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A priori estimation of staff exposure during medical interventional procedures involving the use of ionising radiation is essential to carry out an adequate risk assessment and, therefore, to define the maximum workloads, to choose appropriate dosimeters and additional shielding. To date, research activity has been mainly focused on cardiac procedures, which involve high dose rates, and much attention is paid to the optimisation of radiation protection in this field. The purpose of this retrospective study was to evaluate the dose exposure of different healthcare professionals starting from the Dose Area Products (DAPs) recorded after various interventional (non-cardiac) and surgical procedures. A total of 374 operators, 2829 interventional procedures and 4463 surgical procedures were considered. Estimated thyroid/lens absorbed dose (median-75%) for surgeons/interventionists were as follows (ìSv/procedure). Interventional procedures: endoscopy (107–121)/(85–97) and urology (60–130)/(48–104); surgeries: vascular (68–73)/(55–60), general (28–35)/(22–28), orthopaedic (6–9)/(5–7). After grouping the data of all the procedures, the same estimations are reported for anesthesiologists (15–29)/(13–25), nurses (13–24)/(11–20) and radiographers (15–32)/(12–26). [ABSTRACT FROM AUTHOR]

Beam-matched linear accelerators (LA's) require accurate and precise dosimetry for fractionated stereotactic treatment. In this study, the beam data were validated by comparing the three-beam-matched LA's measured data and the vendor reference data. Upon its validation, the accuracy of the volumetric dose delivery for eighty patient-specific fractionated stereotactic treatment plans was evaluated. Measurements of the percentage depth dose (PDD), beam profiles, output factors (OFs), absolute output, and dynamic multi-leaf collimator (MLC) transmission factors for 6 MV and 10 MV flattening filter (FF) and flattening filter-free (FFF) photon beams were obtained from three-beam-matched LA's. The patient-specific quality assurance evaluation for all eighty plans was performed using PTW Octavius 1000 SRS™ array detectors for two-dimensional (2D) fluence measurement. The following 2D gamma passing criteria were used: 1%/1 mm, 2%/1 mm, 1%/2 mm, 2%/2 mm and 3%/2 mm. In all three LA's, gamma analysis for PDD and profile were above 97% with gamma criteria of 1%/1 mm. The differences OFs, absolute output, and dynamic MLC transmission factors were less than ± 1% of base value. For all eighty cases, the median passing rates on the three LA's were above 76%, 88%, 92%, 96%, and 98% for the above-mentioned gamma criteria of the three LA's. The beam-matched LA's showed good agreement between the measured and treatment planning system (TPS) calculated values for fractionated stereotactic VMAT plans with 6 MV and 10 MV (FF and FFF) photon beams. Patients can be shifted and treated on any beam-matched linac without the need of re-planning. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, advanced radiotherapy techniques such as intensity-modulated radiation therapy (IMRT) and simultaneous integrated boost (SIB) irradiation have played a key role in enhancing the precision of radiation delivery to tumors. However, these advancements have also increased the complexity of treatment planning by involving a trial-and-error approach, resulting in greater variability between operators and longer planning times. The automation of planning processes has shown promise in standardizing treatment plans while maintaining their quality and reducing workload. Additionally, deep learning-based fully automated planning solutions have become a significant focus of research in radiation oncology. In this study, we propose a single, end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments, requiring minimal human input and producing a machine-deliverable volumetric modulated arc therapy (VMAT) plan. A comprehensive clinical evaluation was performed, incorporating both dosimetric analysis and plan deliverability assessment. Background/Objectives: To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. Methods: In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its input a planning CT with organs-at-risk (OARs) and planning target volume (PTV) contours, the targeted linac machine, and the prescription dose. The primary components are (i) dose prediction by a single deep learning model for both localizations and (ii) a direct aperture VMAT plan optimization that seeks to mimic the predicted dose. The deep learning model was trained on 238 cases, and a held-out set of 86 cases was used for model validation. An end-to-end clinical evaluation study was performed on another 40 cases (20 prostate-only, 20 whole-pelvic). First, a quantitative evaluation was performed based on dose–volume histogram (DVH) points and plan parameter metrics. Then, the plan deliverability was assessed via portal dosimetry using the global gamma index. Additionally, the reference clinical manual plans were compared with the automated