Zhang, Ying;
Alshaikhi, Jailan;
Amos, Richard A;
Lowe, Matthew;
Tan, Wenyong;
Bar, Esther;
Royle, Gary;
(2022)
Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept.
Radiotherapy and Oncology
, 173
pp. 93-101.
10.1016/j.radonc.2022.05.036.
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Abstract
PURPOSE: To demonstrate predictive anatomical modelling for improving the clinical workflow of adaptive intensity-modulated proton therapy (IMPT) for head and neck cancer. METHODS: 10 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT, weekly verification CTs during radiotherapy and predicted weekly CTs from our anatomical model. Predicted CTs were used to create predicted adaptive plans in advance with the aim of maintaining clinically acceptable dosimetry. Adaption was triggered when the increase in mean dose (Dmean) to the parotid glands exceeded 3Gy(RBE). We compared the accumulated dose of two adaptive IMPT strategies: 1) Predicted plan adaption: One adaptive plan per patient was optimised on a predicted CT triggered by replan criteria. 2) Standard replan: One adaptive plan was created reactively in response to the triggering weekly CT. RESULTS: Statistical analysis demonstrates that the accumulated dose differences between two adaptive strategies are not significant (p>0.05) for CTVs and OARs. We observed no meaningful differences in D95 between the accumulated dose and the planned dose for the CTVs, with mean differences to the high-risk CTV of -1.20%, -1.23% and -1.25% for no adaption, standard and predicted plan adaption, respectively. The accumulated parotid Dmean using predicted plan adaption is within 3Gy(RBE) of the planned dose and 0.31Gy(RBE) lower than the standard replan approach on average. CONCLUSION: Prediction-based replanning could potentially enable adaptive therapy to be delivered without treatment gaps or sub-optimal fractions, as can occur during a standard replanning strategy, though the benefit of using predicted plan adaption over the standard replan was not shown to be statistically significant with respect to accumulated dose in this study. Nonetheless, a predictive replan approach can offer advantages in improving clinical workflow efficiency.
| Type: | Article |
|---|---|
| Title: | Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept |
| Location: | Ireland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.radonc.2022.05.036 |
| Publisher version: | https://doi.org/10.1016/j.radonc.2022.05.036 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Application of anatomical modelling, Head and neck cancer, Intensity-modulated proton therapy |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150207 |
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