A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Oud, Michelle; Breedveld, Sebastiaan; Rojo-Santiago, Jesús; Giżyńska, Marta Krystyna; Kroesen, Michiel; Habraken, Steven; Perkó, Zoltán; Heijmen, Ben; Hoogeman, Mischa

doi:10.1088/1361-6560/ad2a98

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…by updating the predictions using CBCTs acquired during treatment. In proton beam therapy, the adaptive radiotherapy workflow is currently performed offline and requires a rescan CT (Oud et al 2024). The model predictions could be used to prospectively predict the point of replanning and be used to create a new proton therapy treatment plan.…”

Section: Discussionmentioning

confidence: 99%

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Nikou,

Thompson,

Nisbet

et al. 2024

Phys. Med. Biol.

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Head and neck cancer patients experience systematic anatomical changes as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes. A patient specific (SM) and population average (AM) model are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based SMs and AMs. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons. The large patient variability highlights the need for more complex, capable population models.

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Section: Discussionmentioning

confidence: 99%

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Nikou,

Thompson,

Nisbet

et al. 2024

Phys. Med. Biol.

View full text Add to dashboard Cite

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“…The first group is full online reoptimization, also referred to as online replanning in the literature, where a new treatment plan is optimized to the same objectives and constraints as the initial treatment plan (Matter et al 2019, 2021. The second group includes constrained online reoptimization and dose restoration, where the original plan is reoptimized to restore either the original plan quality or the original dose distribution (Bernatowicz et al 2018, 2023, 2023, Borderías-Villarroel et al 2022, Oud et al 2024.…”

Section: Introductionmentioning

confidence: 99%

Multi-institutional experimental validation of online adaptive proton therapy workflows

Bobić,

Choulilitsa,

Lee

et al. 2024

Phys. Med. Biol.

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Objective:To experimentally validate two online adaptive proton therapy (APT) workflows using Gafchromic EBT3 films and optically stimulated luminescent dosimeters (OSLDs) in an anthropomorphic head-and-neck phantom.Approach:A three-field proton plan was optimized on the planning CT of the head-and-neck phantom with 2.0 Gy(RBE) per fraction prescribed to the clinical target volume. Four fractions were simulated by varying the internal anatomy of the phantom. Three distinct methods were delivered: daily adaptive proton therapy researched by the Paul Scherrer Institute (DAPT_PSI), online adaptation researched by the Massachusetts General Hospital (OA_MGH), and a non-adaptive (NA) workflow. All methods were implemented and measured at PSI. DAPT_PSI performed full online replanning based on analytical dose calculation, optimizing to the same objectives as the initial treatment plan. OA_MGH performed Monte-Carlo-based online plan adaptation by only changing the fluences of a subset of proton beamlets, mimicking the planned dose distribution. NA delivered the initial plan with a couch-shift correction based on in-room-imaging. For all 12 deliveries, two films and two sets of OSLDs were placed at different locations in the phantom.Main results:Both adaptive methods showed improved dosimetric results compared to NA. For film measurements in the presence of anatomical variations, the [min-max] gamma pass rates (3%/3 mm) between measured and clinically approved doses were [91.5%-96.1%], [94.0%-95.8%], and [67.2%-93.1%] for DAPT_PSI, OA_MGH, and NA, respectively. The OSLDs confirmed the dose calculations in terms of absolute dosimetry. Between the two adaptive workflows, OA_MGH showed improved target coverage, while DAPT_PSI showed improved normal tissue sparing, particularly relevant for the brainstem.Significance:This is the first multi-institutional study to experimentally validate two different concepts with respect to online adaptive proton therapy workflows. It highlights their respective dosimetric advantages, particularly in managing interfractional variations in patient anatomy that cannot be addressed by non-adaptive methods, such as internal anatomy changes.

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A probabilistic evaluation of the Dutch robustness and model-based selection protocols for Head-and-Neck IMPT: A multi-institutional study

Rojo-Santiago,

Habraken,

Unipan

et al. 2024

Radiotherapy and Oncology

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A fast and robust constraint-based online re-optimization approach for automated online adaptive intensity modulated proton therapy in head and neck cancer

Cited by 5 publications

References 46 publications

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment

Multi-institutional experimental validation of online adaptive proton therapy workflows

A probabilistic evaluation of the Dutch robustness and model-based selection protocols for Head-and-Neck IMPT: A multi-institutional study

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