Machine learning model for predicting interfraction motion of the seminal vesicles in prostate cancer radiotherapy
This study suggests an association between anatomical features derived from pCT and patient-specific SV motion. Although the current predictive performance is moderate, this approach may help support radiotherapy strategies when the SV is included in the CTV.
Authors
- Mitsuaki Terabe
- Junji Ito (ORCID: https://orcid.org/0000-0001-9170-4422)
- Takeshi Kamomae (ORCID: https://orcid.org/0000-0001-7115-4822)
- Mariko Kawamura (ORCID: https://orcid.org/0000-0001-9211-4934)
- Takayuki Miyachi
- Risei Miyauchi
- Shinji Naganawa
- Shunichi Ishihara
- Yuki Taniguchi
- Hajime Ichikawa
Institutions
- Nagoya University Hospital (JP)
- Toyohashi Municipal Hospital (JP)
- Nagoya University (JP)
- Niigata University of Health and Welfare (JP)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1016/j.radonc.2026.111368
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science