Penalization of aperture complexity in inversely planned volumetric modulated arc therapy

Quantification of Treatment Plan Deliverability in Breast Volumetric-modulated Arc Therapy With Agility Multi-leaf Collimator

BACKGROUND/AIM

The aim was to assess the complexity of breast volumetric-modulated arc therapy (VMAT) plans using various indices and to evaluate their performance through gamma analysis in predicting plan deliverability.

MATERIALS AND METHODS

A total of 285 VMAT plans for 260 patients were created using the VersaHD™ linear accelerator with a Monaco treatment planning system. Corresponding verification plans were generated using the ArcCHECK® detector, and gamma analysis was conducted employing various criteria. Twenty-eight plan complexity metrics were computed, and Pearson's correlation coefficients were determined between the gamma passing rate (GPR) and these metrics.

RESULTS

The average GPR values for all plans were 97.7%, 89.9%, and 78.0% for the 2 mm/2%, 1 mm/2%, and 1 mm/1% criteria, respectively. While most complexity metrics exhibited weak correlations with GPRs under the 2 mm/2% criterion, leaf sequence variability (LSV), plan-averaged beam area (PA), converted area metric (CAM), and edge area metric (EAM) demonstrated the most robust performance, with Pearson's correlation coefficients of 0.57, 0.50, -0.70, and -0.56, respectively.

CONCLUSION

Metrics related to beam aperture size and irregularity, such as LSV, PA, CAM and EAM, proved to be reasonable predictors of plan deliverability in breast VMAT.

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.

Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study

Background

The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results.

Materials and method

802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated.

Results

Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894).

Conclusion

Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability

BACKGROUND

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures.

PURPOSE

By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability.

METHODS

The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation.

RESULTS

The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific.

CONCLUSION

This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters

In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan's complexity. This automation improved efficiency by saving time and resources in treatment planning.

Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans

Background and purpose

Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity.

Materials and methods

Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use.

Results

Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization.

Conclusions

ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.

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

Background and purpose

In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses.

Materials and methods

A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR).

Results

The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA  ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR's determination coefficients (R2 ) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42).

Conclusions

CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

MU variability in CBCT-guided online adaptive radiation therapy

PURPOSE

CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.

METHODS

Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.

RESULTS

MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.

CONCLUSIONS

MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.

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.

Clinical association between plan complexity and the local-recurrence-free-survival of non-small-cell lung cancer patients receiving stereotactic body radiation therapy

PURPOSE

To investigate the clinical impact of plan complexity on the local recurrence-free survival (LRFS) of non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

METHODS

Data from 123 treatment plans for 113 NSCLC patients were analyzed. Plan-averaged beam modulation (PM), plan beam irregularity (PI), monitor unit/Gy (MU/Gy) and spherical disproportion (SD) were calculated. The γ passing rates (GPR) were measured using ArcCHECK 3D phantom with 2 %/2mm criteria. High complexity (HC) and low complexity (LC) groups were statistically stratified based on the aforementioned metrics, using cutoffs determined by their significance in correlation with survival time, as calculated using the R-3.6.1 packages. Kaplan-Meier analysis, Cox regression, and Random Survival Forest (RSF) models were employed for the analysis of local recurrence-free survival (LRFS). Propensity-score-matched pairs were generated to minimize bias in the analysis.

RESULTS

The median follow-up time for all patients was 25.5 months (interquartile range 13.4-41.2). The prognostic capacity of PM was suggested using RSF, based on Variable Importance and Minimal Depth methods. The 1-, 2-, and 3-year LRFS rates in the HC group were significantly lower than those in the LC group (p = 0.023), when plan complexity was defined by PM. However, no significant difference was observed between the HC and LC groups when defined by other metrics (p > 0.05). All γ passing rates exceeded 90.5 %.

CONCLUSIONS

This study revealed a significant association between higher PM and worse LRFS in NSCLC patients treated with SBRT. This finding offers additional clinical evidence supporting the potential optimization of pre-treatment quality assurance protocols.

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.

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).

A Beam Angle Selection Method to Improve Plan Robustness Against Position Error in Intensity-Modulated Radiotherapy for Left-Sided Breast Cancer

PURPOSE

We report a dosimetric study in whole breast irradiation (WBI) of plan robustness evaluation against position error with two radiation techniques: tangential intensity-modulated radiotherapy (T-IMRT) and multi-angle IMRT (M-IMRT).

METHODS

Ten left-sided patients underwent WBI were selected. The dosimetric characteristics, biological evaluation and plan robustness were evaluated. The plan robustness quantification was performed by calculating the dose differences (Δ) of the original plan and perturbed plans, which were recalculated by introducing a 3-, 5-, and 10-mm shift in 18 directions.

