Use of metrics to quantify IMRT and VMAT treatment plan complexity: A systematic review and perspectives

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy

PURPOSE

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning.

METHODS

The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis.

RESULTS

The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365.

CONCLUSIONS

The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

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.

Impact of beam complexity on plan delivery accuracy verification of a transmission detector in volumetric modulated arc therapy

OBJECTIVE

To study the effect of beam complexity on VMAT delivery accuracy evaluated by means of a transmission detector, together with the possibility of scoring plan complexity.

METHODS

43 clinical VMAT plans delivered by a TrueBeam linear accelerator to both Delta4 Discover and Delta4 Phantom+ for patient-specific quality assurance were evaluated. Global Dose-γ analysis, MLC-γ analysis, percentage of leaves with a deviation between planned and measured leaf tip position lower than 1 mm (LD) were computed. Modulation complexity score (MCSv), average leaf travel (LT), a multiplicative combination of LT and MCSv (LTMCS), percentage of leaves with speed lower than 5 mm/s (LS), from 5 to 20 mm/s (MS), higher than 20 mm/s (HS) and the average value of leaf speed (MLCSav) were evaluated by means of an home-made Matlab script.

RESULTS

Dose-γ passing rate showed a moderate correlation with MCSv, LT, MLCSav, LS and HS, while a stronger positive correlation was found with LTMCS. A strong correlation was observed between LD and both LT and leaves speed, while a weak correlation was observed with MCSv. A correlation between MLC-γ pass rate and plan complexity parameters was found except for MCSv; a moderate correlation with LS was observed, while all other parameters showed weak correlations.

CONCLUSIONS

The study confirmed the possibility to establish correlations between plan complexity indices versus dose distribution and MLC parameters measured by a transmissive detector. Further investigation is necessary to define specific values of the complexity indices to evaluate whether a VMAT plan is deliverable as intended.

Evaluating AAPM-TG-218 recommendations: Gamma index tolerance and action limits in IMRT and VMAT quality assurance using SunCHECK

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.

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.

Plan complexity metrics for head and neck VMAT competition plans

Previous plan competitions have largely focused on dose metric assessments. However, whether the submitted plans were realistic and reasonable from a quality assurance (QA) perspective remains unclear. This study aimed to investigate the relationship between aperture-based plan complexity metrics (PCM) in volumetric modulated arc therapy (VMAT) competition plans and clinical treatment plans verified through patient-specific QA (PSQA). In addition, the association of PCMs with plan quality was examined. A head and neck (HN) plan competition was held for Japanese institutions from June 2019 to July 2019, in which 210 competition plans were submitted. Dose distribution quality was quantified based on dose-volume histogram (DVH) metrics by calculating the dose distribution plan score (DDPS). Differences in PCMs between the two VMAT treatment plan groups (HN plan competitions held in Japan and clinically accepted HN VMAT plans through PSQA) were investigated. The mean (± standard deviation) DDPS for the 98 HN competition plans was 158.5 ± 20.6 (maximum DDPS: 200). DDPS showed a weak correlation with PCMs with a maximum r of 0.45 for monitor unit (MU); its correlation with some PCMs was "very weak." Significant differences were found in some PCMs between plans with the highest 20% DDPSs and the remaining plans. The clinical VMAT and competition plans revealed similar distributions for some PCMs. Deviations in PCMs for the two groups were comparable, indicating considerable variability among planners regarding planning skills. The plan complexity for HN VMAT competition plans increased for high-quality plans, as shown by the dose distribution. Direct comparison of PCMs between competition plans and clinically accepted plans showed that the submitted HN VMAT competition plans were realistic and reasonable from the QA perspective. This evaluation may provide a set of criteria for evaluating plan quality in plan competitions.

Dosimetric impact of MLC positional errors on dose distribution in IMRT

Optimizing the positional accuracy of multileaf collimators (MLC) for radiotherapy is important for dose accuracy and for reducing doses delivered to normal tissues. This study investigates dose sensitivity variations and complexity metrics of MLC positional error in volumetric modulated arc therapy and determines the acceptable ranges of MLC positional accuracy in several clinical situations. Treatment plans were generated for four treatment sites (prostate cancer, lung cancer, spinal, and brain metastases) using different treatment planning systems (TPSs) and fraction sizes. Each treatment plan introduced 0.25-2.0 mm systematic or random MLC leaf bank errors. The generalized equivalent uniform dose (gEUD) sensitivity and complexity metrics (MU/Gy and plan irregularity) were calculated, and the correlation coefficients were assessed. Furthermore, the required tolerances for MLC positional accuracy control were calculated. The gEUD sensitivity showed the highest dependence of systematic positional error on the treatment site, followed by TPS and fraction size. The gEUD sensitivities were 6.7, 4.5, 2.5, and 1.7%/mm for Monaco and 8.9, 6.2, 3.4, and 2.3%/mm (spinal metastasis, lung cancer, prostate cancer, and brain metastasis, respectively) for RayStation. The gEUD sensitivity was strongly correlated with the complexity metrics (r = 0.88-0.93). The minimum allowable positional error for MLC was 0.63, 0.34, 1.02, and 0.28 mm (prostate, lung, brain, and spinal metastasis, respectively). The acceptable range of MLC positional accuracy depends on the treatment site, and an appropriate tolerance should be set for each treatment site with reference to the complexity metric. It is expected to enable easier and more detailed MLC positional accuracy control than before by reducing dose errors to patients at the treatment planning stage and by controlling MLC quality based on complexity metrics, such as MU/Gy.

A TPS integrated machine learning tool for predicting patient-specific quality assurance outcomes in volumetric-modulated arc therapy

PURPOSE

Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS.

MATERIALS AND METHODS

Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed.

RESULTS

The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated.

CONCLUSIONS

The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.

Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy

Background and purpose

Treatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA).

Material and methods

A number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA.

Results

Intra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Inter-institutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute.

Conclusions

The study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type.

Machine learning-based ensemble prediction model for the gamma passing rate of VMAT-SBRT plan

PURPOSE

The purpose of this study was to accurately predict or classify the beam GPR with an ensemble model by using machine learning for SBRT(VMAT) plans.

