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Purpose To validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions. Methods A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input. Results The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle. Conclusions We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

Purpose This study aimed to improve the safety and accuracy of radiotherapy by establishing tolerance (TL) and action (AL) limits for the gamma index in patient‐specific quality assurance (PSQA) for intensity‐modulated radiation therapy (IMRT) and volumetric‐modulated arc therapy (VMAT) using SunCHECK software, as per AAPM TG‐218 report recommendations. Methods The study included 125 patients divided into six groups by treatment regions (H&N, thoracic and pelvic) and techniques (VMAT, IMRT). SunCHECK was used to calculate the gamma passing rate (%GP) and dose error (%DE) for each patient, for the planning target volume and organs at risk (OARs). The TL and AL were then determined for each group according to TG‐218 recommendations. We conducted a comprehensive analysis to compare %DE among different groups and examined the relationship between %GP and %DE. Results The TL and AL of all groups were more stringent than the common standard as defined by the TG218 report. The TL and AL values of the groups differed significantly, and the values for the thoracic groups were lower for both VMAT and IMRT. The %DE of the parameters D95%, D90%, and Dmean in the planning target volume, and Dmean and Dmax in OARs were significantly different. The dose deviation of VMAT was larger than IMRT, especially in the thoracic group. A %GP and %DE correlation analysis showed a strong correlation for the planning target volume, but a weak correlation for the OARs. Additionally, a significant correlation existed between %GP of SunCHECK and Delta4. Conclusion The study established TL and AL values tailored to various anatomical regions and treatment techniques at our institution. Establishing PSQA workflows for VMAT and IMRT offers valuable clinical insights and guidance. We also suggest developing a standard combining clinically relevant metrics with %GP to evaluate PSQA results comprehensively.

Purpose To create a snapshot of common practices for 3D‐CRT and intensity‐modulated radiation therapy (IMRT) QA through a large‐scale survey and compare to TG‐218 recommendations. Methods A survey of 3D‐CRT and IMRT QA was constructed at and distributed by the IROC‐Houston QA center to all institutions monitored by IROC (n = 2,861). The first part of the survey asked about methods to check dose delivery for 3D‐CRT. The bulk of the survey focused on IMRT QA, inquiring about treatment modalities, standard tools used to verify planned dose, how assessment of agreement is calculated and the comparison criteria used, and the strategies taken if QA fails. Results The most common tools for dose verification were a 2D diode array (52.8%), point(s) measurement (39.0%), EPID (27.4%), and 2D ion chamber array (23.9%). When IMRT QA failed, the highest average rank strategy utilized was to remeasure with the same setup, which had an average position ranking of 1.1 with 90.4% of facilities employing this strategy. The second highest average ranked strategy was to move to a new calculation point and remeasure (54.9%); this had an average ranking of 2.1. Conclusion The survey provided a snapshot of the current state of dose verification for IMRT radiotherapy. The results showed variability in approaches and that work is still needed to unify and tighten criteria in the medical physics community, especially in reference to TG‐218's recommendations.

Purpose To validate an MR‐compatible version of the ScandiDos Delta4 Phantom+ on a 0.35T MR guided linear accelerator (MR‐Linac) system and to determine the effect of plan complexity on the measurement results. Methods/Materials 36 clinical treatment plans originally delivered on a 0.35T MR linac system were re‐planned on the Delta4 Phantom+ MR geometry following our clinical quality assurance (QA) protocol. The QA plans were then measured using the Delta4 Phantom+ MR and the global gamma pass rates were compared to previous results measured using a Sun Nuclear ArcCHECK‐MR. Both 3%/3mm and 2%/2mm global gamma pass rates with a 20% dose threshold were recorded and compared. Plan complexity was quantified for each clinical plan investigated using 24 different plan metrics and each metric’s correlation with the overall 2%/2mm global gamma pass rate was investigated using Pearson correlation coefficients. Results Both systems demonstrated comparable levels of gamma pass rates at both the 3%/3mm and 2%/2mm level for all plan complexity metrics. Nine plan metrics including area, number of active MLCs, perimeter, edge metric, leaf segment variability, complete irradiation area outline, irregularity, leaf travel index, and unique opening index were moderately (|r| > 0.5) correlated with the Delta4 2%/2mm global gamma pass rates whereas those same metrics had weak correlation with the ArcCHECK‐MR pass rates. Only the perimeter to area ratio and small aperture score (20 mm) metrics showed moderate correlation with the ArcCHECK‐MR gamma pass rates. Conclusions The MR‐compatible version of the ScandiDos Delta4 Phantom+ MR has been validated for clinical use on a 0.35T MR‐Linac with results being comparable to an ArcCHECK‐MR system in use clinically for almost five years. Most plan complexity metrics did not correlate with lower 2%/2mm gamma pass rates using the ArcCHECK‐MR but several metrics were found to be moderately correlated with lower 2%/2mm global gamma pass rates for the Delta4 Phantom+ MR.