RESULTS

M-IMRT showed better sparing of high-dose volume of organs at risk (OARs), but performed a larger low-dose irradiation volume of normal tissue. The greater shift worsened plan robustness. For a 10-mm perturbation, greater dose differences were observed in T-IMRT plans in nearly all directions, with higher ΔD98%, ΔD95%, and ΔDmean of CTV Boost and CTV. A 10-mm shift in inferior (I) direction induced CTV Boost in T-IMRT plans a 1.1 (ΔD98%), 1.1 (ΔD95%), and 1.7 (ΔDmean) times dose differences greater than dose differences in M-IMRT plans. For CTV Boost, shifts in the right (R) and I directions generated greater dose differences in T-IMRT plans, while shifts in left (L) and superior (S) directions generated larger dose differences in M-IMRT plans. For CTV, T-IMRT plans showed higher sensitivity to a shift in the R direction. M-IMRT plans showed higher sensitivity to shifts in L, S, and I directions. For OARs, negligible dose differences were found in V20 of the lungs and heart. Greater ΔDmax of the left anterior descending artery (LAD) was seen in M-IMRT plans.

CONCLUSION

We proposed a plan robustness evaluation method to determine the beam angle against position uncertainty accompanied by optimal dose distribution and OAR sparing.

A novel lung SBRT treatment planning: Inverse VMAT plan with leaf motion limitation to ensure the irradiation reproducibility of a moving target

This study exposed the implementation of a novel technique (VMATLSL) for the planning of moving targets in lung stereotactic body radiation therapy (SBRT). This new technique has been compared to static conformal radiotherapy (3D-CRT), volumetric-modulated arc therapy (VMAT), and dynamic conformal arc (DCA). The rationale of this study was to lower geometric complexity (54.9% lower than full VMAT) and hence ensure the reproducibility of the treatment delivery by reducing the risk for interplay errors induced by respiratory motion. Dosimetry metrics were studied with a cohort of 30 patients. Our results showed that leaf speed limitation provided conformal number (CN) close to the VMAT (median CN of VMATLSL is 0.78 vs 0.82 for full VMAT) and was a significant improvement on 3D-CRT and DCA with segment-weight optimized (respectively 0.55 and 0.57). This novel technique is an alternative to VMAT or DCA for lung SBRT treatments, combining independence from the patient's breathing pattern, from the size and amplitude of the lesion, free from interplay effect, and with dosimetry metrics close to the best that could be achieved with full VMAT.

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.

The effects of flattening filter-free beams and aperture shape controller on the complexity of conventional large-field treatment plans

PURPOSE

The purpose of this study was to investigate the impact of using flattening filter-free (FFF) beams and the aperture shape controller (ASC) on the complexity of conventional large-field treatment plans.

METHODS AND MATERIALS

A total of 24 head and neck (H&N) and 24 prostate with pelvic nodes treatment plans were used in this study. Each plan was reoptimized using the original clinical objectives with both flattened and FFF beams, as well as six different ASC settings. The dosimetric qualities of each plan cohort were evaluated using commonly used dose-volume histogram values, and plan complexities were assessed through metrics including monitor unit (MU)/Dose, change in gantry speed, multileaf collimator (MLC) speed, the edge area ratio metric (EM), and the equivalent square length.

RESULTS

No significant differences in dosimetric qualities were found between plans with flattened and FFF beams. The ASC settings did not have significant effects on dosimetric qualities in the H&N plan cohort, but the "very high" ASC setting resulted in poorer dosimetric results for the prostate plans. Plans with FFF beams had significantly higher MU/Dose compared to plans with flattened beams. The use of flattening filter (FF) had significant effects on the change in gantry speed, with flattened beams producing plans that required higher change in gantry speed. However, the FF did not have significant effects on MLC speed, EM, or equivalent square length. In contrast, ASC settings had significant effects on these three metrics; increasing the ASC level resulted in plans with decreasing MLC speed, lower edge area ratio, and higher equivalent square length.

CONCLUSION

This study demonstrated that using FFF beams with various ASC settings, except for the "very high" level, can produce plans with reduced complexities without compromising dosimetric qualities in conventional large-field treatment plans.

Implementation of a knowledge-based decision support system for treatment plan auditing through automation

BACKGROUND

Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross-campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members.

PURPOSE

A knowledge-based automated anomaly-detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution.

METHODS

A total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre-processed. A knowledge-based anomaly detection algorithm, namely, "isolation forest" (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto-populated parameters were used to guide the manual auditing process and validated by two plan auditors.

RESULTS

The two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year.

CONCLUSION

iForest effectively detects anomalous plans and strengthens our cross-campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently.

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.

Dosimetric comparison of VMAT standard optimization (SO) and multi-criteria optimization (MCO) treatment plans with standard mode delivery (STD) or sliding window (SW) for head and neck cancer

PURPOSE

A new development on the RayStation treatment planning system (TPS) allows a plan to be planned by imposing a constraint on the leaf sequencing: all leaves move in the same direction before moving again in the opposite direction to create a succession of sliding windows (SWs). The study aims to investigate this new leaf sequencing, coupled with standard optimization (SO) and multi-criteria optimization (MCO) and to compare it with the standard sequencing (STD).

METHODS

Sixty plans were replanned for 10 head and neck cancer patients (two dose levels simultaneously SIB, 56 and 70 Gy in 35 fractions). All plans were compared, and a Wilcoxon signed-rank test was performed. Pre-processing QA and metrics of multileaf collimator (MLC) complexity were studied.