METHODS

A total of 128 SBRT VMAT plans with 330 arc beams were retrospectively selected, and 216 radiomics and 34 plan complexity features were calculated for each arc beam. Three models for GPR prediction and classification using support vector machine algorithm were as follows: (1) plan complexity feature-based model (plan model); (2) radiomics feature-based model (radiomics model); and (3) an ensemble model combining the two models (ensemble model). The prediction performance was evaluated by calculating the mean absolute error (MAE), root mean square error (RMSE), and Spearman's correlation coefficient (SC), and the classification performance was measured by calculating the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.

RESULTS

The MAE, RMSE and SC at the 2 %/2 mm gamma criterion in the test dataset were 1.4 %, 2.57 %, and 0.563, respectively, for the plan model; 1.42 %, and 2.51 %, and 0.508, respectively, for the radiomics model; and 1.33 %, 2.49 %, and 0.611, respectively, for the ensemble model. The accuracy, specificity, sensitivity, and AUC at the 2 %/2 mm gamma criterion in the test dataset were 0.807, 0.824, 0.681, and 0.854, respectively, for the plan model; 0.860, 0.893, 0.624, and 0.877, respectively, for the radiomics model; and 0.852, 0.871, 0.710, and 0.896, respectively, for the ensemble model.

CONCLUSIONS

The ensemble model can improve the prediction and classification performance for the GPR of SBRT (VMAT).

Impact of ovary-sparing treatment planning on plan quality, treatment time and gamma passing rates in intensity-modulated radiotherapy for stage I/II cervical cancer

BACKGROUND

This study aimed to investigate the impact of ovary-sparing intensity-modulated radiotherapy (IMRT) on plan quality, treatment time, and gamma passing rates for stage I/II cervical cancer patients.

METHODS

Fifteen stage I/II cervical cancer patients were retrospectively enrolled, and a pair of clinically suitable IMRT plans were designed for each patient, with (Group A) and without (Group B) ovary-sparing. Plan factors affecting plan quality, treatment time, and gamma passing rates, including the number of segments, monitor units, percentage of small-area segments (field area < 20 cm2), and percentage of small-MU segments (MU < 10), were compared and statistically analyzed. Key plan quality indicators, including ovarian dose, target dose coverage (D98%, D95%, D50%, D2%), conformity index, and homogeneity index, were evaluated and statistically assessed. Treatment time and gamma passing rates collected by IBA MatriXX were also compared.

RESULTS

The median ovarian dose in Group A and Group B was 7.61 Gy (range 6.71-8.51 Gy) and 38.52 Gy (range 29.84-43.82 Gy), respectively. Except for monitor units, all other plan factors were significantly lower in Group A than in Group B (all P < .05). Correlation coefficients between plan factors, treatment time, and gamma passing rates that were statistically different were all negative. Both Groups of plans met the prescription requirement (D95% ≥ 45.00 Gy) for clinical treatment. D98% was smaller for Group A than for Group B (P < .05); D50% and D2% were larger for Group A than for Group B (P < .05, P < .05). Group A plans had worse conformity index and homogeneity index than Group B plans (P < .05, P < .05). Treatment time did not differ significantly (P > .05). Gamma passing rates in Group A were higher than in Group B with the criteria of 2%/3 mm (P < .05) and 3%/2 mm (P < .05).

CONCLUSION

Despite the slightly decreased quality of the treatment plans, the ovary-sparing IMRT plans exhibited several advantages including lower ovarian dose and plan complexity, improved gamma passing rates, and a negligible impact on treatment time.

Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman's rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = - 0.608, p < 0.01; TrueBeam Rs = - 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.

Pretreatment patient-specific quality assurance prediction based on 1D complexity metrics and 3D planning dose: classification, gamma passing rates, and DVH metrics

PURPOSE

Highly modulated radiotherapy plans aim to achieve target conformality and spare organs at risk, but the high complexity of the plan may increase the uncertainty of treatment. Thus, patient-specific quality assurance (PSQA) plays a crucial role in ensuring treatment accuracy and providing clinical guidance. This study aims to propose a prediction model based on complexity metrics and patient planning dose for PSQA results.

MATERIALS AND METHODS

Planning dose, measurement-based reconstructed dose and plan complexity metrics of the 687 radiotherapy plans of patients treated in our institution were collected for model establishing. Global gamma passing rate (GPR, 3%/2mm,10% threshold) of 90% was used as QA criterion. Neural architecture models based on Swin-transformer were adapted to process 3D dose and incorporate 1D metrics to predict QA results. The dataset was divided into training (447), validation (90), and testing (150) sets. Evaluation of predictions was performed using mean absolute error (MAE) for GPR, planning target volume (PTV) HI and PTV CI, mean absolute percentage error (MAPE) for PTV D95, PTV D2 and PTV Dmean, and the area under the receiver operating characteristic (ROC) curve (AUC) for classification. Furthermore, we also compare the prediction results with other models based on either only 1D or 3D inputs.

RESULTS

In this dataset, 72.8% (500/687) plans passed the pretreatment QA under the criterion. On the testing set, our model achieves the highest performance, with the 1D model slightly surpassing the 3D model. The performance results are as follows (combine, 1D, and 3D transformer): The AUCs are 0.92, 0.88 and 0.86 for QA classification. The MAEs of prediction are 0.039, 0.046, and 0.040 for 3D GPR, 0.018, 0.021, and 0.019 for PTV HI, and 0.075, 0.078, and 0.084 for PTV CI. Specifically, for cases with 3D GPRs greater than 90%, the MAE could achieve 0.020 (combine). The MAPE of prediction is 1.23%, 1.52%, and 1.66% for PTV D95, 2.36%, 2.67%, and 2.45% for PTV D2, and 1.46%, 1.70%, and 1.71% for PTV Dmean.

CONCLUSION

The model based on 1D complexity metrics and 3D planning dose could predict pretreatment PSQA results with high accuracy and the complexity metrics play a leading role in the model. Furthermore, dose-volume metric deviations of PTV could be predicted and more clinically valuable information could be provided.

