arXiv subjects

Erik Engwall

Publications and source records attributed to Erik Engwall.

5 recordsLinked to original sources

arXiv2026

Sparse probabilistic evaluation for treatment planning: a feasibility study in IMPT head & neck patients

Probabilistic evaluation improves the trade-off between target coverage and OAR sparing in IMPT but remains computationally demanding. This study proposes sparse probabilistic evaluation (SPE), a computationally efficient approach integrated into a clinical TPS. Clinical plans of 20 IMPT HNC patients treated in 2024 were included. SPE used a predefined setup and range error grid with Monte Carlo computed dose distributions. Two grid settings were evaluated: the maximum error Emax (3$\sigma$ or 4$\sigma$) and the number of setup error points nsetup (7, 33, 123). Accuracy and duration of SPE with each grid were evaluated in the calibration group (5 patients). 1000 treatments with normally distributed random ($\sigma$ = 1 mm) and systematic ($\sigma$ = 0.92 mm) setup and range ($\sigma$ = 1.5%) errors were simulated. The dose distribution of the nearest error point in the grid was assigned to each fraction. Probability distributions derived from SPE were compared with those from a reference based on 35,000 Monte Carlo calculations. The found optimal grid (Emax = 3$\sigma$, nsetup = 33) was applied to the validation group (15 patients). Accuracy of SPE in the calibration group increased significantly as the number of error points increased from 7 (tavg = 2 minutes) to 33 (tavg = 9 minutes), with no further improvement between 33 and 123 (tavg = 27 minutes) error points. Increasing Emax only improved accuracy for values above the 98th percentile. Applying SPE to the validation group resulted in median errors of 0.02 Gy RBE (range:-0.11 to 0.07) for the 10th percentile of the D99.8%, CTV distribution and 0.0 Gy RBE (range:-0.14 to 0.23) for the 95th percentile of the D0.03cc,SpinalCord Core distribution. Sparse probabilistic evaluation achieves sufficient accuracy while requiring clinically acceptable computation times, paving the way for probabilistic evaluation in clinical practice.

physics.med-ph↗

arXiv2026

Percentile-based probabilistic optimization for systematic and random uncertainties in radiation therapy

Geometric uncertainty can degrade treatment quality in radiation therapy. While margins and robust optimization mitigate these effects, they provide only implicit control over clinical goal fulfillment probability. We therefore develop a probabilistic planning framework using a percentile-based optimization function that targets a specified probability of clinical goal fulfillment. Systematic and random uncertainties were explicitly modeled over full treatment courses. A scenario dose approximation method based on interpolation between a fixed set of doses was used, enabling efficient simulation of treatment courses during optimization. The framework was evaluated on a prostate case treated with volumetric-modulated arc therapy (VMAT) and a brain case treated with pencil beam scanning (PBS) proton therapy. Plans were compared to conventional margin-based and worst-case robust optimization using probabilistic evaluation. For the prostate case, probabilistic optimization improved organ at risk (OAR) sparing while maintaining target coverage compared to margin-based planning, increasing average OAR goal fulfillment probability by 13.3 percentage points and reducing 90th percentile OAR doses by an average of 3.5~Gy. For the brain case, probabilistic optimization improved target minimum dose passing probabilities (e.g., 88\% vs.~22\% for $D_{95}$) and brainstem maximum dose passing probability (70\% vs.~30\%), while maintaining comparable or improved OAR sparing compared to worst-case optimization. Probabilistic optimization enables explicit and interpretable control over goal fulfillment probabilities. Combining full treatment course modeling with efficient approximate dose calculation, the proposed framework improved the trade-off between target coverage and OAR sparing compared to conventional planning approaches in both photon and proton therapy.

physics.med-ph↗

arXiv2025

Shoot-through layers in upright proton arcs unlock advantages in plan quality and range verification

