Facts and hopes on the use of artificial intelligence for predictive immunotherapy biomarkers in cancer

• Verfasst von: Ghaffari Laleh, Narmin [VerfasserIn]
• Verfasst von: Ligero, Marta [VerfasserIn]
• Verfasst von: Perez-Lopez, Raquel [VerfasserIn]
• Verfasst von: Kather, Jakob Nikolas [VerfasserIn]
• Titel: Facts and hopes on the use of artificial intelligence for predictive immunotherapy biomarkers in cancer
• Verf.angabe: Narmin Ghaffari Laleh, Marta Ligero, Raquel Perez-Lopez, and Jakob Nikolas Kather
• E-Jahr: 2023
• Jahr: January 17 2023
• Umfang: 8 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht am 9. September 2022 ; Gesehen am 28.11.2023
• Titel Quelle: Enthalten in: Clinical cancer research
• Ort Quelle: Philadelphia, Pa. [u.a.] : AACR, 1995
• Jahr Quelle: 2023
• Band/Heft Quelle: 29(2023), 2, Seite 316-323
• ISSN Quelle: 1557-3265
• DOI: doi:10.1158/1078-0432.CCR-22-0390
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1840119276

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.