Validation of secondary dose calculation system with manufacturer-provided reference beam data using heterogeneous phantoms

Sensitivity and specificity of secondary dose calculation for head and neck treatment plans

PURPOSE

Secondary dose calculation (SDC) with an independent algorithm is one option to perform plan-specific quality assurance (QA). While measurement-based QA can potentially detect errors in plan delivery, the dose values are typically only compared to calculations on homogeneous phantom geometries instead of patient CT data. We analyzed the sensitivity and specificity of an SDC software by purposely introducing different errors and determined thresholds for optimal decisions.

METHODS

Thirty head and neck VMAT plans and 30 modifications of those plans, including errors related to density and beam modelling, were recalculated using RadCalc with a Monte Carlo algorithm. Decision thresholds were obtained by receiver operating characteristics (ROC) analysis. For comparison, measurement-based QA using the ArcCHECK phantom was carried out and evaluated in the same way.

RESULTS

Despite optimized decision thresholds, none of the systems was able to reliably detect all errors. ArcCHECK analysis using a 2%/2 mm criterion with a threshold of 91.1% had an area under the curve (AUC) of 0.80. Evaluating differences in recalculated and planned DVH parameter of the target structures in RadCalc with a 2% threshold an AUC of 0.86 was achieved. Out-of-field deviations could be attributed to weaknesses in the beam model.

CONCLUSIONS

Secondary dose calculation with RadCalc is an alternative to established measurement-based phantom QA. Different tools catch different errors; therefore, a combination of approaches should be preferred.

Validation of a secondary dose check tool against Monte Carlo and analytical clinical dose calculation algorithms in VMAT

PURPOSE

Patient-specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time-consuming reducing considerably the time available for patient treatments. In this work, a new MC-based secondary dose check software (SciMoCa) is evaluated and benchmarked against well-established TPS (Monaco and Pinnacle3 ) by means of treatment plans and dose measurements.

METHODS

Fifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%-2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans.

RESULTS

TPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and -0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95 % clinical tolerance threshold.

CONCLUSION

This work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC-based TPS for the first time. Results show that a MC-based secondary patient-specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per-plan basis QA that effectively complements traditional commissioning and calibration protocols.

Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance

PURPOSE

Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019.

METHODS AND MATERIALS

In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements.

RESULTS

In the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement.

CONCLUSIONS

It is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.