Background: Upright proton therapy with compact delivery systems has the potential to reduce costs for treatments but could also lead to broadening of the beam penumbra due to energy selection close to the patient. Purpose: This study aims at combining upright static proton arcs with additional layers of shoot-through (ST) protons to sharpen the beam penumbra and improve plan quality for such systems. Methods: We examined various treatment plans for a virtual phantom: 3-beam IMPT, static arc (Arc) with/without ST (Arc+ST), and with/without collimation (+Coll). In the virtual phantom three different targets were utilized to study the effect on conformity index (CI), homogeneity index (HI), robustness and mean dose to the phantom volume. The phantom study was complemented with a head-and-neck (H&N) patient case with a similar set of plans. The delivery time for all plans was estimated. A range verification concept that determines residual ranges of the ST protons was studied in simulated scenarios for the H&N case. Results: In the phantom study, the Arc+ST plans show superior CI, HI and target robustness compared to the Arc+Coll plans. For the Arc plans without ST, the collimated plans perform better than the uncollimated plans. On the other hand, for Arc+ST, collimation has little impact on CI, HI and robustness. For the H&N case, similar improvements for Arc+ST can be seen. These results imply that no aperture is needed when combining arcs with ST, which in turn substantially reduces treatment times. The range verification simulation shows that the method is sensitive to detect SPR errors, setup errors and changes in the patient anatomy. Conclusions: Combining proton arcs and ST layers can enhance compact upright proton solutions by improving plan quality. It is also tailored for the inclusion of a fast and straightforward residual range verification method.

physics.med-ph↗

arXiv2024

A dosimetric and robustness analysis of Proton Arc Therapy (PAT) with Early Energy Layer and Spot Assignment (ELSA) for lung cancer versus conventional Intensity modulated therapy (IMPT)

Background and purpose: IMPT faces challenges in lung cancer treatment, like maintaining plan robustness for moving tumors against setup, range errors, and interplay effects. Proton Arc Therapy (PAT) is an alternative to maintain target coverage, potentially improving organ at risk (OAR) sparing, reducing beam delivery time (BDT), and enhancing patient experience. We aim to perform a systematic plan comparison study between IMPT and ELSA-PAT to assess its potential for lung cancer treatment. Material and Methods: 14 Lung ELSA-PAT plans were compared retrospectively with IMPT plans. 4D worst-case minimax robust optimization was performed, including 84 scenarios (3%,3 mm). Dosimetry assessment included target (CTV) and important OARs, on nominal and worst-case scenarios. Most relevant normal tissue complication probabilities (NTCP), target coverage robustness against interplay effect and beam delivery time (BDT) were evaluated. Results: CTV D95% and D98% showed no significant difference in comparison. PAT demonstrated better conformality by 66% (p = 0.00012) but delivered a higher heart mean dose (HMD,23%). There was a 2% increase in NTCP 2-year mortality risk with PAT. Total BDT was comparable among techniques. IMPT was more robust than PAT against interplay effect, considering both D1% (1,0 $\pm$ 0.8 Gy vs 1.1 $\pm$ 1.4 Gy) and D98% bandwidths (0.9$\pm$0.9 Gy vs 1.1 $\pm$ 1.3 Gy). Interpretation: both techniques provide a similar level of dose coverage to the target volume. Although PAT improved dose conformality, higher HMD translated into increased heart toxicity, presumably due to chosen planning methodology and OAR proximity to target. Increased energy layers and spots raised PAT beam delivery time, although it could improve daily treatment workflow.

physics.med-ph↗

arXiv2023

Impact of limited temporal resolution on 4D Monte Carlo dose calculation for intensity modulated proton therapy

The interplay between the beam delivery time structure and the patient motion makes 4D dose calculation (4DDC) important when treating moving tumors with intensity modulated proton therapy. 4DDC based on phase sorting of a 4DCT suffers from approximation errors in the assignment of spots to phases, since the temporal image resolution of the 4DCT is much lower than that of the delivery time structure. In this study we investigate and address this limitation by a method which applies registration-based interpolation between phase images to increase the temporal resolution of the 4DCT. First, each phase image is deformed toward its neighbor using the deformation vector field that aligns them, scaled by the desired time step. Then Monte Carlo-based 4DDC is performed on both the original 4DCT (10 phases), and extended 4DCTs at increasingly fine temporal resolutions. The method was evaluated on seven lung cancer patients treated with three robustly optimized beams, with simulated delivery time structures. Errors resulting from limited temporal resolution were measured by comparisons of doses computed using extended 4DCTs of various resolutions. The dose differences were quantified by gamma pass rates and volumes of the CTV that had dose differences above certain thresholds. The ground truth was taken as the dose computed using 100 phase images, and was justified by considering the diminishing effects of adding more images. The effect on dose-averaged linear energy transfer was also included in the analysis. A resolution of 20 (30) phase images per breathing cycle was sufficient to bring mean CTV $\gamma$-pass rates for $3\%/3$mm ($2\%/2$mm) above 99$\%$. For the patients with well behaved image data, mean CTV $\gamma$-pass rates for $1\%/1$mm surpassed 99$\%$ at a resolution of 50 images.

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