Towards the clinical implementation of online adaptive proton therapy : strategies for automated and robust treatment planning
(2023)
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- Abstract
- The goal of radiation therapy is to deliver a sufficient dose to cancer cells while keeping the exposure of healthy tissue as low as possible to avoid toxicity. Conventional radiotherapy has benefited from technological advances to deliver more conformal doses to the tumor, yet these techniques are still constrained to the natural interaction of photons with matter. Unlike photons, protons slow down as they pass through the patient, releasing most of their energy at the end of their trajectory in the form of what is known as the Bragg peak. By taking advantage of this feature, it is possible to precisely localize the dose peaks in the tumor, reducing the exposure of surrounding healthy tissue. Radiation therapy treatments are planned based on a pre-treatment image to simulate the patient’s anatomy. However, patients are not static, and variations in the anatomy, such as weight loss, are frequent during a treatment course of several weeks. Proton therapy is especially sensitive to anatomical changes along the proton beam path, which alter the position of the Bragg peaks and can strongly deteriorate the initially planned dose. The adaptation of the initial plan is therefore required to preserve the quality of the treatment. Online adaptation in proton therapy, with the patient in the treatment position, aims to provide a rapid response to anatomical changes to compensate for proton dose distortions ensuring accurate treatment delivery. In this thesis, we investigate different strategies to automatically perform online adaptation to facilitate its clinical implementation in proton therapy. Eventually, online adaptation would allow clinicians to achieve greater control over the delivery of conformal proton dose distributions, reducing toxicity and improving patients’ quality of life.
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Citations
Borderias Villarroel, E. L. (2023). Towards the clinical implementation of online adaptive proton therapy : strategies for automated and robust treatment planning. https://hdl.handle.net/2078.5/102318