Predicting Remaining Survival of Glioblastoma Patients with Radiomics Analysis Based on 18F-DOPA PET Images
Our study demonstrates that ML models utilizing FU <sup>18</sup>F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.
Authors
- Val J. Lowe (ORCID: https://orcid.org/0000-0002-5612-1667)
- J. Qian (ORCID: https://orcid.org/0000-0001-5276-4158)
- Deanna Hasenauer (ORCID: https://orcid.org/0000-0002-0768-7277)
- Derek Richard Johnson (ORCID: https://orcid.org/0000-0002-4217-5517)
- Jann Nagina Sarkaria (ORCID: https://orcid.org/0000-0001-7489-4885)
- Joon H. Uhm (ORCID: https://orcid.org/0009-0003-1048-546X)
- Sani H. Kizilbash (ORCID: https://orcid.org/0000-0001-8568-1519)
- Mark Jacobson
- Michael W. Ruff (ORCID: https://orcid.org/0000-0001-8513-9615)
- Bradley J. Kemp (ORCID: https://orcid.org/0000-0002-2360-2368)
- Mark J. Zakhary (ORCID: https://orcid.org/0000-0003-4333-648X)
- Debra H. Brinkmann (ORCID: https://orcid.org/0000-0002-0812-3094)
- Elizabeth Yan
- Timothy J. Kaufmann (ORCID: https://orcid.org/0000-0001-7483-1569)
- Paul D. Brown (ORCID: https://orcid.org/0000-0003-0022-4533)
- Nadia N. Laack (ORCID: https://orcid.org/0000-0002-4385-6349)
- Christopher H. Hunt (ORCID: https://orcid.org/0000-0003-0301-0493)
- William G. Breen (ORCID: https://orcid.org/0000-0001-8039-2398)
- Hok Seum Wan Chan Tseung
- Yan Zhang (ORCID: https://orcid.org/0000-0003-3418-9385)
- Maasa Seaberg (ORCID: https://orcid.org/0000-0002-7655-1111)
Institutions
- Mayo Clinic (US)
- University of Florida Health (US)
- WinnMed (US)
- University of California San Francisco Medical Center (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Cancers
- Published
- 2025-11-03
- DOI
- https://doi.org/10.3390/cancers17213560
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Institutes of Health
- National Cancer Institute