plans in terms of monitor unit (MU) numbers and modulation complexity scores (MCSv). Results: The automated plans provided adequate treatment plans (or minor deviations) with respect to the dose constraints, and the quality of the plans was similar to the manual plans for both localizations. Moreover, the automated plans showed successful deliverability and passed the portal dose verification. Despite higher median total MUs, no statistically significant correlation was observed between any of the gamma criteria tested and the number of MUs or MCSv. Conclusions: This study shows the feasibility of a deep learning-based fully automated treatment planning pipeline that generates high-quality plans that are competitive with manually made plans and are clinically approved in terms of dosimetry and machine deliverability. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to introduce modified fixed field volumetric modulated arc therapy (MF‐VMAT) which manually opened the field size by fixing the jaws and comparing it to the typical planning technique, auto field volumetric modulated arc therapy (AF‐VMAT) in cervical cancer treatment planning. Methods and materials: Previously treated twenty‐eight cervical cancer plans were retrospectively randomly selected and replanned in this study using two different planning techniques: AF‐VMAT and MF‐VMAT, resulting in a total of fifty‐six treatment plans. In this study, we compared both planning techniques in three parts: (1) Organ at Risk (OARs) and whole‐body dose, (2) Treatment plan efficiency, and (3) Treatment plan accuracy. Results: For OARs dose, bowel bag (p‐value = 0.001), rectum (p‐value = 0.002), and left femoral head (p‐value = 0.001) and whole‐body (p‐value = 0.000) received a statistically significant dose reduction when using the MF‐VMAT plan. Regarding plan efficiency, MF‐VMAT exhibited a statistically significant increase in both number of monitor units (MUs) and control points (p‐values = 0.000), while beam‐on time, maximum leaf travel, average maximum leaf travel, and maximum leaf travel per gantry rotation were statistically significant decreased (p‐values = 0.000). In terms of plan accuracy, the average gamma passing rate was higher in the MF‐VMAT plan for both absolute dose (AD) (p‐value = 0.001, 0.004) and relative dose (RD) (p‐value = 0.000, 0.000) for 3%/3 and 3%/2 mm gamma criteria, respectively. Conclusion: The MF‐VMAT planning technique significantly reduces OAR doses and decreases the spread of low doses to normal tissues in cervical cancer patients. Additionally, this planning approach demonstrates efficient plans with lower beam‐on time and reduced maximum leaf travel. Furthermore, it indicates higher plan accuracy through an increase in the average gamma passing rate compared to the AF‐VMAT plan. Consequently, MF‐VMAT offers an effective treatment planning technique for cervical cancer patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: SRS MapCHECK (SMC) is a commercially available patient‐specific quality assurance (PSQA) tool for stereotactic radiosurgery (SRS) applications. This study investigates the effects of degree of modulation, location off‐axis, and low dose threshold (LDT) selection on gamma pass rates (GPRs) between SMC and treatment planning system, Analytical Anisotropic Algorithm (AAA), or Vancouver Island Monte Carlo (VMC++ algorithm) system calculated dose distributions. Methods: Volumetric‐modulated arc therapy (VMAT) plans with modulation factors (MFs) ranging from 2.7 to 10.2 MU/cGy were delivered to SMC at isocenter and 6 cm off‐axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1 mm) with LDT of 10% to 80% using SNC Patient software. Results: Comparing on‐axis SMC dose against AAA and VMC++ with LDT of 10%, all AAA‐calculated plans met the acceptance criteria of GPR ≥ 90%, and only one VMC++ calculated plan was marginally outside the acceptance criteria with pass rate of 89.1%. Using LDT of 80% revealed decreasing GPR with increasing MF. For AAA, GPRs reduced from 100% at MF of 2.7 MU/cGy to 57% at MF of 10.2 MU/cGy, and for VMC++ calculated plans, the GPRs reduced from 89% to 60% in the same MF range. Comparison of SMC dose off‐axis against AAA and VMC++ showed more pronounced reduction of GPR with increasing MF. For LDT of 10%, AAA GPRs reduced from 100% to 83% in the MF range of 2.7 to 9.8 MU/cGy, and VMC++ GPR reduced from 100% to 91% in the same range. With 80% LDT, GPRs dropped from 100% to 42% for both algorithms. Conclusions: MF, dose calculation algorithm, and LDT selections are vital in VMAT‐based SRT PSQA. LDT of 80% enhances sensitivity of gamma analysis for detecting dose differences compared to 10% LDT. To achieve better agreement between calculated and SMC dose, it is recommended to limit the MF to 4.6 MU/cGy on‐axis and 3.6 MU/cGy off‐axis. [ABSTRACT FROM AUTHOR]

