Effect of different optimization parameters in single isocenter multiple brain metastases radiosurgery.

PURPOSE: Automated treatment planning for multiple brain metastases differs from traditional planning approaches. It is therefore helpful to understand which parameters for optimization are available and how they affect the plan quality. This study aims to provide a reference for designing multi-metastases treatment plans and to define quality endpoints for benchmarking the technique from a scientific perspective. METHODS: In all, 20 patients with a total of 183 lesions were retrospectively planned according to four optimization scenarios. Plan quality was evaluated using common plan quality parameters such as conformity index, gradient index and dose to normal tissue. Therefore, different scenarios with combinations of optimization parameters were evaluated, while taking into account dependence on the number of treated lesions as well as influence of different beams. RESULTS: Different scenarios resulted in minor differences in plan quality. With increasing number of lesions, the number of monitor units increased, so did the dose to healthy tissue and the number of interlesional dose bridging in adjacent metastases. Highly modulated cases resulted in 4-10% higher V10% compared to less complex cases, while monitor units did not increase. Changing the energy to a flattening filter free (FFF) beam resulted in lower local V12Gy (whole brain-PTV) and even though the number of monitor units increased by 13-15%, on average 46% shorter treatment times were achieved. CONCLUSION: Although no clinically relevant differences in parameters where found, we identified some variation in the dose distributions of the different scenarios. Less complex scenarios generated visually more dose overlap; therefore, a more complex scenario may be preferred although differences in the quality metrics appear minor.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors.

PURPOSE: This study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors. METHODS: Multiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation. RESULTS: Even for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %-20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities. CONCLUSIONS: A higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

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.

Plan complexity metrics for head and neck VMAT competition plans.

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

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.

Dosimetric impact of MLC positional errors on dose distribution in IMRT.

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

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to delivery errors.

BACKGROUND: The use of modulated techniques for intra-cranial stereotactic radiosurgery (SRS) results in highly modulated fields with small apertures, which may be susceptible to uncertainties in the delivery device. PURPOSE: This study aimed to quantify the impact of simulated delivery errors on treatment plan dosimetry and how this is affected by treatment planning system (TPS), plan geometry, delivery technique, and plan complexity. A beam modelling error was also included as context to the dose uncertainties due to treatment delivery errors. METHODS: Delivery errors were assessed for multiple-target brain SRS plans obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets, each with a prescription of 20 Gy. Of the final dataset of 54 plans, 51 were created using the volumetric modulated arc therapy (VMAT) technique and three used intensity modulated arc therapy (IMRT). Thirty-five plans were from the Varian Eclipse TPS, 17 from Elekta Monaco TPS, and one plan each from RayStation and Philips Pinnacle TPS. The errors introduced included: monitor unit calibration errors, multi-leaf collimator (MLC) bank offset, single MLC leaf offset, couch rotations, and collimator rotations. Dosimetric leaf gap (DLG) error was also included as a beam modelling error. Dose to targets was assessed via dose covering 98% of planning target volume (PTV) (D98%), dose covering 2% of PTV (D2%), and dose covering 99% of gross tumor volume (GTV) (D99%). Dose to organs at risk (OARs) was assessed using the volume of normal brain receiving 12 Gy (V12Gy), mean dose to normal brain, and maximum dose covering 0.03cc brainstem (D0.03cc). Plan complexity was also assessed via edge metric, modulation complexity score (MCS), mean MLC gap, mean MLC speed, and plan modulation (PM). RESULTS: PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0 mm and large rotational errors ≥1.0° for either the couch or collimator. However, in some cases, errors close to or within generally accepted machine tolerances resulted in clinically relevant impacts. The greatest impact upon normal brain V12Gy, mean dose to normal brain, and D0.03cc brainstem was found for DLG error in alignment with other recent studies. All delivery errors had on average a minimal impact across these parameters. Comparing plans from the Monaco TPS and the Eclipse TPS, showed a lesser increase to V12Gy, mean dose to normal brain, and D0.03cc brainstem for Monaco plans (p < 0.01) when DLG error was simulated. Monaco plans also correlated to lower plan complexity. Using Spearman's correlation coefficient (r) a strong negative correlation (r ≤ -0.8) was found between the mean MLC gap and dose to OARs for DLG errors. CONCLUSIONS: Reducing MLC complexity and using larger mean MLC gaps is recommended to improve plan robustness and reduce sensitivity to delivery and modelling errors. For cases in which the calculated dose distribution or dose indices are close to the clinically acceptable limits, this is especially important.

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).

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.

Integrating plan complexity and dosiomics features with deep learning in patient-specific quality assurance for volumetric modulated arc therapy.

