Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer
Background and purpose: No best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmar...
Saved in:
| Main Authors: | Aly Khalifa, Jeff D. Winter, Tony Tadic, Thomas G. Purdie, Chris McIntosh |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-10-01
|
| Series: | Physics and Imaging in Radiation Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631624001192 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Added value of non-rigid image registration for intrafraction dose accumulation in magnetic resonance imaging-guided prostate radiotherapy
by: Georgios Tsekas, et al.
Published: (2025-01-01) -
Prognostic value of intraductal carcinoma subtypes and postoperative radiotherapy for localized prostate cancer
by: Fang Cao, et al.
Published: (2025-01-01) -
Dose-volume parameter evaluation of a sub-fractionation workflow for adaptive radiotherapy of prostate cancer patients on a 1.5 T magnetic resonance imaging radiotherapy system
by: Georgios Tsekas, et al.
Published: (2025-01-01) -
Improved outcomes after radiotherapy for prostate cancer: Anticoagulation, antiplatelet therapy, and platelet count as key factors in disease progression
by: Stanley I. Gutiontov, et al.
Published: (2020-07-01) -
Urethra-sparing prostate cancer radiotherapy: Current practices and future insights from an international survey
by: Jennifer Le Guévelou, et al.
Published: (2025-03-01)