Purpose: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery. Methods: A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database. Multi‐correlation was used to analyzed the potential influences on MLC positional accuracy, including the spatial characteristics and temporal variability of VMAT fluence, and the mechanical wear parameters of MLC. We developed a model to forecast the accuracy of MLC moving position utilizing the random forest (RF) ensemble learning method. Spearman correlation was used to further investigate the associations between MLC positional deviation and dosage deviations as well as gamma passing rates. Results: The MLC positional deviation and effective impact factors show a strong multi‐correlation (R = 0.701, p‐value < 0.05). This leads to the development of a highly accurate prediction model with average variables explained of 95.03% and average MSE of 0.059 in the 5‐fold cross‐validation, and MSE of 0.074 for the test data was obtained. The absolute dose deviations caused by MLC positional deviation ranging from 12.948 to 210.235 cGy, while the relative volume deviation remained small at 0.470%–5.161%. The average MLC positional deviation correlated substantially with gamma passing rates (with correlation coefficient of −0.506 to −0.720 and p‐value < 0.05) but marginally with dosage deviations (with correlation coefficient < 0.498 and p‐value > 0.05). Conclusions: The RF predictive model provides a prior tool for VMAT quality assurance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA. [ABSTRACT FROM AUTHOR]

Background: For patient‐specific quality assurance (PSQA) for small targets, the dose resolution can change depending on the characteristics of the dose calculation algorithms. Purpose: This study aimed to evaluate the influence of the dose calculation algorithms Acuros XB (AXB), anisotropic analytical algorithm (AAA), photon Monte Carlo (pMC), and collapsed cone (CC) on a helical diode array using volumetric‐modulated arc therapy (VMAT) for small targets. Materials and methods: ArcCHECK detectors were inserted with a physical depth of 2.9 cm from the surface. To evaluate the influence of the dose calculation algorithms for small targets, rectangular fields of 2×100, 5×100, 10×100, 20×100, 50×100, and 100×100 mm2 were irradiated and measured using ArcCHECK with TrueBeam STx. A total of 20 VMAT plans for small targets, including the clinical sites of 19 brain metastases and one spine, were also evaluated. The gamma passing rates (GPRs) were evaluated for the rectangular fields and the 20 VMAT plans using AXB, AAA, pMC, and CC. Results: For rectangular fields of 2×100 and 5×100 mm2, the GPR at 3%/2 mm of AXB was < 50% because AXB resulted in a coarser dose resolution with narrow beams. For field sizes > 10×100 mm2, the GPR at 3%/2 mm was > 88.1% and comparable for all dose calculation algorithms. For the 20 VMAT plans, the GPRs at 3%/2 mm were 79.1 ± 15.7%, 93.2 ± 5.8%, 94.9 ± 4.1%, and 94.5 ± 4.1% for AXB, AAA, pMC, and CC, respectively. Conclusion: The behavior of the dose distribution on the helical diode array differed depending on the dose calculation algorithm for small targets. Measurements using ArcCHECK for VMAT with small targets can have lower GPRs owing to the coarse dose resolution of AXB around the detector area. [ABSTRACT FROM AUTHOR]

Backgroud and objectives: The implementation of patient-specific quality assurance (PSQA) has become a crucial aspect of the radiation therapy process. Machine learning models have demonstrated their potential as virtual QA tools, accurately predicting the gamma passing rate (GPR) of volumetric modulated arc therapy (VMAT)plans, thereby ensuring safe and efficient treatment for patients. However, there is limited multi-center research dedicated to predicting the GPR. In this study, a dosiomics-based machine learning approach was employed to construct a prediction model for classifying GPR in multiple radiotherapy institutions. Additionally, the model's performance was compared by evaluating the impact of two distinct feature selection methods. Methods: A retrospective data collection was conducted on 572 VMAT patients across three radiotherapy institutions. Utilizing a three-dimensional dose verification technique grounded in real-time measurements, y analysis was conducted according to the criteria of 3%/2 mm and 2%/2 mm, employing a dose threshold of 10% along with absolute dose and global normalization mode. Dosiomics features were extracted from the dose files, and distinct subsets of features were selected as inputs for the model using the random forest (RF) and RF combined with SHapley Additive exPlanations (SHAP) methods. The data underwent training using the extreme gradient boosting (XGBoost) algorithm, and the model's classification performance was assessed through F1-score and area under the curve (AUC) values. Results: The model exhibited optimal performance under the 3%/2 mm criteria, utilizing a subset of 20 features and attaining an AUC value of 0.88 and an F1score of 0.89. Similarly, under the 2%/2 mm criteria, the model demonstrated superior performance with a subset of 10 features, resulting in an AUC value of 0.91 and an F1-score of 0.89. The feature selection methods of RF and RF + SHAP have achieved good model performance by selecting as few features as possible. Conclusion: Based on the multi-center PSQA results, it is possible to utilize dosiomics features extracted from dose files to construct a machine learning predictive model. This model demonstrates excellent discriminative abilities, thus promoting the progress of gamma passing rate prognostic models in clinical application and implementation. Furthermore, it holds potential in providing patients with secure and efficient personalized QA management, while also reducing the workload of medical physicists. [ABSTRACT FROM AUTHOR]