PURPOSE: To investigate the feasibility and performance of deep learning (DL) models combined with plan complexity (PC) and dosiomics features in the patient-specific quality assurance (PSQA) for patients underwent volumetric modulated arc therapy (VMAT). METHODS: Total of 201 VMAT plans with measured PSQA results were retrospectively enrolled and divided into training and testing sets randomly at 7:3. PC metrics were calculated using house-built algorithm based on Matlab. Dosiomics features were extracted and selected using Random Forest (RF) from planning target volume (PTV) and overlap regions with 3D dose distributions. The top 50 dosiomics and 5 PC features were selected based on feature importance screening. A DL DenseNet was adapted and trained for the PSQA prediction. RESULTS: The measured average gamma passing rate (GPR) of these VMAT plans was 97.94% ± 1.87%, 94.33% ± 3.22%, and 87.27% ± 4.81% at the criteria of 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. Models with PC features alone demonstrated the lowest area under curve (AUC). The AUC and sensitivity of PC and dosiomics (D) combined model at 2%/2 mm were 0.915 and 0.833, respectively. The AUCs of DL models were improved from 0.943, 0.849, 0.841 to 0.948, 0.890, 0.942 in the combined models (PC + D + DL) at 3%/3 mm, 3%/2 mm and 2%/2 mm, respectively. A best AUC of 0.942 with a sensitivity, specificity and accuracy of 100%, 81.8%, and 83.6% was achieved with combined model (PC + D + DL) at 2%/2 mm. CONCLUSIONS: Integrating DL with dosiomics and PC metrics is promising in the prediction of GPRs in PSQA for patients underwent VMAT.

Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy.

PURPOSE: To assess the impact of the planner's experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute. METHODS: Eighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity. RESULTS: Plan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 ± 0.002 to 0.025 ± 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner's experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found. CONCLUSION: Both the planner's experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments.

A novel dose fall-off index and preliminary application in brain and lung stereotactic radiotherapy.

BACKGROUND: Stereotactic radiotherapy (SRT) has been widely used for the treatment of brain metastases and early stage non-small-cell lung cancer (NSCLC). Excellent SRT plans are characterized by steep dose fall-off, making it critical to accurately and comprehensively predict and evaluate dose fall-off. PURPOSE: A novel dose fall-off index was proposed to ensure high-quality SRT planning. METHODS: The novel gradient index (NGI) had two different modes: NGIx V for three-dimensions and NGIx r for one-dimension. NGIx V and NGIx r were defined as the ratios of the decreased percentage dose (x%) to the corresponding isodose volume and equivalent sphere radii, respectively. A total of 243 SRT plans at our institution between April 2020 and March 2022 were enrolled, including 126 brain and 117 lung SRT plans. Measurement-based verifications were performed using SRS MapCHECK. Ten plan complexity indexes were calculated. Dosimetric parameters related to radiation injuries were also extracted, including the normal brain volume exposed to 12 Gy (V12 ) and 18 Gy (V18 ) during single-fraction SRT (SF-SRT) and multi-fraction SRT (MF-SRT), respectively, and the normal lung volume exposed to 12 Gy (V12 ). The performance of NGI and other common dose fall-off indexes, gradient index (GI), R50% and D2cm were evaluated using Spearman correlation analysis to explore their correlations with the PTV size, gamma passing rate (GPR), plan complexity indexes, and dosimetric parameters. RESULTS: There were statistically significant correlations between NGI and PTV size (r = -0.98, P < 0.01 for NGI50 V and r = -0.93, P < 0.01 for NGI50 r), which were the strongest correlations compared with GI (r = 0.11, P = 0.13), R50% (r = -0.08, P = 0.19) and D2cm (r = 0.84, P < 0.01). The fitted formulas of NGI50 V = 23.86V-1.00 and NGI50 r = 113.5r-1.05 were established. The GPRs of enrolled SRT plans were 98.6 ± 1.7%, 94.2 ± 4.7% and 97.1 ± 3.1% using the criteria of 3%/2 mm, 3%/1 mm, and 2%/2 mm, respectively. NGI50 V achieved the strongest correlations with various plan complexity indexes (|r| ranged from 0.67 to 0.91, P < 0.01). NGI50 V also showed the highest r values with V12 (r = -0.93, P < 0.01) and V18 (r = -0.96, P < 0.01) of the normal brain during SF-SRT and MF-SRT, respectively, and V12 (r = -0.86, P < 0.01) of the normal lung during lung SRT. CONCLUSIONS: Compared with GI, R50% and D2cm , the proposed dose fall-off index, NGI, had the strongest correlations with the PTV size, plan complexity and V12 /V18 of the normal tissues. These correlations established on NGI are more helpful and reliable for SRT planning, quality control, and reducing the risk of radiation injuries.

