| Autor: |
Aly Khalifa, Jeff D. Winter, Tony Tadic, Thomas G. Purdie, Chris McIntosh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Physics and Imaging in Radiation Oncology, Vol 32, Iss , Pp 100649- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2405-6316 |
| DOI: |
10.1016/j.phro.2024.100649 |
| Popis: |
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 benchmarks it against current clinical RT plan adaptation methods. Materials and methods: We trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.7 Gy in 7 fractions) from 46 patients with prostate cancer previously treated at our institution. For a held-out test set of 38 patients, retrospectively generated ML RT plans were compared to clinical human-generated adaptive RT plans for all 266 fractions. Differences in dose-volume metrics and clinical objective pass rates were evaluated using Wilcoxon tests (p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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