RESULTS

All methodologies met the dose requirements for the planning target volumes (PTVs) and organs at risk (OARs). SO demonstrates significantly best results for homogeneity index (HI), conformity index (CI), and target coverage (TC). SO-SW gives best results for PTVs (D98% and D2% ) but the differences between techniques are less than 1%. Only the D2%,PTV-56 Gy is higher with both MCO methods. MCO-STD offer the best sparing OARs (parotids, spinal cord, larynx, oral cavity). The gamma passing rates (GPRs) with 3%/3 mm criteria between the measured and calculated dose distributions are higher than 95%, slightly lowest with SW. The number of monitor units (MUs) and MLC metrics are higher in SW show a higher modulation.

CONCLUSIONS

All plans are feasible for the treatment. A clear advantage of SO-SW is that the treatment plan is more straightforward to planning by the user due to the more advanced modulation. MCO stands out for its ease of use and will allow a less experienced user to offer a better plan than in SO. In addition, MCO-STD will reduce the dose to the OARs while maintaining good TC.

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.

Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning

AIM OF THE STUDY

To elucidate the important factors and their interplay that drive performance on IMRT phantoms from the Imaging and Radiation Oncology Core (IROC).

METHODS

IROC's IMRT head and neck phantom contains two targets and an organ at risk. Point and 2D dose are measured by TLDs and film, respectively. 1,542 irradiations between 2012-2020 were retrospectively analyzed based on output parameters, complexity metrics, and treatment parameters. Univariate analysis compared parameters based on pass/fail, and random forest modeling was used to predict output parameters and determine the underlying importance of the variables.

RESULTS

The average phantom pass rate was 92% and has not significantly improved over time. The step-and-shoot irradiation technique had significantly lower pass rates that significantly affected other treatment parameters' pass rates. The complexity of plans has significantly increased with time, and all aperture-based complexity metrics (except MCS) were associated with the probability of failure. Random forest-based prediction of failure had an accuracy of 98% on held-out test data not used in model training. While complexity metrics were the most important contributors, the specific metric depended on the set of treatment parameters used during the irradiation.

CONCLUSION

With the prevalence of errors in radiotherapy, understanding which parameters affect treatment delivery is vital to improve patient treatment. Complexity metrics were strongly predictive of irradiation failure; however, they are dependent on the specific treatment parameters. In addition, the use of one complexity metric is insufficient to monitor all aspects of the treatment plan.

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.

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

Objective.Reducing plan complexity in intensity modulated radiation therapy (IMRT) to ensure dosimetric accuracy and delivery efficiency of the radiation treatment plans. We propose a novel approach by representing the beamlet intensities using an incomplete wavelet basis that explicitly excludes fluctuating intensity maps from the decision space (explicit hard constraint). This technique provides a built-in wavelet-induced smoothness that improves both dosimetric plan quality and delivery efficiency.Approach.The beamlet intensity maps need to be especially smooth in the leaf travel direction (referred to as theX-direction). We treat the intensity map of each beam as a 2D image and represent it using the wavelets corresponding to low-frequency changes in theX-direction (i.e. approximation and horizontal). The absence of wavelets corresponding to high-frequency changes (i.e. vertical and diagonal) induces built-in smoothness. We still utilize a regularization term in the objective function to promote smoothness in theY-direction (perpendicular to theX-direction) and further possible smoothness in theX-direction. This technique has been tested on three patient cases of different disease sites (paraspinal, lung, prostate) and all final evaluations and comparisons have been performed on an FDA-approved commercial treatment planning system (Varian Eclipse^TM).Main results.Wavelet-induced smoothness reduced monitor units by about 10%, 45%, and 14% for paraspinal, lung, and prostate cases, respectively. It also improved organ at risk sparing, especially on the complex paraspinal case where it resulted in about 7%, 13%, and 14% less mean dose to esophagus, lung, and cord, respectively. Moreover, built-in wavelet-induced smoothness desensitizes the results to changing the weight associated to the regularization term, and thereby mitigates the weight fine-tuning difficulty.Significance.Fluence smoothness is often achieved by smoothing the beamlet intensity maps using a proper regularization term in the objective function aiming at disincentivizing fluctuation in the beamlet intensities (implicit soft constraint). This work reports a novel application of wavelets in imposing an explicit smoothness hard constraint in the search space using an incomplete wavelet basis. This idea has been successfully applied to exclude complex and clinically irrelevant radiation plans from the search space. The code and pertained models along with a sample dataset are released on our LowDimRT GitHub (https://github.com/PortPy-Project/LowDimRT).

Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology

The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.

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.

Clinical Feasibility of Using Single-isocentre Non-coplanar Volumetric Modulated Arc Therapy Combined with Non-coplanar Cone Beam Computed Tomography in Hypofractionated Stereotactic Radiotherapy for Five or Fewer Multiple Intracranial Metastases

AIMS

To evaluate the clinical feasibility of single-isocentre non-coplanar volumetric modulated arc therapy (NC-VMAT) with non-coplanar cone beam computed tomography (NC-CBCT) in hypofractionated stereotactic radiotherapy (HSRT) for five or fewer multiple brain metastases.

