Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer
Integrating clinical and pCT-based radiomics features enhanced BF prediction in patients with high-risk and very high-risk localized PCa when accounting for competing risks using random survival forest. However, no significant improvement was observed for DM prediction.
Authors
- Takashi Mizowaki (ORCID: https://orcid.org/0000-0002-8135-8746)
- Rihito Aizawa (ORCID: https://orcid.org/0000-0001-7098-6221)
- Takanori Adachi (ORCID: https://orcid.org/0000-0003-1356-5118)
- Hideaki Hirashima (ORCID: https://orcid.org/0000-0003-1548-1970)
- Mitsuhiro Nakamura (ORCID: https://orcid.org/0000-0002-6406-2097)
- Takashi Ogata (ORCID: https://orcid.org/0000-0002-6453-6408)
- K. Hirata
Institutions
- Kyoto University (JP)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1016/j.adro.2025.101916
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science