Comprehensive Patient-Specific Intensity-Modulated Radiation Therapy Quality Assurance Comparing Mobius3D/FX to Conventional Methods of Evaluation

Basavatia, Amar; Fiedler, Derek; Ritter, Johannes; Brodin, P.; Goddard, Lee; Jeong, Ki-Hun; Hsu, Shu-Hui; Mynampati, D; Yaparpalvi, Ravindra

doi:10.7759/cureus.14910

Cited by 10 publications

(16 citation statements)

References 23 publications

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“…Figure 1 shows the breakdown of patients by disease site. In total over 6000 log files were mined to yield delivery data on 4.1 × 10 6 spots. These spots were used as input to train the ML model.…”

Section: Resultsmentioning

confidence: 99%

“…4,5 Log files also have been used in volumetric modulated arc therapy (VMAT) patient-specific QA. 6,7 Recently there has been significant interest in applying machine learning/artificial intelligence (ML/AI) to radiotherapy. 8,9 Some of these include dose calculation, 10,11 automatic contouring of structures, 12 automated planning, 13 and prediction of output factors for proton uniform scanning.…”

Section: Introductionmentioning

confidence: 99%

“…There is also interest in using log files to complement or even replace patient‐specific quality assurance (QA), a traditionally time and labor‐intensive task 4,5 . Log files also have been used in volumetric modulated arc therapy (VMAT) patient‐specific QA 6,7 …”

Section: Introductionmentioning

confidence: 99%

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Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

Newpower

Chiang

Ahmad

et al. 2023

J Applied Clin Med Phys

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The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1 × 10 6 delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

Newpower

Chiang

Ahmad

et al. 2023

J Applied Clin Med Phys

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“…M3D was validated for a variety of radiation therapy techniques, IMRT/VMAT/TomoTherapy, demonstrating to be a safely pre-treatment calculation-based verification system. [19][20][21][22][23][24] However, there is a lack of investigation regarding the appropriateness of M3D as patient-specific pre-treatment verification system for stereotactic brain radiation treatment with a high dose in a single fraction.…”

Section: Introductionmentioning

confidence: 99%

Is it still necessary to perform measured based pre‐treatment patient‐specific QA for SRS HyperArc treatments?

Cavalli,

Bonanno,

Borzì

et al. 2023

J Applied Clin Med Phys

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PurposeThe Mobius3D system was validated as a modern secondary check dosimetry system.In particular, our objective has been to assess the suitability of the M3D as pre‐treatment patient‐specific Quality Assurance (QA) tool for Stereotactic Radiosurgery (SRS) HyperArc (HA) treatments. We aimed to determine whether Mobius3D could safely replace the measurements‐based patient‐specific QA for this type of treatment.Methods30 SRS HA treatment plans for brain were selected. The dose distributions, calculated by Mobius and our routinely used algorithm (AcurosXB v.15.6), were compared using gamma analysis index and DVH parameters based on the patient's CT dataset.All 30 plans were then delivered across the ionization chamber in a homogeneous phantom and the measured dose was compared with both M3D and TPS calculated one.The plans were delivered and verified in terms of PSQA using the electronic portal imaging device (EPID) with Portal Dosimetry (PD) and myQA SRS (IBA Dosimetry) detector.Plans that achieved a global gamma passing rate (GPR) ≥ 97% based on 2%/2 mm criteria, with both Mobius3D and the conventional methods were evaluated acceptable. Finally, we assessed the capability of the M3D system to detect errors related to the position of the Multi‐Leaf Collimator (MLC) in comparison to the analyzed measurement‐based systems.ResultsNo relevant differences were observed in the comparison between the dose calculated on the CT‐dataset by M3D and the TPS. Observed discrepancies are imputable to different used algorithms, but no discrepancies related to goodness of plans have been found.Average differences between calculated (M3D and TPS) vs measured dose with ionization chamber were 2.5% (from 0.41% to 3.2%) and 1.81% (from 0.66% to 2.65%), for M3D and TPS, respectively.All plans passed with a gamma passing rate > 97% using conventional PSQA methods with a gamma criterion of 2% dose difference and 2 mm distance‐to‐agreement. The average gamma passing rate for the M3D system was determined to be 99.4% (from 97.3% to 100%). Results from this study also demonstrated Mobius has better error detectability than conventional measurement‐based systems.ConclusionOur study shows Mobius3D could be a suitable alternative to conventional measured based QA methods for SRS HyperArc treatments.

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“…However, in recent years, comparative studies for an overall range of clinically relevant plans, including intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), have been performed. 20 – 22 , 29 , 30 Moreover, detailed characteristics of Mobius3D/FX, such as the dosimetric leaf gap (DLG) correction factor, have been explored for optimal performance. 20 – 22 , 30 , 31 In summary, in-depth evaluations of dosimetric accuracy of Mobius3D/FX for IMRT and VMAT plans have been actively performed.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Mobius3D Dose Calculation Accuracy for Small-Field Flattening- Filter-Free Photon Beams

Kim

Lee

et al. 2022

Technol Cancer Res Treat

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Purpose: We aimed to investigate the dose calculation accuracy of Mobius3D for small-field flattening-filter-free x-rays, mainly utilized for stereotactic body radiation therapy (SBRT). The accuracy of beam modeling and multileaf collimator (MLC) modeling in Mobius3D, significantly affecting the dose calculation is investigated. Methods: The commissioning procedures of Mobius3D were performed for unflattened 6 MV and 10 MV x-ray beams of the linear accelerator, including beam model adjustment and dosimetric leaf gap (DLG) optimization. An experimental study with artificial plans was conducted to evaluate the accuracy of small-field modeling. The dose calculation accuracy of Mobius3D was also evaluated for retrospective SBRT plans with multiple targets. Results: Both studies evaluated the dose calculation accuracy through comparisons with the measured data. Relatively large differences were observed for off-axis distances over 5 cm and for small fields less than 1 cm field size. For the study with artificial plans, the maximum absolute error of 9.96% for unflattened 6 MV and 9.07% for unflattened 10 MV was observed when the field size was 1 cm. For the study with patient plans, the mean gamma passing rate with 3%/3 mm gamma criterion was 63.6% for unflattened 6 MV and 82.6% for unflattened 10 MV. The maximum of the average dose difference was −19.9% for unflattened 6MV and −10.1% for unflattened 10MV. Conclusions: The dose calculation accuracy uncertainties of Mobius3D for small-field flattening-filter-free photon beams were observed. The study results indicated that the beam and MLC modeling of Mobius3D must be improved for use in SBRT pretreatment QA in clinical practice.

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Comprehensive Patient-Specific Intensity-Modulated Radiation Therapy Quality Assurance Comparing Mobius3D/FX to Conventional Methods of Evaluation

Cited by 10 publications

References 23 publications

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

Is it still necessary to perform measured based pre‐treatment patient‐specific QA for SRS HyperArc treatments?

Evaluating Mobius3D Dose Calculation Accuracy for Small-Field Flattening- Filter-Free Photon Beams

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