MATERIALS AND METHODS

Ten patients with multiple brain metastases who underwent single-isocentre NC-VMAT HSRT with limited couch rotations (within ±45°) and NC-CBCT with a limited scanning range (150-200°) were included in the current analysis. Conventional single-isocentre coplanar VMAT (C-VMAT) plans were generated and compared with NC-VMAT plans. The intracranial response and toxicities of single-isocentre NC-VMAT HSRT were also evaluated.

RESULTS

Compared with C-VMAT, NC-VMAT generated better target conformity (P < 0.05), a lower gradient index (P < 0.05) and better normal brain tissue sparing, especially for volume ≥12 Gy, with a median reduction of 12.65 cm³. For 45° couch rotation, NC-CBCT produced sufficient image quality to differentiate bony anatomy, even with a 150° scanning range, which could be successfully used for patient set-up correction. After NC-CBCT, 57.1% of the measured non-coplanar set-up errors exceeded the threshold value. The median gamma passing rate of NC-VMAT was higher than that of C-VMAT plans (P < 0.05). The non-coplanar beam of NC-VMAT with NC-CBCT corrections exhibited superior gamma passing rate to that without NC-CBCT corrections. The intracranial objective response rate and disease control rate for all patients were 80% (8/10) and 100% (10/10), respectively, and the most common toxicities were headache (20%) and dizziness (20%).

CONCLUSION

NC-VMAT with limited couch rotation (within ±45°) combined with NC-CBCT with a limited scanning range (150-200°) markedly improves the plan quality and set-up accuracy in single-isocentre multiple-target HSRT.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

BACKGROUND AND PURPOSE

Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis.

MATERIALS AND METHODS

A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (M_t), while the other one at achieving the highest mean sample quality (R_t). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored.

RESULTS

Both update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [-0.2 %;-14.7 %] while plan complexity showed only minor changes. Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability.

CONCLUSIONS

Repeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

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.

Using machine learning to predict gamma passing rate in volumetric-modulated arc therapy treatment plans

PURPOSE

This study aims to develop an algorithm to predict gamma passing rate (GPR) in the volumetric-modulated arc therapy (VMAT) technique.

MATERIALS AND METHODS

A total of 118 clinical VMAT plans, including 28 mediastina, 25 head and neck, 40 brains intensity-modulated radiosurgery, and 25 prostate cases, were created in RayStation treatment planning system for Edge and TrueBeam linacs. In-house scripts were developed to compute Modulation indices such as plan-averaged beam area (PA), plan-averaged beam irregularity (PI), total monitor unit (MU), leaf travel/arc length, mean dose rate variation, and mean gantry speed variation. Pretreatment verifications were performed on ArcCHECK phantom with SNC software. GPR was calculated with 3%/2 mm and 10% threshold. The dataset was randomly split into a training (70%) and a test (30%) dataset. A random forest regression (RFR) model and support vector regression (SVR) with linear kernel were trained to predict GPR using the complexity metrics as input. The prediction performance was evaluated by calculating the mean absolute error (MAE), R² , and root mean square error (RMSE).

RESULTS

RMSEs at γ 3%/2 mm for RFR and SVR were 1.407 ± 0.103 and 1.447 ± 0.121, respectively. MAE was 1.14 ± 0.084 for RFR and 1.101 ± 0.09 for SVR. R² was equal to 0.703 ± 0.027 and 0.689 ± 0.053 for RFR and SVR, respectively. GPR of 3%/2 mm with a 10% threshold can be predicted with an error smaller than 3% for 94% of plans using RFR and SVR models. The most important metrics that had the greatest impact on how accurately GPR can be predicted were determined to be the PA, PI, and total MU.

CONCLUSION

In terms of its prediction values and errors, SVR (linear) appeared to be comparable with RFR for this dataset. Based on our results, the PA, PI, and total MU calculations may be useful in guiding VMAT plan evaluation and ultimately reducing uncertainties in planning and radiation delivery.

An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

PURPOSE

To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced.

METHODS

The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units.

RESULTS

The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work.

CONCLUSIONS

An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.

A measurement validation of improved plan deliverability with monitor unit objective tool for spine stereotactic ablative radiotherapy