Sensitivity and specificity of Monte Carlo based independent secondary dose computation for detecting modulation-related dose errors in intensity modulated radiotherapy

BACKGROUND

The recent availability of Monte Carlo based independent secondary dose calculation (ISDC) for patient-specific quality assurance (QA) of modulated radiotherapy requires the definition of appropriate, more sensitive action levels, since contemporary recommendations were defined for less accurate ISDC dose algorithms.

PURPOSE

The objective is to establish an optimum action level and measure the efficacy of a Monte Carlo ISDC software for pre-treatment QA of intensity modulated radiotherapy treatments.

METHODS

The treatment planning system and the ISDC were commissioned by their vendors from independent base data sets, replicating a typical real-world scenario. In order to apply Receiver-Operator-Characteristics (ROC), a set of treatment plans for various case classes was created that consisted of 190 clinical treatment plans and 190 manipulated treatment plans with dose errors in the range of 1.5-2.5%. All 380 treatment plans were evaluated with ISDC in the patient geometry. ROC analysis was performed for a number of Gamma (dose-difference/distance-to-agreement) criteria. QA methods were ranked according to Area under the ROC curve (AUC) and optimum action levels were derived via Youden's J statistics.

RESULTS

Overall, for original treatment plans, the mean Gamma pass rate (GPR) for Gamma(1%, 1 mm) was close to 90%, although with some variation across case classes. The best QA criterion was Gamma(2%, 1 mm) with GPR > 90% and an AUC of 0.928. Gamma criteria with small distance-to-agreement had consistently higher AUC. GPR of original treatment plans depended on their modulation degree. An action level in terms of Gamma(1%, 1 mm) GPR that decreases with modulation degree was the most efficient criterion with sensitivity = 0.91 and specificity = 0.95, compared with Gamma(3%, 3 mm) GPR > 99%, sensitivity = 0.73 and specificity = 0.91 as a commonly used action level.

CONCLUSIONS

ISDC with Monte Carlo proves highly efficient to catch errors in the treatment planning process. For a Monte Carlo based TPS, dose-difference criteria of 2% or less, and distance-to-agreement criteria of 1 mm, achieve the largest AUC in ROC analysis.

The NCS code of practice for the quality assurance of treatment planning systems (NCS-35)

A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinviset al2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS. New sections about education, automated planning, information technology (IT) and updates are therefore added. Although the scope is photon therapy, large parts of this report will also apply to all other treatment modalities. This paper is a condensed version of these guidelines; the full version of the report in English is freely available from the NCS website (http://radiationdosimetry.org/ncs/publications). The paper starts with the scope of this report in relation to earlier reports on this subject. Next, general aspects of the commissioning process are addressed, like e.g. project management, education, and safety. It then focusses more on technical aspects such as beam commissioning and patient modeling, dose representation, dose calculation and (automated) plan optimisation. The final chapters deal with IT-related subjects and scripting, and the process of updating or upgrading the TPS.

Dosimetric performance evaluation of the Halcyon treatment platform for stereotactic radiotherapy: A pooled study

With the advancement of radiotherapy equipment, stereotactic radiotherapy (SRT) has been increasingly used. Among the many radiotherapy devices, Halcyon shows promising applications. This article reviews the dosimetric performance such as plan quality, plan complexity, and gamma passing rates of SRT plans with Halcyon to determine the effectiveness and safety of Halcyon SRT plans. This article retrieved the last 5 years of PubMed studies on the effectiveness and safety of the Halcyon SRT plans. Two authors independently reviewed the titles and abstracts to decide whether to include the studies. A search was conducted to identify publications relevant to evaluating the dosimetric performance of SRT plans on Halcyon using the key strings Halcyon, stereotactic radiosurgery, SRT, stereotactic body radiotherapy, and stereotactic ablative radiotherapy. A total of 18 eligible publications were retrieved. Compared to SRT plans on the TrueBeam, the Halcyon has advantages in terms of plan quality, plan complexity, and gamma passing rates. The high treatment speed of SRT plans on the Halcyon is impressive, while the results of its plan evaluation are also encouraging. As a result, Halcyon offers a new option for busy radiotherapy units while significantly improving patient comfort in treatment. For more accurate results, additional relevant publications will need to be followed up in subsequent studies.

Study of the prediction of gamma passing rate in dosimetric verification of intensity-modulated radiotherapy using machine learning models based on plan complexity

Objective

To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF).

Materials and methods

269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance.

Results

The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN.

Conclusion

All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.

Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process

PURPOSE

Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans.

MATERIALS AND METHODS

5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed.

RESULTS

The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR.

CONCLUSIONS

ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine

BACKGROUND

Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.

METHODS

Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance.

RESULTS

For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%.

CONCLUSIONS

The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.

An effective and optimized patient-specific QA workload reduction for VMAT plans after MLC-modelling optimization

INTRODUCTION

Many complexity metrics characterize modulated plans. First, this study aimed at identify the optimal complexity metrics to reduce workload associated to patient-specific quality assurance (PSQA) for our equipment and processes. Second, it intended to optimize our MLC modelling to improve measurement and calculation agreement with expectation of further reducing PSQA workload.

METHODS

Correlation and sensitivity at specificity equals to 1 were evaluated for PSQA results and different complexity metrics. Thresholds to stop PSQA were determined. After validation of the optimal complexity metric and threshold for our equipment and process, the MLC modelling was reviewed with a recently published methodology. This method is based on measurements with a Farmer-type ionization chamber of synchronous and asynchronous sweeping gap plans. Effect on the PSQA results and the identified threshold was investigated.

RESULTS

In our center, the most appropriate complexity metric for reducing our PSQA workload was the Modulation Complexity Score for VMAT (MCSv). The optimization of the MLC modelling significantly reduced the number of controlled plans, specifically for one of our two Varian Clinac. Any plan with a MCSv >= 0.34 is treated without PSQA.

