Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non–Small Cell Lung Cancer
The study developed and validated a deep learning radiomic biomarker using pretreatment routine CT/PET-CT scans to identify ICI benefit in advanced NSCLC.
Authors
- Hugo J.W.L. Aerts (ORCID: https://orcid.org/0000-0002-2122-2003)
- Kevin Maresca (ORCID: https://orcid.org/0000-0003-4420-1296)
- Sean Kent (ORCID: https://orcid.org/0000-0001-8697-9069)
- Ravi B. Parikh (ORCID: https://orcid.org/0000-0003-2692-6306)
- Petr Jordan (ORCID: https://orcid.org/0000-0002-3278-2866)
- Chiharu Sako (ORCID: https://orcid.org/0000-0003-3243-3954)
- Taly Gilat Schmidt (ORCID: https://orcid.org/0000-0002-9840-0963)
- Chong Duan (ORCID: https://orcid.org/0000-0001-9817-5340)
- George R. Simon (ORCID: https://orcid.org/0000-0002-3894-0773)
Institutions
- Pfizer (United States) (US)
- Moffitt Cancer Center (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1200/cci.24.00133
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- Massachusetts General Hospital
- Ohio State University
- University of Rochester
- University of Toronto
- School of Medicine and Public Health, University of Wisconsin-Madison
- Medical Center, University of Rochester