Accurate assessment of a Dutch practical robustness evaluation protocol in clinical PT with pencil beam scanning for neurological tumors

Santiago, J. Rojo; Habraken, S.; Lathouwers, D.; Romero, Alejandra Méndez; Perkó, Zoltán; Hoogeman, M.

doi:10.1016/j.radonc.2021.07.028

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…Treatment plans-especially sensitive proton and ion treatments-must also be repeatedly evaluated under uncertainties (e.g. setup and range errors, tumor motion or complex anatomical changes) to ensure sufficient plan robustness, requiring recalculating the dose distribution in many different scenarios (Perkó et al 2016, Rojo-Santiago et al 2021. With RT practice steadily moving towards adaptive treatments, accurate, fast and general purpose dose (and particle transport) calculations represent an increasingly pressing, currently unmet need in most clinical settings.…”

Section: Introductionmentioning

confidence: 99%

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Pastor-Serrano

Perkó

2022

Phys. Med. Biol.

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Objective: Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries. Approach: Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g., tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80,000 different head and neck, lung, and prostate geometries. Main results: Predicting beamlet doses in 5±4.9 ms with a very high gamma pass rate of 99.37±1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 second depending on the number of beamlets (800-2200 in our plans), achieving a 99.70±0.14% (2%, 2 mm) gamma pass rate across 9 test patients. Significance: Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

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Section: Introductionmentioning

confidence: 99%

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Pastor-Serrano

Perkó

2022

Phys. Med. Biol.

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“…The SR setting is generally fixed for the patient population and the treatment course, using a value that ensures target coverage for the vast majority of patients [9]. This may lead to an overly conservative SR setting for patients with relatively small geometrical variations during the treatment course [10,11]. Especially for head and neck (H&N) cancer patients, with critical organs typically close to the target, this results in a potentially avoidable enhanced risk of long term side effects [10,12,13].…”

Section: Discussionmentioning

confidence: 99%

“…The standard deviations (SD) of the Gaussian distributions were derived from QA data at HollandPTC, and included the squared sum of the isocentric errors in the CT (systematic) and gantry (systematic and random), uncertainties in couch positioning (random), registration with the MR (systematic), online matching (random) and intra-fraction motion (systematic and random). This resulted in SDs of 0.88, 0.88 and 0.91 mm for the systematic setup errors, and 0.78, 0.75 and 0.82 mm for the random errors in lateral, longitudinal and vertical directions, respectively [11]. Inter and intra observer variations in contouring were not considered.…”

Section: Evaluation -Simulation Of Treatmentsmentioning

confidence: 99%

An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer

Oud

Breedveld

Gíżyńska³

et al. 2022

Radiotherapy and Oncology

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“…The omission of such analyzes bears the potential of bias in the conclusions regarding truly delivered doses in CTVs and OARs. Recently, studies employing polynomial chaos expansion (PCE) on the planning-CT have shown that large numbers of dose distributions under the influence of potential residual errors can be generated rapidly and used for statistically accurate plan robustness analysis (Perkó et al 2016, Rojo-Santiago et al 2021. This has not yet been employed to evaluate plan adaptation strategies, (2) no comparison with current state-of-the-art clinical treatment planning strategy, such as robust optimization with trigger-based offline adaptive re-planning.…”

Section: Introductionmentioning

confidence: 99%

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

Oud,

Breedveld,

Rojo-Santiago

et al. 2024

Phys. Med. Biol.

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Objective – In head-and-neck cancer intensity modulated proton therapy (IMPT), adaptive radiotherapy is currently restricted to offline re-planning, mitigating the effect of slow changes in patient anatomies. Daily online adaptations can potentially improve dosimetry. Here, a new, fully automated online re-optimization strategy is presented. In a retrospective study, this online re-optimization approach was compared to our trigger-based offline re-planning (offlineTB re-planning) schedule, including extensive robustness analyses.Approach – The online re-optimization method employs automated multi-criterial re-optimization, using robust optimization with 1 mm setup-robustness settings (in contrast to 3 mm for offlineTB re-planning). Hard planning constraints and spot addition are used to enforce adequate target coverage, avoid prohibitively large maximum doses and minimize organ-at-risk doses. For 67 repeat-CTs from 15 patients, fraction doses of the two strategies were compared for the CTVs and organs-at-risk. Per repeat-CT, 10.000 fractions with different setup and range robustness settings were simulated using Polynomial Chaos Expansion for fast and accurate dose calculations. Main results – For 14/67 repeat-CTs, offlineTB re-planning resulted in <50% probability of D98%≥95% of the prescribed dose (Dpres) in one or both CTVs, which never happened with online re-optimization. With offlineTB re-planning, eight repeat-CTs had zero probability of obtaining D98%≥95%Dpres for CTV7000, while the minimum probability with online re-optimization was 81%. Risks of xerostomia and dysphagia grade ≥ II were reduced by 3.5 ± 1.7 and 3.9 ± 2.8 percentage point [mean ± SD] (p<10-5 for both). In online re-optimization, adjustment of spot configuration followed by spot-intensity re-optimization took 3.4 minutes on average. Significance – The fast online re-optimization strategy always prevented substantial losses of target coverage caused by day-to-day anatomical variations, as opposed to the clinical trigger-based offline re-planning schedule. On top of this, online re-optimization could be performed with smaller setup robustness settings, contributing to improved organs-at-risk sparing.

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Accurate assessment of a Dutch practical robustness evaluation protocol in clinical PT with pencil beam scanning for neurological tumors

Cited by 14 publications

References 29 publications

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

An online adaptive plan library approach for intensity modulated proton therapy for head and neck cancer

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

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