Analysis of imaging signatures in 18F-DOPA PET of glioblastoma treated with dose-escalated radiotherapy

Our study showcased the potential of employing quantitative features derived from <sup>18</sup>F-DOPA images to refine the stratification of patients with unmethylated MGMT for dose escalated therapy. Moreover, the change of these features can serve as valuable tools for monitoring treatment responses following radiotherapy.

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Publication Details

Journal
Frontiers in Oncology
Published
2025-08-13
DOI
https://doi.org/10.3389/fonc.2025.1623313
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36

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article

Analysis of imaging signatures in 18F-DOPA PET of glioblastoma treated with dose-escalated radiotherapy

Val J. Lowe, J. Qian, Derek Richard Johnson, Jann Nagina Sarkaria et al.
1 citations
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
1.36
article

Analysis of imaging signatures in 18F-DOPA PET of glioblastoma treated with dose-escalated radiotherapy

Val J. Lowe, J. Qian, Derek Richard Johnson, Jann Nagina Sarkaria, Joon H. Uhm, Deanna Pafundi, Sani H. Kizilbash, Mark Jacobson, Michael W. Ruff, Bradley J. Kemp, Mark J. Zakhary, Debra H. Brinkmann, Elizabeth Yan, Timothy J. Kaufmann, Paul D. Brown, Nadia N. Laack, Christopher H. Hunt, William G. Breen, Maasa H. Seaberg, Hok Seum Wan Chan Tseung, Yan Zhang
article en
1 citations

Abstract

Our study showcased the potential of employing quantitative features derived from <sup>18</sup>F-DOPA images to refine the stratification of patients with unmethylated MGMT for dose escalated therapy. Moreover, the change of these features can serve as valuable tools for monitoring treatment responses following radiotherapy.

Frontiers in OncologyVol. 15
University of Maryland, Baltimore (US), Mayo Clinic (US), Jacksonville College (US), University of California San Francisco Medical Center (US), Mayo Clinic in Arizona (US), Mayo Clinic in Florida (US)
National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 17%
Radiomics and Machine Learning in Medical Imaging
1.36
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