Clinical Implementation of a Proton Dose Verification System Utilizing a GPU Accelerated Monte Carlo Engine

Beltran, Chris; Tseung, H. Wan Chan; Augustine, Kurt E.; Bues, Martin; Mundy, Daniel W.; Walsh, Timothy J.; Herman, Michael; Laack, Nadia N.

doi:10.14338/ijpt-16-00011.1

Cited by 38 publications

(34 citation statements)

References 21 publications

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“…Although the relationship between LET d and RBE is difficult to know, a linear form for LET d between 1 and 10 keV/lm was assumed ( Figure 1). The linear model used here was RBE ¼ ð1:1Þ½0:08ðLET d Þ þ 0:88 [14,15] Of important note is that we are not claiming that the RBE of protons follows this equation; rather, implementation of this equation aids in detection of possible areas of high biologic dose at risk for complications. Fractionation, both number of fractions and dose per fraction, is explicitly excluded from this formula.…”

Section: Dose Calculationmentioning

confidence: 99%

See 1 more Smart Citation

Biological Model for Predicting Toxicity in Head and Neck Cancer Patients Receiving Proton Therapy

Fossum

Beltran

Whitaker

et al. 2017

International Journal of Particle Therapy

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Purpose: To use a linear energy transfer (LET) dependent formula for relative biological effectiveness (RBE) to generate a biological model that can be used to predict toxicity in patients treated with proton therapy for cancer of the head and neck. Patients and Methods: Patients treated with protons to a dose of 60 to 70 Gy (RBE ¼ 1.1) for head and neck cancer were eligible to participate in this study. Treatment plans were developed using graphics processing unit Monte Carlo calculations. The equation, RBE ¼ (1.1)[0.08(LET d)þ0.88], was the biological model. The physical model assumes RBE ¼ 1.1. Tumor volumes and organs at risk (OARs) were contoured, and isodose lines were created for 105%-120% of the prescribed dose. Dose to volume of OARs was calculated for both models for comparative purposes. Physician-reported toxicity was graded from 0 to 5 using the Common Terminology Criteria for Adverse Events, version 4.03. Patient-reported outcomes were obtained using the Promis10 and European Organisation for Research and Treatment of Cancer's QLQ-H&N35 instruments. Results: Eleven patients were included in this study. In each case the biological model revealed an increased dose to several OARs compared with the physical model. For selected OARs, the volume receiving .105% of the target dose was 2-fold to 15-fold greater in the biological model than the volume predicted by the physical model. Patients experienced toxicity that was consistent with the dose to OARs predicted by the biological model. Furthermore, 1 patient developed mucosal ulceration and another developed osteoradionecrosis at the location of a biological hot spot. In each case, the biological hot spot was located 2 mm inside the clinical target volume. Conclusion: The results suggest that increases in dose predicted by the biological model are clinically relevant and that LET and RBE corrections and optimization should be a component of the treatment-planning process in proton therapy.

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Section: Dose Calculationmentioning

confidence: 99%

“…For selected OARs, the volume receiving .105% of the prescribed dose was 2-fold to Figure 1. The present dose-averaged linear energy transferdependent formula for relative biological effectiveness (Beltran et al [14]) compared with previously developed models (Carabe et al [10], McNamara et al [9], Wedenberg et al [11]).…”

Section: Plan Evaluationsmentioning

confidence: 99%

Biological Model for Predicting Toxicity in Head and Neck Cancer Patients Receiving Proton Therapy

Fossum

Beltran

Whitaker

et al. 2017

International Journal of Particle Therapy

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“…The measurement depths and an array of profile coordinates were then propagated automatically to the semi-analytical and Monte Carlo 11 verification plan dose calculations. A 2D representation of these profiles was plotted using the ESAPI graphical user interface for user review prior to export to worksheet, as shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Automation of routine elements for spot‐scanning proton patient‐specific quality assurance

et al. 2018

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Purpose: At our institution all proton patient plans undergo patient-specific quality assurance (PSQA) prior to treatment delivery. For intensity modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency. Methods: We used the treatment planning system’s (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer and storage. To improve analysis of the measurement data, a Python script was developed to automatically perform a 2D-3D γ-index analysis comparing measurements in the plane of a 2D detector array with TPS predictions in a water phantom for each acquired measurement. Results: Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ-index analysis was even more pronounced, dropping by a factor of 10. On average we observed an overall time savings of 55% in completion of the PSQA per patient plan. Conclusions: The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.

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“…Although currently there are clear guidelines on the use of independent dose calculations to verify the dose in 3D and intensity-modulated treatment photon therapy, 2 at the time of writing this manuscript, such guidelines did not exist for proton therapy. Due to lack of independent commercial solutions, proton centers often develop their own in-house Monte Carlo (MC) [3][4][5] or analytical 6 second check systems.…”

Section: Introductionmentioning

confidence: 99%

“…15 LET can be easily scored in MC calculations and integrated into the independent secondary check. 5 MC-based patient-specific dose calculations require the execution of several steps before and after MC simulations. When the process is executed manually and in a stepwise manner, it can be time consuming and prone to human error.…”

Section: Introductionmentioning

confidence: 99%

Automation of Monte Carlo‐based treatment plan verification for proton therapy

Kaluarachchi

Moskvin

Pirlepesov

et al. 2020

J Applied Clin Med Phys

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Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in-depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow. Materials and Methods: The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high-performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria. Results: The automation reduced the human-hours required to initiate and run a calculation to 1-2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm. Conclusion: An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment.

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Clinical Implementation of a Proton Dose Verification System Utilizing a GPU Accelerated Monte Carlo Engine

Cited by 38 publications

References 21 publications

Biological Model for Predicting Toxicity in Head and Neck Cancer Patients Receiving Proton Therapy

Biological Model for Predicting Toxicity in Head and Neck Cancer Patients Receiving Proton Therapy

Automation of routine elements for spot‐scanning proton patient‐specific quality assurance

Automation of Monte Carlo‐based treatment plan verification for proton therapy

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