A deep learning model for predicting the modified micro-dosimetric kinetic model-based dose and the dose-averaged linear energy transfer for prostate cancer in carbon ion therapy
Adaptive carbon ion radiotherapy for localized prostate cancer requires accurate evaluation of biological dose and dose-averaged linear energy transfer (LET<sub>d</sub>) changes. This study developed a deep learning model to rapidly predict the modified micro-dosimetric kinetic model (mMKM)-based dose and LET<sub>d</sub> distributions. Using data from fifty patients for training and testing, the model achieved gamma passing rates exceeding 96% compared to true mMKM-based dose and LET<sub>d</sub> recalculated from local effect model I (LEM I) plans. Incorporating computed tomography images, contours, physical dose, and LEM I-based dose as inputs, this model provided a rapid, accurate tool for comprehensive evaluations.
Authors
- Weiwei Wang (ORCID: https://orcid.org/0000-0002-8993-8411)
- Qing Zhang (ORCID: https://orcid.org/0000-0001-7582-5320)
- Ping Li (ORCID: https://orcid.org/0000-0002-1503-0240)
- Liwen Zhang (ORCID: https://orcid.org/0000-0002-5077-9732)
- Rongcheng Han
Institutions
- Fudan University Shanghai Cancer Center (CN)
- Center for High Pressure Science & Technology Advanced Research (CN)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.phro.2024.100671
- Citations
- 1
- Primary Topic
- Radiation Therapy and Dosimetry
- Type
- article
- Field-Weighted Citation Impact
- 0.42