Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer
Research output: Contribution to journal › Review article › Contributed › peer-review
Contributors
Abstract
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.
Details
Original language | English |
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Pages (from-to) | 316-323 |
Number of pages | 8 |
Journal | Clinical cancer research |
Volume | 29 |
Issue number | 2 |
Publication status | Published - 17 Jan 2023 |
Peer-reviewed | Yes |
External IDs
PubMed | 36083132 |
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Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- biomarkers for supporting clinical decisions, high accuracy, ideally with high reproducibility, low costs, Neoplasms/therapy, Humans, Artificial Intelligence, Immunotherapy, Radiology, Biomarkers