On the implementation of the plan‐class specific reference field using multidimensional clustering of plan features and alternative strategies for improved dosimetry in modulated clinical linear accelerator treatments

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications

BACKGROUND

Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge.

PURPOSE

The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application.

METHODS

A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process.

RESULTS

The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex.

CONCLUSIONS

Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.

Multi-institution single geometry plan complexity characteristics based on IROC phantoms

BACKGROUND

Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution-specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives.

PURPOSE

To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans.

METHODS

1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC-descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k-means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin-Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS.

RESULTS

The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k-means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans.

CONCLUSIONS

There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients.

A clinical validation of the MR-compatible Delta4 QA system in a 0.35 tesla MR linear accelerator

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.

Characteristics and limitations of a secondary dose check software for VMAT plan calculation

Purpose

To assess the implementation, accuracy, and validity of the dosimetric leaf gap correction (DLGC) in Mobius3D VMAT plan calculations.

Methods

The optimal Mobius3D DLGC was determined for both a TrueBeam with a Millennium multi‐leaf collimator and a TrueBeamSTx with a high‐definition multi‐leaf collimator. By analyzing a broad series of seven VMAT plans and comparing the calculated to the measured dose delivered to a cylindrical phantom, optimal DLGC values were determined by minimizing the dose difference for both the collection of all plans, as well as for each plan individually. The effects of plan removal from the optimization of the collective DLGC value, as well as plan‐specific DLGC values, were explored to determine the impact of plan suite design on the final DLGC determination.

Results

Optimal collective DLGC values across all energies were between −0.71 and 0.89 mm for the TrueBeam, and between 0.35 and 1.85 mm for the TrueBeamSTx. The dose differences ranged between −6.1% and 2.6% across all plans when the optimal collective DLGC values were used. On a per‐plan basis, the plan‐specific optimal DLGC values ranged from −4.36 to 2.35 mm for the TrueBeam, and between −1.83 and 2.62 mm for the TrueBeamSTx. Comparing the plan‐specific optimal DLGC to the average absolute leaf position from the central axis for each plan, a negative correlation was observed.

Conclusions

The optimal DLGC determination depends on the plans investigated, making it essential for users to utilize a suite of test plans that encompasses the full range of expected clinical plans when determining the optimal DLGC value. Validation of the secondary dose calculation should always be based on measurements, and not a comparison with the primary TPS. Varying disagreement with measurements across plans for a single DLGC value indicates potential limitations in the Mobius3D MLC model.

Reference dosimetry of modulated and dynamic photon beams

In the late 1980s, a new technique was proposed that would revolutionize radiotherapy. Now referred to as intensity-modulated radiotherapy, it is at the core of state-of-the-art photon beam delivery techniques, such as helical tomotherapy and volumetric modulated arc therapy. Despite over two decades of clinical application, there are still no established guidelines on the calibration of dynamic modulated photon beams. In 2008, the IAEA-AAPM work group on nonstandard photon beam dosimetry published a formalism to support the development of a new generation of protocols applicable to nonstandard beam reference dosimetry (Alfonso et al 2008 Med. Phys. 35 5179-86). The recent IAEA Code of Practice TRS-483 was published as a result of this initiative and addresses exclusively small static beams. But the plan-class specific reference calibration route proposed by Alfonso et al (2008 Med. Phys. 35 5179-86) is a change of paradigm that is yet to be implemented in radiotherapy clinics. The main goals of this paper are to provide a literature review on the dosimetry of nonstandard photon beams, including dynamic deliveries, and to discuss anticipated benefits and challenges in a future implementation of the IAEA-AAPM formalism on dynamic photon beams.