The treatment of head and neck cancers (HNCs) encompasses a complex paradigm involving a combination of surgery, radiotherapy, and systemic treatment. Locoregional recurrence is a common cause of treatment failure, and few patients are suitable for salvage surgery. Reirradiation with conventional radiation techniques is challenging due to normal tissue tolerance limits and the risk of significant toxicities. Stereotactic body radiotherapy (SBRT) has emerged as a highly conformal modality that offers the potential for cure while limiting the dose to surrounding tissue. There is also growing research that shows that those with oligometastatic disease can benefit from curative intent local ablative therapies such as SBRT. This review will look at published evidence regarding the use of SBRT in locoregional recurrent and oligometastatic HNCs. [ABSTRACT FROM AUTHOR]

Purpose: For individual targets of single isocenter multi‐target (SIMT) Stereotactic radiosurgery (SRS), we assess dose difference between the treatment planning system (TPS) and independent Monte Carlo (MC), and demonstrate persistence into the pre‐treatment Quality Assurance (QA) measurement. Methods: Treatment plans from 31 SIMT SRS patients were recalculated in a series of scenarios designed to investigate sources of discrepancy between TPS and independent MC. Targets with > 5% discrepancy in DMean[Gy] after progressing through all scenarios were measured with SRS MapCHECK. A matched pair analysis was performed comparing SRS MapCHECK results for these targets with matched targets having similar characteristics (volume & distance from isocenter) but no such MC dose discrepancy. Results: Of 217 targets analyzed, individual target mean dose (DMean[Gy]) fell outside a 5% threshold for 28 and 24 targets before and after removing tissue heterogeneity effects, respectively, while only 5 exceeded the threshold after removing effect of patient geometry (via calculation on StereoPHAN geometry). Significant factors affecting agreement between the TPS and MC included target distance from isocenter (0.83% decrease in DMean[Gy] per 2 cm), volume (0.15% increase per cc), and degree of plan modulation (0.37% increase per 0.01 increase in modulation complexity score). SRS MapCHECK measurement had better agreement with MC than with TPS (2%/1 mm / 10% threshold gamma pass rate (GPR) = 99.4 ± 1.9% vs. 93.1 ± 13.9%, respectively). In the matched pair analysis, targets exceeding 5% for MC versus TPS also had larger discrepancies between TPS and measurement with no GPR (2%/1 mm / 10% threshold) exceeding 90% (71.5% ± 16.1%); whereas GPR was high for matched targets with no such MC versus TPS difference (96.5% ± 3.3%, p = 0.01). Conclusions: Independent MC complements pre‐treatment QA measurement for SIMT SRS by identifying problematic individual targets prior to pre‐treatment measurement, thus enabling plan modifications earlier in the planning process and guiding selection of targets for pre‐treatment QA measurement. [ABSTRACT FROM AUTHOR]

Purpose: To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy. Methods: Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non‐small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th‐percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R‐squared values were used to determine the best metric. Results: Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites. Conclusion: Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma. Materials and Methods: We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated. Results: The leaf speed (MILS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCSSPORT, leaf speed (MILS), and gantry speed (MIGS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCSSPORT, leaf speed (MILS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively. Conclusions: The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics. [ABSTRACT FROM AUTHOR]