Spine stereotactic body radiation therapy (SBRT) uses high dose per fraction for palliative pain control. The treatment plans are often heavily modulated due to close proximity to spinal cord and this can lead to poor plan quality which are susceptible to dose delivery discrepancy. Therefore, we aim to assess the effectiveness of the monitor unit (MU) objective tool in Eclipse treatment planning systems in modulating the plan complexity to improve the plan quality in spine SBRT. Seven retrospective spine SBRT plans are re-optimized using the MU objective tool in Eclipse TPS v13.6 and were compared with the original plans. The dose metrics of the tumor PTV were compared using D_1cc. D_99%, D_95%, D_0.03cc, D_0.1cc, D_0.35cc and D_1cc, and that of cord PRV were compared using D_0.03cc, D_0.1cc, D_0.35cc. Four different plan complexities were also calculated for the original and re-optimized plans to quantify the impact of the tool on the modulation. Patient specific quality assurance measurements were performed with Stereophan and SRS MapCheck, and analyzed using the 1%/1-mm and 2%/2-mm criteria with gamma analysis. The dose metrics of the PTV and cord PRV of the re-optimized and original plans are similar and still meet the planning dose constraints. In particular, the PTV dose coverage has a small percentage difference of (0.15 ± 1.33)% and (0.01 ± 1.04)% for D_99% and D_95%, respectively. The 4 calculated plan complexity metrics consistently show that the re-optimized plans are quantitatively less complex than the original plan. The gamma passing rate of the re-optimized plans improved from (92.2 ± 2.0)% to (94.2 ± 1.6)% with the 1%/1-mm criterion, and (98.7 ± 1.0)% to (99.5 ± 0.3)% with the 2%/2-mm criterion. Overall, the re-optimized plans achieve at least a 10% MU reduction (11.7% to 24.6%). Our study shows that optimization with the MU objective tool can reduce plan complexity and improves dose delivery accuracy, while not compromising the dose distribution.

Knowledge-based planning for multi-isocenter VMAT total marrow irradiation

Purpose

Total marrow irradiation (TMI) involves optimization of extremely large target volumes and requires extensive clinical experience and time for both treatment planning and delivery. Although volumetric modulated arc therapy (VMAT) achieves substantial reduction in treatment delivery time, planning process still presents a challenge due to use of multiple isocenters and multiple overlapping arcs. We developed and evaluated a knowledge-based planning (KBP) model for VMAT-TMI to address these clinical challenges.

Methods

Fifty-one patients previously treated in our clinic were selected for the model training, while 22 patients from another clinic were used as a test set. All plans used a 3-isocenter to cover sub-target volumes of head and neck (HN), chest, and pelvis. Chest plan was performed first and then used as the base dose for both the HN and pelvis plans to reduce hot spots around the field junctions. This resulted in a wide range of dose-volume histograms (DVH). To address this, plans without the base-dose plan were optimized and added to the library to train the model.

Results

KBP achieved our clinical goals (95% of PTV receives 100% of Rx) in a single day, which used to take 4-6 days of effort without KBP. Statistically significant reductions with KBP were observed in the mean dose values to brain, lungs, oral cavity and lenses. KBP substantially improved 105% dose spillage (14.1% ± 2.4% vs 31.8% ± 3.8%), conformity index (1.51 ± 0.06 vs 1.81 ± 0.12) and homogeneity index (1.25 ± 0.02 vs 1.33 ± 0.03).

Conclusions

KBP improved dosimetric performance with uniform quality. It reduced dependence on planner experience and achieved a factor of 5 reduction in planning time to produce quality plans to allow its wide-spread clinical implementation.

Comparative study of SRS end-to-end QA processes of a diode array device and an anthropomorphic phantom loaded with GafChromic XD film

PURPOSE

End-to-end testing (E2E) is a necessary process for assessing the readiness of the stereotactic radiosurgery (SRS) program and annual QA of an SRS system according to the AAPM MPPG 9a. This study investigates the differences between using a new SRS MapCHECK (SRSMC) system and an anthropomorphic phantom film-based system in a large network with different SRS delivery techniques.

METHODS AND MATERIALS

Three SRS capable Linacs (Varian Medical Systems, Palo Alto, CA) at three different regional sites were chosen to represent a hospital network, a Trilogy with an M120 multi-leaf collimator (MLC), a TrueBeam with an M120 MLC, and a TrueBeam Stx with an HD120 MLC. An anthropomorphic STEEV phantom (CIRS, Norfolk, VA) and a phantom/diode array: StereoPHAN/SRSMC (Sun Nuclear, Melbourne, FL) were CT scanned at each site. The new STV-PHANTOM EBT-XD films (Ashland, Bridgewater, NJ) were used. Six plans with various complexities were measured with both films and SRSMC in the StereoPHAN to establish their dosimetric correlations. Three SRS cranial plans with a total of sixteen fields using dynamic conformal arc and volumetric-modulated arc therapy, with 1-4 targets, were planned with Eclipse v15.5 treatment planning system (TPS) using a custom SRS beam model for each machine. The dosimetric and localization accuracy were compared. The time of analysis for the two systems by three teams of physicists was also compared to assess the throughput efficiency.

RESULTS

The correlations between films and SRSMC were found to be 0.84 (p = 0.03) and 0.16 (p = 0.76) for γ (3%, 1 mm) and γ (3%, 2 mm), respectively. With film, the local dose differences (ΔD) relative to the average dose within the 50% isodose line from the three sites were found to be -3.2%-3.7%. The maximum localization errors (E_local ) were found to be within 0.5 ± 0.2 mm. With SRSMC, the ΔD was found to be within 5% of the TPS calculation. E_local were found to be within 0.7 to 1.1 ± 0.4 mm for TrueBeam and Trilogy, respectively. Comparing with film, an additional uncertainty of 0.7 mm was found with SRSMC. The delivery and analysis times were found to be 6 and 2 h for film and SRSMC, respectively.

