<i>Gliomap-GAN</i> : A conditional generative adversarial network to visualize glioblastoma’s cell density from contrast-enhanced magnetic resonance imaging
The present research demonstrates that the <i>Gliomap,</i> generated from contrast-enhanced magnetic resonance imaging using generative artificial intelligence, is a promising imaging surrogate for visualizing tumor cell density in newly diagnosed glioblastoma.
Authors
- Jun Hatazawa (ORCID: https://orcid.org/0000-0003-1425-0521)
- Haruhiko Kishima (ORCID: https://orcid.org/0000-0002-9041-2337)
- Kayako Isohashi (ORCID: https://orcid.org/0000-0002-2081-3020)
- Hideyuki Arita (ORCID: https://orcid.org/0000-0002-1126-8155)
- Manabu Kinoshita (ORCID: https://orcid.org/0000-0001-7923-6902)
- Wataru Ide
- Keisuke Miyake (ORCID: https://orcid.org/0000-0001-5284-2267)
Institutions
- Osaka Gakuin University (JP)
- Asahikawa Medical University (JP)
- Kagawa University (JP)
- Osaka International Cancer Institute (JP)
- Hokuto Hospital (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Neuro-Oncology Advances
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1093/noajnl/vdaf227
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Akiyama Life Science Foundation
- Asahikawa Medical University
- Okawa Foundation for Information and Telecommunications
- Japan Society for the Promotion of Science