We demonstrate a virtual pretreatment patient‐specific QA (PSQA) procedure that is capable of quantifying dosimetric effect on patient anatomy for both intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). A machine learning prediction model was developed to use linear accelerator parameters derived from the DICOM‐RT plan to predict delivery discrepancies at treatment delivery (defined as the difference between trajectory log file and DICOM‐RT) and was coupled with an independent Monte Carlo dose calculation algorithm for dosimetric analysis. Machine learning models for IMRT and VMAT were trained and validated using 120 IMRT and 206 VMAT fields of prior patients, with 80% assigned for iterative training and testing, and 20% for post‐training validation. Various prediction models were trained and validated, with the final models selected for clinical implementation being a boosted tree and bagged tree for IMRT and VMAT, respectively. After validation, these models were then applied clinically to predict the machine parameters at treatment delivery for 7 IMRT plans from various sites (61 fields) and 10 VMAT multi‐target intracranial radiosurgery plans (35 arcs) and compared to the dosimetric effect calculated directly from trajectory log files. Dose indices tracked for targets and organs at risk included dose received by 99%, 95%, and 1% of the volume, mean dose, percent of volume receiving 25%–100% of the prescription dose. The average coefficient of determination (r2) when comparing intra‐field predicted and actual delivery error was 0.987 ± 0.012 for IMRT and 0.895 ± 0.095 for VMAT, whereas r2 when comparing inter‐field predicted versus actual delivery error was 0.982 for IMRT and 0.989 for VMAT. Regarding dosimetric analysis, r2 when comparing predicted versus actual dosimetric changes for all dose indices was 0.966 for IMRT and 0.907 for VMAT. Prediction models can be used to anticipate the dosimetric effect calculated from trajectory files and have potential as a “delivery‐free” pretreatment analysis to enhance PSQA.

Purpose Patient Specific QA (PSQA) by direct phantom measurement for all intensity modulated radiation therapy (IMRT) cases is labor intensive and an inefficient use of the Medical Physicist's time. The purpose of this work was to develop a hybrid quality assurance (QA) technique utilizing 3D dose verification as a screening tool to determine if a measurement is necessary. Methods This study utilized Sun Nuclear DoseCHECK (DC), a 3D secondary verification software, and Fraction 0, a trajectory log IMRT QA software. Twenty‐two Lung stereotactic body radiation therapy (SBRT) and thirty single isocentre multi‐lesion SRS (MLSRS) plans were retrospectively analysed in DC. Agreement of DC and the TPS dose for selected dosimetric criteria was recorded. Calculated 95% confidence limits (CL) were used to establish action limits. All cases were delivered and measured using the Sun Nuclear stereotactic radiosurgery (SRS) MapCheck. Trajectory logs of the delivery were used to calculate Fraction 0 results for the same criteria calculated by DC. Correlation of DC and Fraction 0 results were calculated. Phantom measured QA was compared to Fraction 0 QA results for the cases which had DC criteria action limits exceeded. Results Correlation of DC and Fraction 0 results were excellent, demonstrating the same action limits could be used for both and DC can predict Fraction 0 results. Based on the calculated action limits, zero lung SBRT cases and six MLSRS cases were identified as requiring a measurement. All plans that passed the DC screening had a passing measurement based PSQA and agreed with Fraction 0 results. Conclusion Using 95% CL action limits of dosimetric criteria, a 3D secondary dose verification can be used to determine if a measurement is required for PSQA. This method is efficient for it is part of the normal clinical workflow when verifying any clinical treatment. In addition, it can drastically reduce the number of measurements needed for PSQA.

Background Pulsed reduced dose rate (PRDR) is an emerging radiotherapy technique for recurrent diseases. It is pertinent that the linac beam characteristics are evaluated for PRDR dose rates and a suitable dosimeter is employed for IMRT QA. Purpose This study sought to investigate the pulse characteristics of a 6 MV photon beam during PRDR irradiations on a commercial linac. The feasibility of using EBT3 radiochromic film for use in IMRT QA was also investigated by comparing its response to a commercial diode array phantom. Methods A plastic scintillator detector was employed to measure the photon pulse characteristics across nominal repetition rates (NRRs) in the 5–600 MU/min range. Film was irradiated with dose rates in the 0.033–4 Gy/min range to study the dose rate dependence. Five clinical PRDR treatment plans were selected for IMRT QA with the Delta4 phantom and EBT3 film sheets. The planned and measured dose were compared using gamma analysis with a criterion of 3%/3 mm. EBT3 film QA was performed using a cumulative technique and a weighting factor technique. Results Negligible differences were observed in the pulse width and height data between the investigated NRRs. The pulse width was measured to be 3.15 ± 0.01 μs $\\mu s$and the PRF was calculated to be 3–357 Hz for the 5–600 MU/min NRRs. The EBT3 film was found to be dose rate independent within 3%. The gamma pass rates (GPRs) were above 99% and 90% for the Delta4 phantom and the EBT3 film using the cumulative QA method, respectively. GPRs as low as 80% were noted for the weighting factor EBT3 QA method. Conclusions Altering the NRRs changes the mean dose rate while the instantaneous dose rate remains constant. The EBT3 film was found to be suitable for PRDR dosimetry and IMRT QA with minimal dose rate dependence.

Purpose To develop an independent log file–based intensity‐modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance‐linac (MR‐linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced. Methods The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor‐supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units. Results The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work. Conclusions An independent MC and log file–based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.