Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors
Our data support the generalizability of our developed ML algorithm in predicting response to ICI treatment in patients with metastatic unresectable melanoma. We also showed, for the first time, tumor features associated with patients' overall survival.
Authors
- Ke Yuan (ORCID: https://orcid.org/0000-0002-2318-1460)
- Irineu Illa-Bochaca (ORCID: https://orcid.org/0000-0002-8039-565X)
- Douglas Buckner Johnson (ORCID: https://orcid.org/0000-0002-6390-773X)
- David L. Rimm (ORCID: https://orcid.org/0000-0001-5820-4397)
- Jeffrey S. Weber (ORCID: https://orcid.org/0000-0002-4962-2957)
- Aristotelis Tsirigos (ORCID: https://orcid.org/0000-0002-7512-8477)
- Nicolas Coudray (ORCID: https://orcid.org/0000-0002-6050-2219)
- Milad Ibrahim (ORCID: https://orcid.org/0000-0001-8841-7393)
- Faisal Fa’ak (ORCID: https://orcid.org/0000-0002-9034-4100)
- George Jour (ORCID: https://orcid.org/0000-0001-8916-8966)
- Iman Osman (ORCID: https://orcid.org/0000-0002-1472-1971)
- Adalberto Claudio Quiros (ORCID: https://orcid.org/0000-0003-4804-0741)
- Shi Qiu (ORCID: https://orcid.org/0009-0004-5023-0469)
Institutions
- Washington University in St. Louis (US)
- Yale University (US)
- NYU Langone Health (US)
- Foster Care Associates Scotland (GB)
- New York University (US)
- University of Glasgow (GB)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Clinical Cancer Research
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1158/1078-0432.ccr-24-3720
- Citations
- 2
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- York University
- National Institutes of Health
- National Cancer Institute