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 EclipseTM).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).

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.

Comprehensive comparison between Halcyon 2.0 and Truebeam VMAT plans for different treatment sites: dosimetric quality and plan complexity / 中华放射肿瘤学杂志

Objective:To analyze the differences in dosimetric quality and plan complexity of volumetric modulated arc therapy (VMAT) plans based on Halcyon 2.0 and Truebeam for different treatment sites of the patients.Methods:Halcyon 2.0 VMAT plans in head & neck, chest, abdomen, and pelvis treatment sites of 49 cases were retrospectively selected and the VMAT plans were re-designed based on Truebeam with the same optimization parameters. The differences in dosimetric metrics and plan complexity between the two types of plans were compared and analyzed. P<0.05 was considered as statistically significant. Results:In terms of PTV, Halcyon 2.0 plans showed better homogeneity index (HI), conformal index (CI) in the head & neck and chest. Besides, Halcyon 2.0 plans yielded better D 98% and CI in the abdomen and better D 2% in the pelvis. For organs at risk (OAR), the D 20% and D mean of bilateral lungs, and D meanof heart for Halcyon 2.0 plans in the chest were lower than those for Truebeam plans (all P<0.05). For the complexity metrics, the median average aperture area variability (AAV) of Halcyon 2.0 plans in the head & neck, abdomen and pelvis were 0.414, 0.425 and 0.432, which were better than 0.385, 0.368 and 0.361 of Truebeam plans in the corresponding sites, respectively. In the abdomen and pelvis, Halcyon 2.0 plans showed better median modulation complexity score (MCS) than Truebeam plans (0.320 vs. 0.268, 0.303 vs. 0.282; both P<0.05). The median small aperture score (SAS) for all plans of Halcyon 2.0 were better than that of Truebeam plans (all P<0.05), and the median plan average beam area (PA) of all plans of Halcyon 2.0 were larger than that of Truebeam plans (all P<0.05). Conclusions:Compared with conventional fractionated VMAT plans based on Halcyon 2.0 and Truebeam, Halcyon 2.0 plans have similar or even better dosimetric quality. However, Halcyon 2.0 plans have lower plan complexity, which makes it an advantage in clinical application.

Impact of Knowledge-based Plan Model Improvement on Plan Complexity and Deliverability in VMAT For Prostate Cancer: A Multi-institutional Study.

BACKGROUND/AIM: This study evaluated the impact of knowledge-based plan (KBP) model improvement on plan complexity and delivery accuracy in volumetric modulated arc therapy (VMAT) for prostate cancer at multiple institutions. MATERIALS AND METHODS: Five institutions created the first KBP model before April 2017 and subsequently devised a new model (second model) based on feedback from the first KBP and the efforts of planners after April 2019. The dose-volume histogram (DVH) parameters were validated for two prostate cancer cases between the first and second KBPs. Plan complexity metrics, of the modulation complexity score for VMAT (MCSv), closed leaf score (CLS), small aperture score (SAS), and leaf travel (LT), were compared. The delivery accuracy metrics of γ pass rate and point dose discrepancy (plan vs. measurement) at isocenter were also compared. RESULTS: There were no significant differences in DVH parameters between the KBPs. Conversely, V50% of the rectum and bladder was reduced in 6/10 and 8/10 patients, respectively, and these variations were also converged from the first KBP to the second KBP. The mean±1SDs of MCSv, CLS, SAS20mm, and LT (first KBP vs. second KBP) were 0.27±0.033 vs. 0.26±0.044, 0.062±0.032 vs. 0.14±0.091, 0.59±0.048 vs. 0.70±0.14, and 411.91±32.08 mm vs. 548.33±127.50 mm, respectively. The delivery accuracy did not differ, whereas MCSv was moderately correlated with the point dose discrepancy. CONCLUSION: Multi-leaf collimator motion could be more complex with KBP model improvement, which had the potential to deteriorate the delivery accuracy.

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.

Reduction of systematic dosimetric uncertainties in volumetric modulated arc therapy triggered by patient-specific quality assurance.

