Journal article
Precision immunoprofiling by image analysis and artificial intelligence
- Abstract:
- Clinical success of immunotherapy is driving the need for new prognostic and predictive assays to inform patient selection and stratification. This requirement can be met by a combination of computational pathology and artificial intelligence. Here, we critically assess computational approaches supporting the development of a standardized methodology in the assessment of immune-oncology biomarkers, such as PD-L1 and immune cell infiltrates. We examine immunoprofiling through spatial analysis of tumor-immune cell interactions and multiplexing technologies as a predictor of patient response to cancer treatment. Further, we discuss how integrated bioinformatics can enable the amalgamation of complex morphological phenotypes with the multiomics datasets that drive precision medicine. We provide an outline to machine learning (ML) and artificial intelligence tools and illustrate fields of application in immune-oncology, such as pattern-recognition in large and complex datasets and deep learning approaches for survival analysis. Synergies of surgical pathology and computational analyses are expected to improve patient stratification in immuno-oncology. We propose that future clinical demands will be best met by (1) dedicated research at the interface of pathology and bioinformatics, supported by professional societies, and (2) the integration of data sciences and digital image analysis in the professional education of pathologists.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 4.5MB, Terms of use)
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- Publisher copy:
- 10.1007/s00428-018-2485-z
Authors
+ Swiss National Science Foundation
More from this funder
- Funding agency for:
- Koelzer, V
- Grant:
- P2SKP3_168322
- Publisher:
- Springer Berlin Heidelberg
- Journal:
- Virchows Archiv More from this journal
- Volume:
- 474
- Issue:
- 4
- Pages:
- 511–522
- Publication date:
- 2018-11-23
- Acceptance date:
- 2018-11-09
- DOI:
- EISSN:
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1432-2307
- ISSN:
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0945-6317
- Pmid:
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30470933
- Language:
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English
- Keywords:
- Pubs id:
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pubs:946564
- UUID:
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uuid:77ba0645-96e5-4bb4-8a6c-3e121fcfc43d
- Local pid:
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pubs:946564
- Source identifiers:
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946564
- Deposit date:
-
2018-12-07
Terms of use
- Copyright holder:
- Koelzer et al
- Copyright date:
- 2018
- Notes:
-
Copyright © 2018 The Authors.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Licence:
- CC Attribution (CC BY)
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