Introduction: Complexity metrics have been suggested to characterize treatment plans based on machine parameters such as multileaf collimator (MLC) position. Several complexity metrics have been proposed and related to the Intensity-modulated radiation therapy (IMRT) quality assurance results. This study aims to evaluate aperture-based complexity metrics on MLC openings used in clinicaland establish a correlation between plan complexity and the gamma passing rate (GPR) for the IMRT plans. Material and Methods: We implemented the aperture-based complexity metric on MLC openings of the IMRT treatment plan for breast and central nervous system (CNS) cases. The modulation complexity score (MCS), the edge area metric (EAM), the converted area metric (CAM), the circumference/area (CPA), and the ratio monitor unit MU/Gy are evaluated in this study. The complexity score was calculated using Matlab. The MatriXX Evolution was used for dose verification. The dose distribution was analyzed using the OmniPro-I'mRT program and the gamma index was assessed using two criteria: 3%/3 mm and 3%/2 mm. The correlation between the calculated complexity score and the GPR is analyzed using SPSS. Results: The complexity score calculated by MCS, EAM, CAM, CPA, and MU/Gy shows breast plan is more complex than the CNS plan. The results of the correlation test of the complexity metric and GPR show that only the EAM metric shows a good correlation with GPR for both cases. Conclusion: EAM strongly correlates with the gamma pass rate. The MCS, CAM, CPA, and MU/Gy have a weak correlation with the GPR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: We sought to develop machine learning models to predict the results of patient‐specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose‐evaluation metrics—including the gamma passing rates (GPRs)—and criteria based on the radiomic features of 3D dose distribution in a phantom. Methods: A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose‐evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance‐to‐agreement in 1‐mm and 2‐mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient‐specific QA. The machine learning regression models for predicting the values of the dose‐evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross‐validation in which four‐fold cross‐validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. Results: The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose‐evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. Conclusions: The developed machine learning models showed high performance for predicting dose‐evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: To improve organ at risk (OAR) sparing, dynamic trajectory radiotherapy (DTRT) extends VMAT by dynamic table and collimator rotation during beam‐on. However, comprehensive investigations regarding the impact of the gantry‐table (GT) rotation gradient on the DTRT plan quality have not been conducted. Purpose: To investigate the impact of a user‐defined GT rotation gradient on plan quality of DTRT plans in terms of dosimetric plan quality, dosimetric robustness, deliverability, and delivery time. Methods: The dynamic trajectories of DTRT are described by GT and gantry‐collimator paths. The GT path is determined by minimizing the overlap of OARs with planning target volume (PTV). This approach is extended to consider a GT rotation gradient by means of a maximum gradient of the path (Gmax${G}_{max}$) between two adjacent control points (G=|Δtableangle/Δgantryangle|$G = | \Delta {{\mathrm{table\ angle}}/\Delta {\mathrm{gantry\ angle}}} |$) and maximum absolute change of G (ΔGmax${{\Delta}}{G}_{max}$). Four DTRT plans are created with different maximum G&∆G: Gmax${G}_{max}$&ΔGmax${{\Delta}}{G}_{max}$ = 0.5&0.125 (DTRT‐1), 1&0.125 (DTRT‐2), 3&0.125 (DTRT‐3) and 3&1‍(DTRT‐4), including 3–4 dynamic trajectories, for three clinically motivated cases in the head and neck and brain region (A, B, and C). A reference VMAT plan for each case is created. For all plans, plan quality is assessed and compared. Dosimetric plan quality is evaluated by target coverage, conformity, and OAR sparing. Dosimetric robustness is evaluated against systematic and random patient‐setup uncertainties between ±3mm$ \pm 3\ {\mathrm{mm}}$ in the lateral, longitudinal, and vertical directions, and machine uncertainties between ±4∘$ \pm 4^\circ \ $in the dynamically rotating machine components (gantry, table, collimator rotation). Delivery time is recorded. Deliverability and delivery accuracy on a TrueBeam are assessed by logfile analysis for all plans and additionally verified by film measurements for one case. All dose calculations are Monte Carlo based. Results: The extension of the DTRT planning process with user‐defined Gmax&ΔGmax${G}_{max}\& {{\Delta}}{G}_{max}$ to investigate the impact of the GT rotation gradient on plan quality is successfully demonstrated. With increasing Gmax&ΔGmax${G}_{max}\& {{\Delta}}{G}_{max}$, slight (case C, Dmean,parotidl.${D}_{mean,\ parotid\ l.}$: up to‍−1‍Gy) and substantial (case A, D0.03cm3,opticnerver.${D}_{0.03c{m}^3,\ optic\ nerve\ r.