Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept

Ying Zhang; Jailan Alshaikhi; Richard A Amos; Matthew Lowe; Wenyong Tan; Esther Bär; Gary Royle

doi:10.1016/j.radonc.2022.05.036

Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept

Radiother Oncol. 2022 Aug:173:93-101. doi: 10.1016/j.radonc.2022.05.036. Epub 2022 Jun 3.

Authors

Ying Zhang¹, Jailan Alshaikhi², Richard A Amos³, Matthew Lowe⁴, Wenyong Tan⁵, Esther Bär⁶, Gary Royle³

Affiliations

¹ Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom. Electronic address: ying.zhang.18@ucl.ac.uk.
² Saudi Proton Therapy Center, King Fahad Medical City, Riyadh, Saudi Arabia.
³ Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom.
⁴ Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
⁵ Department of Oncology, Shenzhen Hospital of Southern Medical University, China.
⁶ Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; University College London Hospitals NHS Foundation Trust, United Kingdom.

PMID: 35667573
DOI: 10.1016/j.radonc.2022.05.036

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 (D_mean) to the parotid glands exceeded 3 Gy(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 D_mean using predicted plan adaption is within 3 Gy(RBE) of the planned dose and 0.31 Gy(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.

Keywords: Application of anatomical modelling; Head and neck cancer; Intensity-modulated proton therapy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Nasopharyngeal Neoplasms* / radiotherapy
Organs at Risk
Proton Therapy* / methods
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted / methods
Radiotherapy, Intensity-Modulated* / methods
Retrospective Studies
Workflow

Abstract

Publication types

MeSH terms

Grants and funding