CONCLUSION

This study rationalized and reduced our PSQA workload by approximately 30%. It is a continuing work with new TPS, machine or PSQA equipment. It encourages centers to re-evaluate their MLC modelling as well as assess the benefit of complexity metrics to streamline their PSQA workflow. An easier access, at least for reporting, at best for optimizing plans, into the TPS would be beneficial for the community.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans

Background and purpose

Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans.

Materials and methods

A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans' sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate P R γ (3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans.

Results

The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%.

Conclusions

The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac

BACKGROUND

Patient-specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C-arm linac of single-layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O-ring gantry linac "Halcyon" equipped with unique jawless stacked-and-staggered dual-layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual-layered MLC system.

PURPOSE

To evaluate the performance of ML to predict PSQA results of fixed field intensity-modulated radiation therapy (FF-IMRT) plans for linac equipped with dual-layered MLC.

METHODS AND MATERIALS

A total of 213 FF-IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1%/1, 2%/2, and 3%/2 mm criteria with a 10% threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features.

RESULTS

The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93%, 1.16%, 0.78% under 1%/1, 2%/2, and 3%/2 mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30% improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree-based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact.

CONCLUSION

For linac equipped with novel dual-layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual-layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures.

A synthesized gamma distribution-based patient-specific VMAT QA using a generative adversarial network

BACKGROUND

Artificial intelligence (AI)-based gamma passing rate (GPR) prediction has been proposed as a time-efficient virtual patient-specific QA method for the delivery of volumetric modulation arc therapy (VMAT). However, there is a limitation that the GPR value loses the locational information of dose accuracy.

PURPOSE

The objective was to predict the failing points in the gamma distribution and the GPR using a synthesized gamma distribution of VMAT QA with a deep convolutional generative adversarial network (GAN).

METHODS

The fluence maps of 270 VMAT beams for prostate cancer were measured using an electronic portal imaging device and analyzed using gamma evaluation with 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances. The 270 gamma distributions were divided into two datasets: 240 training datasets for creating a model and 30 test datasets for evaluation. The image prediction network for the fluence maps calculated by the treatment planning system (TPS) to the gamma distributions was created using a GAN. The sensitivity, specificity, and accuracy of detecting failing points were evaluated using measured and synthesized gamma distributions. In addition, the difference between measured GPR (mGPR) and predicted GPR (pGPR) values calculated from the synthesized gamma distributions was evaluated.

RESULTS

The root mean squared errors between mGPR and pGPR were 1.0%, 2.1%, 3.5%, and 3.6% for the 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The accuracies for detecting failing points were 98.9%, 96.9%, 94.7%, and 93.7% for 3%/2-mm, 2%/1-mm, 1%/1-mm, and 1%/0.5-mm tolerances, respectively. The sensitivity and specificity were the highest for 1%/0.5-mm and 3%/2-mm tolerances, which were 82.7% and 99.6%, respectively.

CONCLUSIONS

We developed a novel system using a GAN to generate a synthesized gamma distribution-based patient-specific VMAT QA. The system is promising from the point of view of quality assurance in radiotherapy because it shows high performance and can detect failing points.

Quantitative assessment of helical tomotherapy plans complexity

PURPOSE

An unnecessary amount of complexity in radiotherapy plans affects the efficiency of the treatments, increasing the uncertainty of dose deposition and its susceptibility to anatomical changes or setup errors. To date, tools for quantitatively assessing the complexity of tomotherapy plans are still limited. In this study, new metrics were developed to characterize different aspects of helical tomotherapy (HT) plans, and their actual effectiveness was investigated.

METHODS

The complexity of 464 HT plans delivered on a Radixact platform was evaluated. A new set of metrics was devised to assess beam geometry, leaf opening time (LOT) variability, and modulation over space and time. Sixty-five complexity metrics were extracted from the dataset using the newly in-house developed software library TCoMX: 29 metrics already proposed in the literature and 36 newly developed metrics. Their reciprocal relation is discussed. Their effectiveness was evaluated through correlation analyses with patient-specific quality assurance (PSQA) results.

RESULTS

An inverse linear relation was found between the average number of closed leaves and the average number of MLC openings and closures as well as between the choice of the modulation factor and the discontinuity of the field, suggesting some intrinsic link between the LOT distribution and the geometrical complexity of the MLC openings. The newly proposed metrics were at least as correlated as the existing ones to the PSQA results. Metrics describing the geometrical complexity of the MLC openings showed the strongest connection to the PSQA results. Significant correlations were found between at least one of the new metrics and the γ index passing rate P R γ % ( 3 % G , 2 mm ) $P{R}_{\gamma}\%(3\%G,2\textit{mm})$ for six out of seven groups of plans considered.

CONCLUSION

The new metrics proposed were shown to be effective to characterize more comprehensively the complexity of HT plans. A software library for their automatic extraction is described and made available.

Evaluating the effect of high-density measurement mode on patient-specific quality assurance for head and neck cancer with ArcCHECK

The high-density measurement (HDm) mode of the ArcCHECK device can achieve a twofold resolution enhancement compared to the standard measurement (Sm) mode. The aim of this study was to evaluate the effect of HDm on the gamma passing rate (GPR) for the patient-specific quality assurance (PSQA) in head and neck cancer. We retrospectively evaluated 30 patients who underwent volumetric modulated arc therapy (VMAT) for head and neck cancer. Absolute gamma analysis was performed on Sm and HDm data. We also investigated correlations between the modulation complexity score for VMAT (MCSv) and differences in the GPR between the two measurement modes. The global GPR of Sm and HDm was 81.0% ± 8.4% and 82.6% ± 7.6% for the 2%/2 mm criterion, 94.0% ± 4.1% and 94.9% ± 3.6% for the 3%/2 mm criterion, and 96.6% ± 2.4% and 97.0% ± 2.4% for the 3%/3 mm criterion, respectively. HDm slightly improved GPR (p < 0.01) for the 2%/2 mm criterion. Differences in GPR between Sm and HDm for the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria were 1.6% ± 3.0%, 0.8% ± 2.0%, and 0.4% ± 1.2%, respectively. No correlation was identified between the MCSv and the difference in GPR between Sm and HDm. Despite an improvement in GPR with HDm, the difference in GPR between Sm and HDm was approximately 2% even when the tighter criteria were used. Moreover, the change in the GPR between Sm and HDm did not depend on plan complexity. Thus, the effect of HDm on GPR is limited for the PSQA in VMAT for head and neck cancer.