}$: up to −9.3 Gy, case‍B,Dmean,brain$\ {D}_{mean,\ brain}$: up to −4.7‍Gy) improvements in OAR sparing are observed compared to VMAT, while maintaining similar target coverage. All plans are delivered on the TrueBeam. Expected and actual machine position values recorded in the logfiles deviated by <0.2° for gantry, table and collimator rotation. The film measurements agreed by >96% (2%‍global/2 mm Gamma passing rate) with the dose calculation. With increasing Gmax&ΔGmax${G}_{max}\& {{\Delta}}{G}_{max}$, delivery time is prolonged by <2 min/trajectory (DTRT‐4) compared to VMAT and DTRT‐1. The DTRT plans for case A and B and the VMAT plan for case C plan reveal the best dosimetric robustness for the considered uncertainties. Conclusion: The impact of the GT rotation gradient on DTRT plan quality is comprehensively investigated for three cases in the head and neck and brain region. Increasing freedom in this gradient improves dosimetric plan quality at the cost of increased delivery time for the investigated cases. No clear dependency of GT rotation gradient on dosimetric robustness is observed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to investigate the impact of large target offset distances on the dose distribution and gamma passing rate (GPR) in single-isocenter multiple-target stereotactic radiosurgery (SIMT SRS) using volumetric modulated arc therapy (VMAT) with a flattening filter-free (FFF) beam from a linear accelerator. Methods: Two targets with a diameter of 1 cm were offset by "±2, ±4, and ±6 cm from the isocenter in a verification phantom for head SRS (20 Gy/fr). The VMAT plans were created using collimator angles that ensured the two targets did not share a leaf pair from the multi-leaf collimator. To evaluate the low-dose spread intermediate dose spill (R50%), GPRs were measured with a criterion of 3%/2 mm using an electronic portal imaging device and evaluated using monitor unit (MU), modulation complexity score for VMAT (MCSv), and leaf travel (LT) parameters. Results: For offsets of 2, 4, and 6 cm, the respective parameters were: R50%, 4.75 ± 0.36, 5.13 ± 0.36, and 5.11 ± 0.33; GPR, 95.01%, 93.82%, and 90.67%; MU, 5893 ± 186, 5825 ± 286, and 5810 ± 396; MCSv, 0.24, 0.16, and 0.13; and LT, 189.21 ± 36.04, 327.69 ± 67.01, and 430.39 ± 114.34 mm. There was a spread in the low-dose region from offsets of =4 cm and the GPR negatively correlated with LT (r = -0.762). There was minimal correlation between GPR and MU or MCSv. Conclusions: In SIMT SRS VMAT plans with an FFF beam from a linear accelerator, target offsets of <4 cm from the isocenter can minimize the volume of the low-dose region receiving 10 Gy or more. During treatment planning, it is important to choose gantry, couch, and collimator angles that minimize LT and thereby improve the GPR. [ABSTRACT FROM AUTHOR]

Purpose: Deep learning‐based virtual patient‐specific quality assurance (QA) is a novel technique that enables patient QA without measurement. However, this method could be improved by further evaluating the optimal data to be used as input. Therefore, a deep learning‐based model that uses multileaf collimator (MLC) information per control point and dose distribution in patient's CT as inputs was developed. Methods: Overall, 96 volumetric‐modulated arc therapy plans generated for prostate cancer treatment were used. We developed a model (Model 1) that can predict measurement‐based gamma passing rate (GPR) for a treatment plan using data stored as a map reflecting the MLC leaf position at each control point (MLPM) and data of the dose distribution in patient's CT as inputs. The evaluation of the model was based on the mean absolute error (MAE) and Pearson's correlation coefficient (r) between the measured and predicted GPR. For comparison, we also analyzed models trained with the dose distribution in patient's CT alone (Model 2) and with dose distributions recalculated on a virtual phantom CT (Model 3). Results: At the 2%/2 mm criterion, MAE[%] and r for Model 1, Model 2, and Model 3 were 2.32% ± 0.43% and 0.54 ± 0.03, 2.70% ± 0.26%, and 0.32 ± 0.08, and 2.96% ± 0.23% and 0.24 ± 0.22, respectively; at the 3%/3 mm criterion, these values were 1.25% ± 0.05% and 0.36 ± 0.18, 1.57% ± 0.35% and 0.19 ± 0.20, and 1.39% ± 0.32% and 0.17 ± 0.22, respectively. This result showed that Model 1 exhibited the lowest MAE and highest r at both criteria of 2%/2 mm and 3%3 mm. Conclusions: These findings showed that a model that combines the MLPM and dose distribution in patient's CT exhibited a better GPR prediction performance compared with the other two studied models. [ABSTRACT FROM AUTHOR]

Introduction: Stereotactic ablative radiotherapy (SABR) is susceptible to challenges for tumours affected by intrafraction organ motion. This study aims to investigate the effect of breathing characteristics and plan complexity on the interplay effect. Methods: A patient-specific interplay effect evaluation was performed using in-house software with an alpha version of the treatment planning verification software Verisoft (PTW-Freiburg, Germany) on VMAT plans. The OCTAVIUS 4D phantom was used to acquire the static dose distribution, and the simulation approach was utilised to generate the moving dose distribution. The influence of plan complexity, PTV size, number of breaths, and motion amplitudes on the interplay effect were examined. The dose distribution of two extreme phases—end-inhale and end-exhale—was considered using the gamma criteria of 2%/2 mm for the interplay effect evaluation. Results: A strong correlation was found between the motion amplitude (p < 0.001) and the NBs (p < 0.001) with the gamma-passing rate. No correlation was found between the gamma-passing rate and the PTV size or plan complexity. Conclusion: The simulation tool allowed the analysis of a large number of breathing traces, demonstrating how free-breathing patients, suspected of high interplay, could be selected for other motion management solutions. The simulated cases showed strong interplay effects for long breathing periods with extended motion amplitudes in a small group of patients. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the dosimetric accuracy of a deep learning (DL)-based deliverable volumetric arc radiation therapy (VMAT) plan generated using DL-based automated planning assistant system (AIVOT, prototype version) for patients with prostate cancer. The VMAT data (cliDose) of 68 patients with prostate cancer treated with VMAT treatment (70–74 Gy/28–37 fr) at our hospital were used (n  = 55 for training and n  = 13 for testing). First, a HD-U-net-based 3D dose prediction model implemented in AIVOT was customized using the VMAT data. Thus, a predictive VMAT plan (preDose) comprising AIVOT that predicted the 3D doses was generated. Second, deliverable VMAT plans (deliDose) were created using AIVOT, the radiation treatment planning system Eclipse (version 15.6) and its vender-supplied objective functions. Finally, we compared these two estimated DL-based VMAT treatment plans—i.e. preDose and deliDose—with cliDose. The average absolute dose difference of all DVH parameters for the target tissue between cliDose and deliDose across all patients was 1.32 ± 1.35% (range: 0.04–6.21%), while that for all the organs at risks was 2.08 ± 2.79% (range: 0.00–15.4%). The deliDose was superior to the cliDose in all DVH parameters for bladder and rectum. The blinded plan scoring of deliDose and cliDose was 4.54 ± 0.50 and 5.0 ± 0.0, respectively (All plans scored ≥4 points, P  = 0.03.) This study demonstrated that DL-based deliverable plan for prostate cancer achieved the clinically acceptable level. Thus, the AIVOT software exhibited a potential for automated planning with no intervention for patients with prostate cancer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: While many have speculated on the reasons for gamma comparison insensitivity for patient‐specific quality assurance analysis, the true reasons for insensitivity have not yet been elucidated. Failing to understand the reasons for this technique's insensitivity limits our ability to either improve the gamma metric to increase sensitivity of the comparison or the capacity to develop new comparison techniques that circumvent the limitations of the gamma comparison. Purpose: To understand the underlying cause(s) for gamma comparison insensitivity and determine if simple plan characteristics can quantitatively predict for gamma comparison sensitivity. Methods: Known MLC and MU errors of varying magnitudes were induced on simple test fields to preliminarily investigate where gamma failures first begin to appear as error magnitude is increased. Gamma value maps between error‐induced plan calculations and error‐free plan calculations were created for 20 IMRT and 20 VMAT cases, each on three different detector geometries—ArcCHECK, MapCHECK, and Delta4. Gamma value maps were qualitatively compared to dose‐gradient maps, and quantitative comparisons were performed between various plan descriptors and the computed gamma sensitivity for five different classes of induced errors were utilized to determine if any plan descriptor could predict the gamma sensitivity on a case‐by‐case basis. All comparisons were performed in a calculation‐only scenario to remove uncertainties introduced by comparisons made with real patient specific QA measurements. Results: Gamma value maps with increasing induced error magnitude illustrated that gamma comparisons fail first in high‐dose, low‐gradient regions of the field. Conversely, in areas of high gradient, gamma values typically remain low, even in the presence of large errors, regardless of detector geometry and gamma normalization setting. Thus, the complex, and often overlapping, high dose gradients in plans appear to be a limiting factor in gamma comparison sensitivity as the number of points along these gradients may often outnumber the points available for failing the comparison in lower gradient regions of the field. None of the simple plan descriptors studied were able to quantitively predict gamma comparison sensitivity, suggesting that quantitatively predicting the sensitivity of gamma comparisons on a case‐by‐case basis may require a combination of multiple factors or metrics not studied here. Conclusions: Simple plan descriptors and the number of points in high‐dose, low‐gradient regions of the field did not quantitively predict for gamma comparison sensitivity. However, it is clear from gradient and gamma value maps that gamma comparisons fail first in high‐dose, low‐gradient regions of the field in the presence of known induced errors, which we have shown to be independent of detector geometry and gamma comparison normalization setting. Gamma comparison sensitivity is thus limited by the ever‐increasing complexity of plans and is particularly important to consider as treatment volumes become smaller and the complexity of overlapping plan gradients increases. This suggests that new methods for patient‐specific QA comparisons are required to circumvent this limitation. [ABSTRACT FROM AUTHOR]

