Predicting Delivery Error Using a DICOM-RT Plan for Volumetric Modulated Arc Therapy

Miura, Hideharu; Tanooka, Masao; Takada, Yasuhiro; Doi, Hiroshi; Odawara, Soichi; Kosaka, Kengo; Kamikonya, Norihiko; Hirota, Shun

doi:10.4236/ijmpcero.2014.32013

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Miura et al also showed that gamma passing rates are correlated with MLC leaf velocity, indicating that errors in MLC positions due to large velocities may have negative effects on dosimetric accuracy (Miura et al 2014b). Furthermore, it has been shown that constraining the millimeters traveled per leaf per MU improves the delivery accuracy of the treatment plan (Chen et al 2011, Miura et al 2014a.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Carlson

Park

et al. 2016

Phys. Med. Biol.

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Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD = 1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be delivered to the patient.

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Section: Introductionmentioning

confidence: 99%

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

Carlson

Park

et al. 2016

Phys. Med. Biol.

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“…The complexity of IMRT and VMAT plans leads to a decreased gamma passing rate in dosimetric results [6] [7] [11]. Our previous study investigated dosimetry measurements of VMAT plan parameters using DICOM-RT verification and compared them to mechanical dose errors using a log file [13]. Despite the necessity of assessing VMAT plan complexity to ensure secure treatment planning, however, methods for quantitative assessment of VMAT plan complexity are still lacking.…”

Section: Introductionmentioning

confidence: 99%

DICOM-RT Plan Complexity Verification for Volumetric Modulated Arc Therapy

Miura¹,

Tanooka²,

Inoue³

et al. 2014

IJMPCERO

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The purpose of this study was to investigate the relationship between plan parameters verified with DICOM-RT and dosimetric results for volumetric modulated arc therapy (VMAT). We investigated three treatment locations: prostate cancer (ten cases), maxillary sinus cancer (four cases), and malignant pleura mesothelioma (four cases) with treatment plans generated by a Monaco TM treatment planning system (TPS), and delivered with an Elekta Synergy TM linear accelerator. We calculated plan parameters, including gantry and multileaf collimator (MLC) positions, Monitor Units (MU), and millimeters of MLC motion per degree of gantry rotation (mm/degree), and performed quality assurance (QA) with a DICOM-RT plan verification system. We measured the VMAT dose with a two-dimensional diode array detector. The average gamma passing rate with percent dose acceptance criteria and distance to agreement criteria of 2 mm and 2% (2 mm/2%) were 97.4%, 97.8% and 92.0% for prostate cancer, maxillary sinus cancer, and malignant pleural mesothelioma, respectively. The mean 95th percentile value for DICOM-calculated mm/degree was 4.0, 5.2, and 11.1 for prostate cancer, maxillary sinus cancer, and malignant pleural mesothelioma, respectively. The gamma passing rate showed a correlation with calculated mm/degree, with a coefficient of determination (R 2 ) of 0.60. Higher calculated mm/degree values led to increased dosimetric errors. We conclude that dose distribution calculated by a TPS is more reliable at smaller mm/degree.

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The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

Li,

Luo,

Tan

et al. 2024

Physics and Imaging in Radiation Oncology

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Predicting Delivery Error Using a DICOM-RT Plan for Volumetric Modulated Arc Therapy

Cited by 6 publications

References 22 publications

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

DICOM-RT Plan Complexity Verification for Volumetric Modulated Arc Therapy

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

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