BACKGROUND AND PURPOSE: Dosimetric patient-Specific Quality Assurance (PSQA) data contain in addition to cases with alerts, many cases without alerts. The aim of this study was to present a procedure to investigate long-term trend analysis of the complete set of PSQA data for the presence of site-specific deviations to reduce underlying systematic dose uncertainties. MATERIALS AND METHODS: The procedure started by analysing a large set of prostate Volumetric Modulated Arc Therapy (VMAT) PSQA data obtained by comparing 3D electronic portal image device (EPID)_based in vivo dosimetry measurements with dose values predicted by the Treatment Planning System (TPS). If systematic deviations were present, several actions were required. These included confirmation of these deviations with an independent dose verification system for which a 2D detector array in a phantom was used, and analysing calculated with measured PSQA data, or delivery machine characteristics. Further analysis revealed that the under-dosage correlated with plan complexity and coincided with changes in clinically applied planning techniques. RESULTS: Prostate VMAT PSQA data showed an under-dosage gradual increasing to about 2% in 3 years, which was confirmed by the measurements with the 2D detector array in a phantom. The implementation of new beam fits in the TPS led to a reduction of the observed deviations. CONCLUSION: Long-term analysis of site-specific PSQA data is a useful method to monitor incremental changes in a radiotherapy department due to various changes in the treatment planning and delivery of prostate VMAT, and may lead to a reduction of systematic dose uncertainties in complex treatments.

Development of a plan complexity mitigation algorithm based on gamma passing rate predictions for volumetric-modulated arc therapy.

BACKGROUND AND PURPOSE: Volumetric-modulated arc therapy (VMAT) is a complex rotational therapy technique in which highly conformal dose distribution can be realized by varying the speed of gantry rotation, multileaf collimator (MLC) shape, and dose rate. However, the complexity of the technique creates a discrepancy between the calculated and measured doses. Thus, to mitigate the plan complexity in VMAT, this study aimed to develop an algorithm and evaluate its usefulness by conducting a feasibility study. MATERIALS AND METHODS: A total of 50 patients who underwent VMAT between September 2015 and December 2020 were arbitrarily selected for this study. Specifically, patients with less than 85% gamma passing rate (GPR) at 5%/1 mm or 3%/2 mm criterion were selected randomly. Using the GPR prediction model, problematic MLC positions that contribute to a decrease in GPR were identified. Those problematic MLC positions were optimized using a limited nonlinear algorithm under mechanical limitations. Additionally, the dose prescription for the target was re-normalized. The VMAT modulated complexity score (MCSv ), averaged aperture area (AA), and monitor unit per gray (MU/Gy) were evaluated as plan complexity parameters. Calculated doses in patient geometry were evaluated for the target and its surrounding region. In addition, an ArcCHECK cylindrical diode array was used to measure the dose, and GPRs at 5%/1 mm and 3%/2 mm criteria were evaluated to analyze the difference between the mitigated and original plans. The difference was calculated using the mean ± standard deviation. RESULTS: The differences between the MCSv , AA, and MU/cGy values for the mitigated and original plans were 0.8 ± 1.7 (×10-2 ), 42.7 ± 57.9, and -5.6 ± 8.5, respectively. Regarding the calculated dose, the dose volume parameters were consistent within 1% for the target and the surrounding region. The differences between the mitigated and original plans were 1.8 ± 2.9% and 1.3 ± 1.8% for GPRs at 5%/1 mm and 3%/2 mm, respectively. CONCLUSIONS: This feasibility study resulted in the development of an algorithm with the potential to mitigate plan complexity and improve the GPR for VMAT under minor leaf position modifications.

Plan Complexity and Delivery Accuracy of Knowledge-based Volumetric Modulated Arc Therapy Plans with Single Optimization for Oropharyngeal Cancer.

BACKGROUND/AIM: We investigated the plan complexity of volumetric modulated arc therapy (VMAT) with knowledge-based plan (KBP) for oropharyngeal cancer (OPC) with a single optimization and whether it could be used clinically. MATERIALS AND METHODS: KBP model was configured using 55 consecutive OPC and nasopharyngeal cancer plans. Plan complexity as a characteristic of multileaf collimator (MLC) motion and γ pass rate (2%/2 mm criterion) were compared between clinical manual plan (CMP) and KBP for other 10 plans. RESULTS: Plan complexity metrics that had significant differences (p<0.05) (CMP vs. KBP), were mean lateral displacement of MLC from central axis (15.82 mm vs. 18.90 mm), proportions of MLC aperture sizes of ≤5 mm (0.14 vs. 0.11), ≤10 mm (0.24 vs. 0.19), and ≤20 mm (0.41 vs. 0.34), and monitor units (578.68 vs. 505.04). The γ pass rate was 91.3% vs. 93.3%. CONCLUSION: Single optimized KBP for OPC had simple plan complexity features and comparable delivery accuracy to CMP, and could be clinically applied.