Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Narmin Ghaffari Laleh - , RWTH Aachen University (Author)
  • Marta Ligero - , Vall d'Hebron Institute of Oncology (VHIO) (Author)
  • Raquel Perez-Lopez - , Vall d'Hebron Institute of Oncology (VHIO), Autonomous University of Barcelona (Author)
  • Jakob Nikolas Kather - , Else Kröner Fresenius Center for Digital Health, RWTH Aachen University, University of Leeds, National Center for Tumor Diseases (NCT) Heidelberg (Author)

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 languageEnglish
Pages (from-to)316-323
Number of pages8
JournalClinical cancer research
Volume29
Issue number2
Publication statusPublished - 17 Jan 2023
Peer-reviewedYes

External IDs

PubMed 36083132

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