abstract

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

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Crossref · 10.1038/s41379-021-00919-2

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Fig. 1 Digital pathology: from drug discovery to clinical diagnostics. Dx diagnostic, EGFR epidermal growth factor receptor, FISH fluorescence in situ hybridization, HER2 human epidermal receptor 2, IHC immunohistochemistry, IVD in vitro diagnostic, MOA mechanism of action, NGS next-generation sequencing, PD pharmacodynamics, PD-L1 programmed death-ligand 1, TME tumor microenvironment.
Fig. 1 Digital pathology: from drug discovery to clinical diagnostics. Dx diagnostic, EGFR epidermal growth factor receptor, FISH fluorescence in situ hybridization, HER2 human epidermal receptor 2, IHC immunohistochemistry, IVD in vitro diagnostic, MOA mechanism of action, NGS next-generation sequencing, PD pharmacodynamics, PD-L1 programmed death-ligand 1, TME tumor microenvironment.CC BY · PubMed Central
Fig. 2 Digital prognostic pathology workflow. BOR best overall response, IHC immunohistochemistry, OS overall survival, PFS progression-free survival, QC quality control. aLeica Biosystems; bIndica Labs; cProscia; dPD-L1 IHC 28-8 pharmDx. Dako/Agilent Technologies.
Fig. 2 Digital prognostic pathology workflow. BOR best overall response, IHC immunohistochemistry, OS overall survival, PFS progression-free survival, QC quality control. aLeica Biosystems; bIndica Labs; cProscia; dPD-L1 IHC 28-8 pharmDx. Dako/Agilent Technologies.CC BY · PubMed Central
Fig. 3 Applications of digital pathology in IHC. a Monoplex slide stained for PD-L1, as seen on a monitor. b Monoplex slide stained for CD8. c Multiplex stain annotated using AI-based analysis allowing multiple marker identification. d Multispectral immunofluorescence. Legend indicates examples of possible phenotypes detected from this assay; however, many more are possible. CD cluster of differentiation, PD-1 programmed death-1, PD-L1 programmed death-ligand 1.
Fig. 3 Applications of digital pathology in IHC. a Monoplex slide stained for PD-L1, as seen on a monitor. b Monoplex slide stained for CD8. c Multiplex stain annotated using AI-based analysis allowing multiple marker identification. d Multispectral immunofluorescence. Legend indicates examples of possible phenotypes detected from this assay; however, many more are possible. CD cluster of differentiation, PD-1 programmed death-1, PD-L1 programmed death-ligand 1.CC BY · PubMed Central
Fig. 4 Comparisons between machine learning and deep learning. Deep learning is a subset of machine learning that uses multi-layer neural networks to analyze data, removing the need for operator input for feature extraction and image annotation37,41,57,59–62,111,145. CPU central processing units, GPU graphics processing units.
Fig. 4 Comparisons between machine learning and deep learning. Deep learning is a subset of machine learning that uses multi-layer neural networks to analyze data, removing the need for operator input for feature extraction and image annotation37,41,57,59–62,111,145. CPU central processing units, GPU graphics processing units.CC BY · PubMed Central

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Metadata

Authors
Baxi, V., Edwards, R., Montalto, M., & Saha, S.
Affiliations
  • Bristol-Myers Squibb (United States) (US)
  • PathAI (United States) (US)
Corresponding author
Baxi, Vipul
ORCID
Venue
Modern Pathology
Publisher
Elsevier BV
Published
2022-01
Volume
35
Issue
1
Pages
23-32
ISSN
0893-3952 (ISSN-L)
DOI
10.1038/s41379-021-00919-2
PMC
PMC8491759
OpenAlex
W3204013916
Cited by
592
References
139
Citations by year
2026: 107 · 2025: 158 · 2024: 175 · 2023: 102 · 2022: 49
FWCI
5.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Modern Pathology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Bristol-Myers Squibb
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
digital pathology, artificial intelligence, whole slide imaging, cancer immunotherapy, biomarker discovery, patient selection, precision medicine, tumor microenvironment, companion diagnostics, translational medicine, clinical implementation barriers
MeSH terms
Translational Science, Biomedical, Algorithms, Artificial Intelligence, Humans, Pathology, Practice Patterns, Physicians', Biomarkers

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