Abstract
Objective. To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system. Approach. DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline’s input included the adapt-to-shape (ATS) plan—i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution—and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients). Main results. The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum. Significance. The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.
| Original language | English |
|---|---|
| Article number | 165009 |
| Journal | Physics in Medicine and Biology |
| Volume | 69 |
| Issue number | 16 |
| DOIs | |
| Publication status | Published - 21 Aug 2024 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- automation
- dose of the day
- dose verification
- MR-Linac
- MRI-guided radiation therapy
- MRL
- Unity
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