Prediction of VMAT delivery accuracy using plan modulation complexity score and log-files analysis

The purpose of this study was to develop a predictive model based on plan complexity metrics and linac log-files analysis to classify the dosimetric accuracy of VMAT plans. A total of 612 VMAT plans, corresponding to 1224 arcs, were analyzed. All VMAT arcs underwent pre-treatment verification that was performed by means of the dynamic log-files generated by the linac. The comparison of predicted (by TPS) and measured (by log-files) integral fluences was performed using γ-analysis in terms of the percentage of points with γ-value smaller than one (γ%) and using a stringent 2%(local)/2mm criteria. This γ-analysis was performed by a commercial software LinacWatch. The action limits (AL) were derived from the mean values, standard deviations and the confidence limit (CL) of the γ% distribution. A plan complexity metric, the modulation complexity score (MCS), based on the aperture beam area variability and leaf sequence variability was used as input variable of the model. A binary logistic regression (LR) model was developed to classify QA results as "pass" (γ%≥AL) or "fail" (γ%<AL). Receiver operator characteristics (ROC) curves were used to determine the optimal MCS threshold to flag "failed" plans that need to be re-optimized. The model reliability was evaluated stratifying the plans in training, validation and testing groups. The confidence and action limits for γ% were found 20.1% and 79.9%, respectively. The accuracy of the model for the training and testing dataset was 97.4% and 98.0%, respectively. The optimal MCS threshold value for the identification of failed plans was 0.142, providing a true positive rate able to flag the plans failing QA of 91%. In clinical routine, the use of this MCS threshold may allow the prompt identification of overly modulated plans, then reducing the number of QA failures and improving the quality of VMAT plans used for treatment.

A Review of PRESAGE Radiochromic Polymer and the Compositions for Application in Radiotherapy Dosimetry

Recent advances in radiotherapy technology and techniques have allowed a highly conformal radiation to be delivered to the tumour target inside the body for cancer treatment. A three-dimensional (3D) dosimetry system is required to verify the accuracy of the complex treatment delivery. A 3D dosimeter based on the radiochromic response of a polymer towards ionising radiation has been introduced as the PRESAGE dosimeter. The polyurethane dosimeter matrix is combined with a leuco-dye and a free radical initiator, whose colour changes in proportion to the radiation dose. In the previous decade, PRESAGE gained improvement and enhancement as a 3D dosimeter. Notably, PRESAGE overcomes the limitations of its predecessors, the Fricke gel and the polymer gel dosimeters, which are challenging to fabricate and read out, sensitive to oxygen, and sensitive to diffusion. This article aims to review the characteristics of the radiochromic dosimeter and its clinical applications. The formulation of PRESAGE shows a delicate balance between the number of radical initiators, metal compounds, and catalysts to achieve stability, optimal sensitivity, and water equivalency. The applications of PRESAGE in advanced radiotherapy treatment verifications are also discussed.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PURPOSE

Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus.

METHODS

A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes.

RESULTS

A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness.

CONCLUSION

Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

A Comprehensive Evaluation of the Application of the Halcyon(2.0) IMRT Technique in Long-Course Radiotherapy for Rectal Cancer

Objective: To evaluate if the Halcyon(2.0) Intensity Modulation Radiotherapy (IMRT) technique has an advantage in the long-course rectal cancer radiotherapy. Methods: A total of 20 clinical IMRT plans of Halcyon(2.0) for long-course (2Gy in 25 fractions) rectal cancer radiotherapy were randomly selected. Based on the parameters of these plans, 20 TrueBeam (with the Millennium 120 MLC) plans were redesigned, respectively. The dosimetry indexes, field complexity parameters, the Gamma Passing Rates (GPR), and the delivery time of the 2 groups of plans were obtained as measures of the plan quality, the modulation complexity, the delivery accuracy, and the delivery efficiency. The differences between the 2 groups of parameters were analyzed, with P < .05 means statistically significant. Results: In terms of dosimetry, there was no significant or clinical difference between the 2 groups in critical dosimetry parameters. The Monitor Unit of the Halcyon(2.0) fields is lower than the TrueBeam fields by 26.39, while the modulation complexity score (MCS), the mean aperture area variability (AAV), and the mean leaf sequence variability (LSV) of the Halcyon(2.0) fields were 23.8%, 20%, and 2.3% larger than those of the TrueBeam fields, respectively. Neither the ArcCheck-based GPRs nor the portal-dosimetry-based GPRs in both 3%/3 mm and 2%/2 mm criteria showed the difference between the Halcyon(2.0) fields and the TrueBeam fields. The Pearson correlation coefficient between GPR(2%/2 mm) and MCS of the Halcyon(2.0) fields was 0.335, while that of the TrueBeam fields was 0.502. The mean total delivery time of the TrueBeam plans was 195.55 ± 22.86 s, while that of Halcyon(2.0) was 124.25 ± 10.42 s (P < .001), which was reduced approximatively by 36%. Conclusion: For long-course rectal cancer radiotherapy, the Halcyon(2.0) IMRT plans behave almost the same in dosimetry and delivery accuracy as the TrueBeam plans. However, the lower MU and the field modulation complexity, combined with the higher delivery efficiency, make Halcyon(2.0) a feasible and reliable platform in long-course radiotherapy for the rectal cancer.

Assessment of Delivery Quality Assurance for Stereotactic Radiosurgery With Cyberknife

Purpose

The purpose of this study is to establish and assess a practical delivery quality assurance method for stereotactic radiosurgery with Cyberknife by analyzing the geometric and dosimetric accuracies obtained using a PTW31016 PinPoint ionization chamber and EBT3 films. Moreover, this study also explores the relationship between the parameters of plan complexity, target volume, and deliverability parameters and provides a valuable reference for improving plan optimization and validation.

