Risk Estimation of Late Rectal Toxicity Using a Convolutional Neural Network-based Dose Prediction in Prostate Cancer Radiation Therapy
A risk estimation of LRB using CNN-based automatic plan generation from anatomic information was feasible. These results will contribute to the development of a decision support system that identifies priority cases for preradiation therapy interventions, such as hydrogel spacer implantation.
Authors
- Akira Torii (ORCID: https://orcid.org/0000-0003-0854-2974)
- Taiki Takaoka (ORCID: https://orcid.org/0000-0002-2667-8923)
- Dai Okazaki (ORCID: https://orcid.org/0000-0003-0936-2633)
- Natsuo Tomita
- Masanosuke Oguri
- Masanari Niwa (ORCID: https://orcid.org/0000-0002-7718-5811)
- Nozomi Kita (ORCID: https://orcid.org/0000-0003-4183-4637)
- Akio Hiwatashi (ORCID: https://orcid.org/0000-0001-5400-5083)
- Takahiro Yasui (ORCID: https://orcid.org/0000-0003-2197-2477)
- Seiya Takano (ORCID: https://orcid.org/0000-0001-6990-9923)
- Akane Matsuura
- Machiko Ukai
Institutions
- Nagoya City University (JP)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2025-02-16
- DOI
- https://doi.org/10.1016/j.adro.2025.101739
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- Japan Society for the Promotion of Science