CONCLUSIONS

The SRS MapCHECK agrees dosimetrically with the films within measurement uncertainties. However, film dosimetry shows superior sub-millimeter localization resolving power for the MPPG 9a implementation.

Influence of beamlet width on dynamic IMRT plan quality in nasopharyngeal carcinoma

Objective

This study aimed to identify the effects of beamlet width on dynamic intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma (NPC) and determine the optimal parameters for the most effective radiotherapy plan.

Methods

This study evaluated 20 patients with NPC were selected for dynamic IMRT. Only the beamlet width in the optimization parameters was changed (set to 2, 4, 6, 8, and 10 mm that were named BL02, BL04, BL06, BL08, and BL10, respectively) to optimize the results of the five groups of plans. Using the plan quality scoring system, the dose results of the planning target volumes (PTVs) and organs at risks (OARs) were analyzed objectively and comprehensively. The lower the quality score, the better the quality of the plan. The efficiency and accuracy of plan execution were evaluated using monitor units (MUs) and plan delivery time (PDT).

Results

The BL04 mm group had the lowest quality score for the targets and OARs (0.087), while the BL10 mm group had the highest total score (1.249). The BL04 mm group had the highest MUs (837 MUs) and longest PDT (358 s). However, the MUs range of each group plan was below 100 MUs, and the PDT range was within 30 s. In the BL02, BL04, BL06, BL08, and BL10 plans, <5 MUs segments accounted for 33%, 16%, 24%, 33%, and 40% of total segments, respectively, with which the lowest was in the BL04 mm group.

Conclusion

Smaller beamlet widths have not only reduced OARs dose while maintaining high dose coverage to the PTVs, but also lead to more MUs that would produce greater PDT. Considering the quality and efficiency of dynamic IMRT, the beamlet width value of the Monaco treatment planning system set to 4 mm would be optimal for NPC.

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.

Prospective clinical validation of virtual patient-specific quality assurance of VMAT radiation therapy plans

PURPOSE

Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiotherapy treatment workflow. Paired with technological refinements in modern radiotherapy, research towards measurement-free PSQA has seen increased interest over the last five years. However, these efforts have not been clinically implemented or prospectively validated in the U.S. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA.

METHODS

An XGBoost machine learning model was designed to predict PSQA outcomes of VMAT plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over three months of measurements at our clinic to assess safety and efficiency gains.

RESULTS

Over three months, VQA predictions for 445 VMAT plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08 +/- 0.77%, with a maximum absolute error of 2.98%. Employing a 1% prediction threshold (i.e. plans predicted to have an absolute error of less than 1% would not require a measurement) would yield a 69.2% reduction in QA workload - saving 32.5 hours per month on average - with 81.5%/72.4%/0.81 sensitivity/specificity/AUC at a 3% clinical threshold and 100%/70%/0.93 sensitivity/specificity/AUC at a 4% clinical threshold.

CONCLUSION

This is the first prospective clinical implementation and validation of VQA in the U.S., which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.

Evaluation of Plan Robustness Using Hybrid Intensity-Modulated Radiotherapy (IMRT) and Volumetric Arc Modulation Radiotherapy (VMAT) for Left-Sided Breast Cancer

Purpose: We aim to evaluate the robustness of multi-field IMRT and VMAT plans to target motion for left-sided BC radiotherapy. Methods: The 7-field hybrid IMRT (7F-H-IMRT) and 2-arc VMAT (2A-VMAT) plans were generated for ten left-sided BC patients. Shifts of 3 mm, 5 mm, and 10 mm in six directions were introduced and the perturbed dose distributions were recalculated. The dose differences (∆D) of the original plan and perturbed plan corresponded to the plan robustness for the structure. Results: Higher ∆D_98%, ∆D_95%, and ∆D_mean of CTV were observed in 2A-VMAT plans, which induced higher tumor control probability reductions. A higher ∆Dmean of CTV Boost was found in 7F-H-IMRT plans despite lower ∆D_98% and ∆D_95%. Shifts in the S-I direction exerted the largest effect on CTV and CTV Boost. Regarding OARs, shifts in R, P, and I directions contributed to increasing the received dose. The 2A-VMAT plans performed better dose sparing, but had a higher robustness in a high-dose volume of the left lung and heart. The 2A-VMAT plans decreased the max dose of LAD but exhibited lower robustness. Conclusion: The 2A-VMAT plans showed higher sensitivity to position deviation. Shifts in the S-I direction exerted the largest effect for CTV and CTV Boost.

Clinical evaluation of two AI models for automated breast cancer plan generation

Background

Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists.

Methods

For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time.

Results

The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model.

Conclusions

Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-01993-9.

Correlation Between Average Segment Width and Gamma Passing Rate as a Function of MLC Position Error in Volumetric Modulated Arc Therapy

Objective:

This study analyzed the correlation between the average segment width (ASW) and gamma passing rate according to the multi-leaf collimator (MLC) position error.

