Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features
This study highlights the potential of using ML approaches with clinical and pre-treatment [<sup>18</sup>F]-FDG-PET radiomic data for predicting the prognosis of gallbladder cancer.
Authors
- Megumi Jinguji
- Daisuke Hirahara (ORCID: https://orcid.org/0000-0003-0243-5086)
- Tetsuya Idichi (ORCID: https://orcid.org/0000-0001-8048-2203)
- Kiyohisa Kamimura (ORCID: https://orcid.org/0000-0003-4191-9358)
- Mitsuho Hirahara
- Koji Takumi (ORCID: https://orcid.org/0000-0003-4067-2891)
- Takao Ohtsuka (ORCID: https://orcid.org/0000-0002-2946-1092)
- Takashi Yoshiura (ORCID: https://orcid.org/0000-0003-2918-4336)
- Atsushi Tani
- Masatoyo Nakajo (ORCID: https://orcid.org/0000-0001-9224-1730)
Institutions
- Kagoshima University (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2024-12-28
- DOI
- https://doi.org/10.1007/s11604-024-01722-0
- Citations
- 3
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.72