Methods

One hundred fifty cases of delivery quality assurance plans were performed on Cyberknife to assess point dose and planar dose distribution, respectively, using a PTW31016 PinPoint ionization chamber and Gafchromic EBT3 films. The measured chamber doses were compared with the planned mean doses in the sensitive volume of the chamber, and the measured planar doses were compared with the calculated dose distribution using gamma index analysis. The gamma passing rates were evaluated using the criteria of 3%/1 mm and 2%/2 mm. The statistical significance of the correlations between the complexity metrics, target volume, and the gamma passing rate were analyzed using Spearman’s rank correlation coefficient.

Results

For point dose comparison, the averaged dose differences (± standard deviations) were 1.6 ± 0.73% for all the cases. For planar dose distribution, the mean gamma passing rate for 3%/1 mm, and 2%/2 mm evaluation criteria were 94.26% ± 1.89%, and 93.86% ± 2.16%, respectively. The gamma passing rates were higher than 90% for all the delivery quality assurance plans with the criteria of 3%/1 mm and 2%/2 mm. The difference in point dose was lowly correlated with volume of PTV, number of beams, and treatment time for 150 DQA plans, and highly correlated with volume of PTV for 18 DQA plans of small target. DQA gamma passing rate (2%/2 mm) was a moderate significant correlation for the number of nodes, number of beams and treatment time, and a low correlation with MU.

Conclusion

PTW31016 PinPoint ionization chamber and EBT3 film can be used for routine Cyberknife delivery quality assurance. The point dose difference should be within 3%. The gamma passing rate should be higher than 90% for the criteria of 3%/1 mm and 2%/2 mm. In addition, the plan complexity and PTV volume were found to have some influence on the plan deliverability.

Evaluation of prediction and classification performances in different machine learning models for patient-specific quality assurance of head-and-neck VMAT plans

PURPOSE

The purpose of this study is to evaluate the prediction and classification performances of the gamma passing rate (GPR) for different machine learning models and to select the best model for achieving machine learning-based patient-specific quality assurance (PSQA).

METHODS

The measurement verification of 356 head-and-neck volumetric modulated arc therapy plans was performed using a diode array phantom (Delta4 Phantom), and GPR values at 2%/2 mm with global normalization and 3%/2 mm with local normalization were calculated. Machine learning models, including ridge regression (RIDGE), random forest (RF), support vector regression (SVR), and stacked generalization (STACKING), were used to predict the GPR. Each machine learning model was trained using 260 plans, and the prediction accuracy was evaluated using the remaining 96 plans. The prediction error between the measured and predicted GPR was evaluated. For the classification evaluation, the lower control limit for the measured GPR and lower control limit for predicted GPR (LCL_p ) was defined to identify whether the GPR values represent a "pass" or a "fail." LCL_p values with 99% and 99.9% confidence levels were calculated as the upper prediction limits for the GPR estimated from the linear regression between the measured and predicted GPR.

RESULTS

There was an overestimation trend of the low measured GPR. The maximum prediction errors for RIDGE, RF, SVR, and STACKING were 3.2%, 2.9%, 2.3%, and 2.2% at the global 2%/2 mm and 6.3%, 6.6%, 6.1%, and 5.5% at the local 3%/2 mm, respectively. In the global 2%/2 mm, the sensitivity was 100% for all the machine learning models except RIDGE when using 99% LCL_p . The specificity was 76.1% for RIDGE, RF, and SVR and 66.3% for STACKING; however, the specificity decreased dramatically when 99.9% LCL_p was used. In the local 3%/2 mm, however, only STACKING showed 100% sensitivity when using 99% LCL_p . The decrease in the specificity using 99.9% LCL_p was smaller than that in the global 2%/2 mm, and the specificity for RIDGE, RF, SVR, and STACKING was 61.3%, 61.3%, 72.0%, and 66.8%, respectively.

CONCLUSIONS

STACKING had better prediction accuracy for low GPR values than other machine learning models. Applying LCL_p to a regression model enabled the consistent evaluation of quantitative and qualitative GPR predictions. Adjusting the confidence level of the LCL_p helped improve the balance between the sensitivity and specificity. We suggest that STACKING can assist the safe and efficient operation of PSQA.

The impact of different optimization strategies on the agreement between planned and delivered doses during volumetric modulated arc therapy for total marrow irradiation

Aim of the study

To evaluate the agreement between planned and delivered doses and its potential correlation with the plans' complexity subjected to dosimetric verification.

Material and methods

Four isocentre volumetric modulated arc therapy for total marrow irradiation plans optimized simultaneously with (P1) and without (P2) MU reduction were evaluated dosimetrically by γ method performed in a global mode for 4 combinations of γ-index criteria (2%/2 mm, 2%/3 mm, 3%/2 mm, and 3%/3 mm). The evaluation was conducted for 4 regions (head and neck, chest, abdomen and upper pelvis, and lower pelvis and thighs) that were determined geometrically by the isocentres. The Wilcoxon test was used to detect significant differences between γ passing rate (GPR) analysis results for the P1 and P2 plans. The Pearson correlation was used to check the relationship between GPR and the plans' complexity.

Results

Except for the head and neck region, the P2 plans had better GPRs than the P1 plans. Only for hard combinations of γ-index criteria (i.e. 2%/3 mm, 2%/2 mm) were the GPRs differences between P1 and P2 clinically meaningful, and they were detected in the chest, abdomen and upper pelvis, and lower pelvis and thighs regions. The highest correlations between GPR and the indices describing the plans' complexity were found for the chest region. No correlation was found for the head and neck region.

Conclusions

The P2 plans showed better agreement between planned and delivered doses compared to the P1 plans. The GPR and the plans' complexity depend on the anatomy region and are most important for the chest region.

Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator

OBJECTIVE

High levels of beam modulation complexity (MC) and monitor units (MU) can compromise the plan deliverability of intensity-modulated radiotherapy treatments. Our study evaluates the effect of three treatment planning system (TPS) parameters on MC and MU using different multi-leaf collimator (MLC) architectures.

METHODS

192 volumetric modulated arc therapy plans were calculated using one virtual prostate phantom considering three main settings: (1) three TPS-parameters (Convergence; Aperture Shape Controller, ASC; and Dose Calculation Resolution, DCR) selected from Eclipse v15.6, (2) four levels of dose-sparing priority for organs at risk (OAR), and (3) two treatment units with same nominal conformity resolution and different MLC architectures (Halcyon-v2 dual-layer MLC, DL-MLC & TrueBeam single-layer MLC, SL-MLC). We use seven complexity metrics to evaluate the MC, including two new metrics for DL-MLC, assessed by their correlation with γ passing rate (GPR) analysis.

RESULTS

DL-MLC plans demonstrated lower dose-sparing values than SL-MLC plans (p<0.05). TPS-parameters did not change significantly the complexity metrics for either MLC architectures. However, for SL-MLC, significant variations of MU, target volume dose-homogeneity, and dose spillage were associated with ASC and DCR (p<0.05). MU were found to be correlated (highly or moderately) with all complexity metrics (p<0.05) for both MLC plans. Additionally, our new complexity metrics presented a moderate correlation with GPR (r<0.65). An important correlation was demonstrated between MC (plan deliverability) and dose-sparing priority level for DL-MLC.

CONCLUSIONS

TPS-parameters selected do not change MC for DL-MLC architecture, but they might have a potential use to control the MU, PTV homogeneity or dose spillage for SL-MLC. Our new DL-MLC complexity metrics presented important information to be considered in future pre-treatment quality assurance programs. Finally, the prominent dependence between plan deliverability and priority applied to OAR dose sparing for DL-MLC needs to be analyzed and considered as an additional predictor of GPRs in further studies.

ADVANCES IN KNOWLEDGE

Dose-sparing priority might influence in modulation complexity of DL-MLC.

Edge area metric complexity scoring of volumetric modulated arc therapy plans

Background and purpose

Aperture-based complexity metrics have been suggested as a method to score complexity of volumetric modulated arc therapy (VMAT) plans. The purpose of this study was to evaluate the edge area metric (EAM) for clinical VMAT plans on a control point and treatment plan level.

Materials and methods

EAM on a control point level was evaluated based on film measurements of 18 static beam openings originating from VMAT plans. EAM on a treatment plan level (arithmetic mean value of EAM scores for control points) was evaluated based on measurements with the Delta⁴® for 200 VMAT plans for four different treatment sites: pelvic, thorax, head and neck, and prostate. Measurements were compared to calculations and dose difference and gamma pass rates were evaluated.

Results

EAM scores on a control point level correlated with Pearson's r-values of -0.96 and -0.77 to dose difference and gamma pass rates, respectively. The prostate plans had the highest average EAM score. A connection between smaller PTVs and higher EAM scores was found. No correlation between the evaluation result and EAM on a plan level was found.

Conclusions

EAM on a control point level was shown to correlate to the difference between measured and calculated 2D dose distributions of clinical VMAT beam openings. No correlation was found for EAM on a plan level for clinical treatment plans.

Robotic MLC-based plans: A study of plan complexity

PURPOSE

The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapting and computing complexity indices previously defined for IMRT plans. Metrics were used to compare the complexity of plans created by two optimization systems and to study correlations between plan complexity and patient-specific quality assurance (PSQA) results. Relationships between pairs of metrics were also analyzed to get insight into possible interdependencies.

METHODS

Two independent in-house software platforms were developed to compute six complexity metrics: modulation complexity score (MCS), edge metric (EM), plan irregularity (PI), plan modulation (PM), leaf gap (LG), and small aperture score (SAS10). MCS and PM definitions were adapted to account for CK plans characteristics. The computed metrics were used to compare the existing optimization algorithms (sequential and VOLO) in terms of plan complexity over 24 selected cases. Metrics were then computed over a large number (103) of VOLO SBRT clinical plans from different treatment sites, mainly liver, prostate, pancreas, and spine. Pearson's r was used to study relationships between each pair of metrics. Correlation between complexity indices and PSQA results expressed as gamma index passing rates (GPR) at (3%, 1 mm) and (2%, 1 mm) was finally analyzed. Correlation was regarded as weak for absolute Pearson's r values in the range 0.2-0.39, moderate 0.4-0.59, strong 0.6-0.79, and very strong 0.8-1.

RESULTS

When compared to VOLO, sequential plans exhibited a higher complexity degree, showing lower MCS and LG values and higher EM, PM and PI values. Differences were significant for 5/6 metrics (Wilcoxon P < 0.05). The analysis of VOLO clinical plans highlighted different degrees of complexity among plans from different treatment sites, increasing from liver to prostate, pancreas, and finally, spine. Analysis of dependencies between pairs of metrics showed a very strong significant negative correlation (P < 0.01), respectively, between MCS and PM (r = -0.97), and EM and LG (-0.82). Most of the remaining pairs showed moderate to strong correlations with the exception of PI, which showed weaker correlations with the other metrics. A moderate significant correlation was observed with GPR values both at (3%, 1 mm) and (2%, 1 mm) for all metrics except PI, which showed no correlation.

CONCLUSIONS

Modulation complexity metrics were computed for CK MLC-based plans for the first time and some metrics' definitions were adapted to CK plans peculiarities. The computed metrics proved a useful tool for comparing optimization algorithms and for characterizing CK clinical plans. Strong and very strong correlations were found between some pairs of metrics. Some significant correlations were found with PSQA GPR, indicating that some indices are promising for rationalizing and reducing PSQA workload. Our results set the basis for evaluating new optimization algorithms and TPS versions in the future, as well as for comparing the complexity of CK MLC-based plans in multicenter and multiplatform comparisons.