Method:

To evaluate the changes in the gamma passing rate according to the MLC position error, 21 volumetric modulated arc therapy (VMAT) plans were generated using pelvic lymph node metastatic prostate cancer patient's data which is sensitive to MLC position errors as they involve several long, narrow, irregular fields. The ASW for each VMAT plan was calculated using our own code developed using Visual Basic for Applications (VBA). The gamma passing rate of the VMAT plan according to the MLC position error was evaluated using ArcCHECK (Sun Nuclear, Melbourne, FL, USA) while inducing symmetric MLC position errors in 0.25 mm intervals from −1 mm to +1 mm in the infinity medical linear accelerator (Elekta AB, Stockholm, Sweden). Finally, we examined the correlation between the change in the passing rate (γgradient) due to the MLC position error and the ASW in VMAT through linear regression analysis using the least squares method.

Results:

The ASW and γgradient were found to have a linear correlation according to the MLC position error, and the coefficient of determination was 0.88. For a 1 mm position error of MLC in VMAT, the gamma passing rate improved by approximately 11.9% as the ASW increased by 10 mm.

Conclusion:

These results are expected to be employed as guidelines to minimize the dose uncertainty due to MLC position error in VMAT.

Feasibility of function-guided lung treatment planning with parametric response mapping

Purpose

Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning.

Methods and materials

A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient.

Results

PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity.

Conclusions

PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.

Improvement Using Planomics Features on Prediction and Classification of Patient-Specific Quality Assurance Using Head and Neck Volumetric Modulated Arc Therapy Plan

Purpose: This study aimed to evaluate the utility of a new plan feature (planomics feature) for predicting the results of patient-specific quality assurance using the head and neck (H&N) volumetric modulated arc therapy (VMAT) plan. Methods: One hundred and thirty-one H&N VMAT plans in our institution from 2019 to 2021 were retrospectively collected. Dosimetric verification for all plans was carried out using the portal dosimetry system integrated into the Eclipse treatment planning system based on the electronic portal imaging devices. Gamma passing rates (GPR) were analyzed using three gamma indices of 3%/3 mm, 3%/2 mm, and 2%/2 mm with a 10% dose threshold. Forty-eight conventional features affecting the dose delivery accuracy were used in the study, and 2,476 planomics features were extracted based on the radiotherapy plan file. Three prediction and classification models using conventional features (CF), planomics features (PF), and hybrid features (HF) combining two sets of features were constructed by the gradient boosting regressor (GBR) and Ridge classifier for each GPR of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. The absolute prediction error (APE) and the area under the curve (AUC) were adopted for assessing the performance of prediction and classification models. Results: In the GPR prediction, the average APE of the models using CF, PF, and HF was 1.3 ± 1.2%/3.6 ± 3.0%, 1.7 ± 1.5%/3.8 ± 3.5%, and 1.1 ± 1.0%/4.1 ± 3.1% for 2%/2 mm; 0.7 ± 0.6%/2.0 ± 2.0%, 1.0±1.1%/2.2 ± 1.8%, and 0.6 ± 0.6%/2.2 ± 1.9% for 3%/2 mm; and 0.4 ± 0.3%/1.2 ± 1.2%, 0.4±0.5%/1.3 ± 1.0%, and 0.3±0.3%/1.2 ± 1.1% for 3%/3 mm, respectively. In the regression prediction, three models give a similar modeling performance for predicting the GPR. The classification results were 0.67 ± 0.03/0.66 ± 0.07, 0.77 ± 0.03/0.73 ± 0.06, and 0.78 ± 0.02/0.75 ± 0.04 for 3%/3 mm, respectively. For 3%/2 mm, the AUCs of the training and testing cohorts were 0.64 ± 0.03/0.62 ± 0.07, 0.70 ± 0.03/0.67 ± 0.06, and 0.75 ± 0.03/0.71 ± 0.07, respectively, and for 2%/2 mm, the average AUCs of the training and testing cohorts were 0.72 ± 0.03/0.72 ± 0.06, 0.78 ± 0.04/0.73 ± 0.07, and 0.81 ± 0.03/0.75 ± 0.06, respectively. In the classification, the PF model has a better classification performance than the CF model. Moreover, the HF model provides the best result among the three classifications models. Conclusions: The planomics features can be used for predicting and classifying the GPR results and for improving the model performance after combining the conventional features for the GPR classification.