Evaluation of dose enhancement with gold nanoparticles in kilovoltage radiotherapy using the new EGS geometry library in Monte Carlo simulation

<abstract><sec> <title>Purpose</title> <p>This study compared the dose enhancement predicted in kilovoltage gold nanoparticle-enhanced radiotherapy using the newly developed EGS lattice and the typical gold-water mixture method in Monte Carlo simulation. This new method considered the gold nanoparticle-added volume consisting of solid nanoparticles instead of a gold-water mixture. In addition, this particle method is more realistic in simulation.</p> </sec><sec> <title>Methods</title> <p>A heterogeneous phantom containing bone and water was irradiated by the 105 and 220 kVp x-ray beams. Gold nanoparticles were added to the tumour volume with concentration varying from 3–40 mg/mL in the phantom. The dose enhancement ratio (DER), defined as the ratio of dose at the tumour with and without adding gold nanoparticles, was calculated by the gold-water mixture and particle method using Monte Carlo simulation for comparison.</p> </sec><sec> <title>Results</title> <p>It is found that the DER was 1.44–4.71 (105 kVp) and 1.27–2.43 (220 kVp) for the gold nanoparticle concentration range of 3–40 mg/mL, when they were calculated by the gold-water mixture method. The DER was slightly larger and equal to 1.47–4.84 (105 kVp) and 1.29–2.5 (220 kVp) for the same concentration range, when the particle method was used. Moreover, the DER predicted by both methods increased with an increase of nanoparticle concentration, and a decrease of x-ray beam energy.</p> </sec><sec> <title>Conclusion</title> <p>The deviation of DER determined by the particle and gold-water mixture method was insignificant when considering the uncertainty in the calculation of DER (2%) in the nanoparticle concentration range of 3–40 mg/mL. It is therefore concluded that the gold-water mixture method could predict the dose enhancement as accurate as the newly developed particle method.</p> </sec></abstract>

Quantified VMAT plan complexity in relation to measurement-based quality assurance results

Volumetric‐modulated arc therapy (VMAT) treatment plans that are highly modulated or complex may result in disagreements between the planned dose distribution and the measured dose distribution. This study investigated established VMAT complexity metrics as a means of predicting phantom‐based measurement results for 93 treatments delivered on a TrueBeam linac, and 91 treatments delivered on two TrueBeam STx linacs. Complexity metrics investigated showed weak correlations to gamma passing rate, with the exception of the Modulation Complexity Score for VMAT, yielding moderate correlations. The Spearman’s rho values for this metric were 0.502 (P < 0.001) and 0.528 (P < 0.001) for the TrueBeam and TrueBeam STx, respectively. Receiver operating characteristic analysis was also performed. The aperture irregularity on the TrueBeam achieved a 53% true positive rate and a 9% false‐positive rate to correctly identify complex plans. Similarly, the average field width on the TrueBeam STx achieved a 60% true‐positive rate and an 8% false‐positive rate. If incorporated into clinical workflow, these thresholds can identify highly modulated plans and reduce the number of dose verification measurements required.

Monte Carlo calculations of radiotherapy dose in "homogeneous" anatomy

Given the substantial literature on the use of Monte Carlo (MC) simulations to verify treatment planning system (TPS) calculations of radiotherapy dose in heterogeneous regions, such as head and neck and lung, this study investigated the potential value of running MC simulations of radiotherapy treatments of nominally homogeneous pelvic anatomy. A pre-existing in-house MC job submission and analysis system, built around BEAMnrc and DOSXYZnrc, was used to evaluate the dosimetric accuracy of a sample of 12 pelvic volumetric arc therapy (VMAT) treatments, planned using the Varian Eclipse TPS, where dose was calculated with both the Analytical Anisotropic Algorithm (AAA) and the Acuros (AXB) algorithm. In-house TADA (Treatment And Dose Assessor) software was used to evaluate treatment plan complexity, in terms of the small aperture score (SAS), modulation index (MI) and a novel exposed leaf score (ELS/ELA). Results showed that the TPS generally achieved closer agreement with the MC dose distribution when treatments were planned for smaller (single-organ) targets rather than larger targets that included nodes or metastases. Analysis of these MC results with reference to the complexity metrics indicated that while AXB was useful for reducing dosimetric uncertainties associated with density heterogeneity, the residual TPS dose calculation uncertainties resulted from treatment plan complexity and TPS model simplicity. The results of this study demonstrate the value of using MC methods to recalculate and check the dose calculations provided by commercial radiotherapy TPSs, even when the treated anatomy is assumed to be comparatively homogeneous, such as in the pelvic region.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Limiting treatment plan complexity by applying a novel commercial tool

PURPOSE

A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality.

METHODS

Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination.

RESULTS

The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths.

CONCLUSIONS

This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.

Plan evaluation indices: A journey of evolution

AIM

A systemic review and analysis of evolution journey of indices, such as conformity index (CI), homogeneity index (HI) and gradient index (GI), described in the literature.

BACKGROUND

Modern radiotherapy techniques like VMAT, SRS and SBRT produce highly conformal plans and provide better critical structure and normal tissue sparing. These treatment techniques can generate a number of competitive plans for the same patients with different dose distributions. Therefore, indices like CI, HI and GI serve as complementary tools in addition to visual slice by slice isodose verification while plan evaluation. Reliability and accuracy of these indices have been tested in the past and found shortcomings and benefits when compared to one another.

MATERIAL AND METHODS

Potentially relevant studies published after 1993 were identified through a pubmed and web of science search using words "conformity index", "Homogeneity index", "Gradient index"," Stereotactic radiosurgery"," stereotactic Body radiotherapy" "complexity metrics" and "plan evaluation index". Combinations of words "plan evaluation index conformity index" were also searched as were bibliographies of downloaded papers.

RESULTS AND CONCLUSIONS

Mathematical definitions of plan evaluation indices modified with time. CI definitions presented by various authors tested at their own and could not be generalized. Those mathematical definitions of CI which take into account OAR sparing grant more confidence in plan evaluation. Gradient index emerged as a significant plan evaluation index in addition to CI whereas homogeneity index losing its credibility. Biological index base plan evaluation is becoming popular and may replace or alter the role of dosimetrical indices.