Report of AAPM Task Group 155: Megavoltage photon beam dosimetry in small fields and non-equilibrium conditions

Small-field dosimetry used in advance treatment technologies poses challenges due to loss of lateral charged particle equilibrium (LCPE), occlusion of the primary photon source, and the limited choice of suitable radiation detectors. These challenges greatly influence dosimetric accuracy. Many high-profile radiation incidents have demonstrated a poor understanding of appropriate methodology for small-field dosimetry. These incidents are a cause for concern because the use of small fields in various specialized radiation treatment techniques continues to grow rapidly. Reference and relative dosimetry in small and composite fields are the subject of the International Atomic Energy Agency (IAEA) dosimetry code of practice that has been published as TRS-483 and an AAPM summary publication (IAEA TRS 483; Dosimetry of small static fields used in external beam radiotherapy: An IAEA/AAPM International Code of Practice for reference and relative dose determination, Technical Report Series No. 483; Palmans et al., Med Phys 45(11):e1123, 2018). The charge of AAPM task group 155 (TG-155) is to summarize current knowledge on small-field dosimetry and to provide recommendations of best practices for relative dose determination in small megavoltage photon beams. An overview of the issue of LCPE and the changes in photon beam perturbations with decreasing field size is provided. Recommendations are included on appropriate detector systems and measurement methodologies. Existing published data on dosimetric parameters in small photon fields (e.g., percentage depth dose, tissue phantom ratio/tissue maximum ratio, off-axis ratios, and field output factors) together with the necessary perturbation corrections for various detectors are reviewed. A discussion on errors and an uncertainty analysis in measurements is provided. The design of beam models in treatment planning systems to simulate small fields necessitates special attention on the influence of the primary beam source and collimating devices in the computation of energy fluence and dose. The general requirements for fluence and dose calculation engines suitable for modeling dose in small fields are reviewed. Implementations in commercial treatment planning systems vary widely, and the aims of this report are to provide insight for the medical physicist and guidance to developers of beams models for radiotherapy treatment planning systems.

Initial clinical experience of patient-specific QA of treatment delivery in online adaptive radiotherapy using a 1.5 T MR-Linac

Purpose. This study aims to evaluate the performance of a commercial 1.5 T MR-Linac by analyzing its patient-specific quality assurance (QA) data collected during one full year of clinical operation.Methods and Materials. The patient-specific QA system consisted of offline delivery QA (DQA) and online calculation-based QA. Offline DQA was based on ArcCHECK-MR combined with an ionization chamber. Online QA was performed using RadCalc that calculated and compared the point dose calculation with the treatment planning system (TPS). A total of 24 patients with 189 treatment fractions were enrolled in this study. Gamma analysis was performed and the threshold that encompassed 95% of QA results (T95) was reported. The plan complexity metric was calculated for each plan and compared with the dose measurements to determine whether any correlation existed.Results. All point dose measurements were within 5% deviation. The mean gamma passing rates of the group data were found to be 96.8 ± 4.0% and 99.6 ± 0.7% with criteria of 2%/2mm and 3%/3mm, respectively. T95 of 87.4% and 98.2% was reported for the overall group with the two passing criteria, respectively. No statistically significant difference was found between adaptive treatments with adapt-to-position (ATP) and adapt-to-shape (ATS), whilst the category of pelvis data showed a better passing rate than other sites. Online QA gave a mean deviation of 0.2 ± 2.2%. The plan complexity metric was positively correlated with the mean dose difference whilst the complexity of the ATS cohort had larger variations than the ATP cohort.Conclusions. A patient-specific QA system based on ArcCHECK-MR, solid phantom and ionization chamber has been well established and implemented for validation of treatment delivery of a 1.5 T MR-Linac. Our QA data obtained over one year confirms that good agreement between TPS calculation and treatment delivery was achieved.

Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning

INTRODUCTION

Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique.

MATERIALS AND METHODS

A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed.

RESULTS

Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum).

CONCLUSION

This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.

On the reduction of aperture complexity in kidney SABR

Background

Stereotactic ablative body radiotherapy (SABR) of primary kidney cancers is confounded by motion. There is a risk of interplay effect if the dose is delivered using volumetric modulated arc therapy (VMAT) and flattening filter‐free (FFF) dose rates due to target and linac motion. This study aims to provide an efficient way to generate plans with minimal aperture complexity.

Methods

In this retrospective study, 62 patients who received kidney SABR were reviewed. For each patient, two plans were created using internal target volume based motion management, on the average intensity projection of a four‐dimensional CT. In the first plan, optimization was performed using a knowledge‐based planning model based on delivered clinical plans in our institution. In the second plan, the optimization was repeated, with a maximum monitor unit (MU) objective applied in the optimization. Dose‐volume, conformity, and complexity metric (with the field edge metric and the modulation complexity score) were compared between the two plans. Results are shown in terms of median (first quartile — third quartile).

Results

Similar dosimetry was obtained with and without the utilization of an objective on the MU. However, complexity was reduced by using the objective on the MUs (modulation complexity score = 0.55 (0.50–0.61) / 0.33 (0.29–0.36), P‐value < 10⁻¹⁰, with/without the MU objective). Reduction of complexity was driven by a larger aperture area (area aperture variability = 0.68 (0.64–0.73) / 0.42 (0.37–0.45), P‐value < 10⁻¹⁰, with/without the MU objective). Using the objective on the MUs resulted in a more spherical dose distribution (sphericity 50% isodose = 0.73 (0.69–0.75) / 0.64 (0.60–0.68), P‐value < 10⁻⁸, with/without the MU objective) reducing dose to organs at risk given respiratory motion.

Conclusions

Aperture complexity is reduced in kidney SABR by using an objective on the MU delivery with VMAT and FFF dose rate.

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.