Background: we assessed the performance of the optimization algorithms by comparing volumetric modulated arc therapy generated by a progressive resolution optimized (VMATPRO) and photon optimizer (VMATPO) in terms of plan quality, MU reduction, sparing of the spinal cord (or cauda equina), and plan complexity. Methods: Fifty-seven patients who received spine stereotactic ablative radiotherapy (SABR) with tumors located in the cervical, thoracic, and lumbar spine were retrospectively selected. For each patient, VMATPRO and VMATPO with two full arcs were generated with using the PRO and PO algorithms. For dosimetric evaluation, the dose-volumetric (DV) parameters of the planning target volume (PTV), organs at risk (OARs), the corresponding planning organs at risk (PRV), and 1.5-cm ring structure surrounding the PTV (Ring1.5 cm) were calculated for all VMAT plans. The total number of monitor units (MUs) and the modulation complexity score for the VMAT (MCSv) were compared. To investigate the correlations of OAR sparing to plan complexity, Pearson’s and Spearman’s correlation tests were conducted between the two algorithms (PO – PRO, denoted as Δ) in the DV parameters for normal tissues, total MUs, and MCSv. Results: For the PTVs, Target conformity and dose homogeneity in the PTVs of VMATPRO were better than those of VMATPO with statistical significance. For the spinal cords (or cauda equine) and the corresponding PRVs, all of the DV parameters for VMATPRO were markedly lower than those for VMATPO, with statistical significance (all p < 0.0001). Among them, the difference in the maximum dose to the spinal cord between VMATPRO and VMATPO was remarkable (9.04 Gy vs. 11.08 Gy with p < 0.0001). For Ring1.5 cm, no significant difference in V115% for VMATPRO and VMATPO was observed. Conclusions: The use of VMATPRO resulted in improved coverage and uniformity of dose to the PTV, as well as OARs sparing, compared with that of VMATPO for cervical, thoracic, and lumbar spine SABR. Better dosimetric plan quality generated by the PRO algorithm was observed to result in higher total MUs and plan complexity. Therefore, careful evaluation of its deliverability should be performed with caution during the routine use of the PRO algorithm. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is a common disease for patients over the age of 50 years, especially males due to smoking habits. This study aimed to compare the modulation complexity score (MCS) for the advanced treatment planning techniques which the intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). Materials and Methods: Thirty patients who had non-small lung cancerous tumors on their left side participated in this study. The range ages were 68 to 98 years, the heights were between 151 and 182cm and they having weights from 46 to 79 kg. For Each patient will create two plans dial using two different techniques, which will be Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) in the Monaco 5.1 version, and then those plans will be forwarded to the Infinity Linear Accelerator (LINAC). For the purposes of evaluation, the dose volume histogram is utilized to perform the calculation necessary to determine the dose for tumors and Organs at Risk (OAR). After that, the modulation complexity score, also known as the MCS, was determined using the multiyear collimators of the plans. Results: Showed that the mean age of males was (82.6 ± 4.93) years, while the mean age of females was (72.12 ± 3.13) years. The proportion of males was significantly higher than that of females. The female patient had a higher body mass index than the male patient. The mean height of men in this study was (172.85 ± 2.02) cm, while the mean height of women was (156.32± 1.21) cm. The coverage planning target volume (PTV) for the left lung tumor shows that the VMAT was significantly higher than the IMRT at 95%, 98%, 5%, and 2%. When comparing IMRT and VMAT for the PTV 105%, there was no discernible difference between the two. According to the statistics, the conformity and homogeneity of the dose delivered by the VMAT was noticeably higher than those delivered by the IMRT. According to the findings of the study, the MCS score for the VMAT is noticeably higher than that of the IMRT. In the case of the IMRT technique, the relationship between the MCS and the total number of monitor units was found to be positive and direct, whereas in the case of the VMAT technique, the relationship was found to be negative and inverse. Conclusions: The volumetric modulated arc therapy (VMAT) shows a better coverage and less complexity technique and could protect the heart, spinal cord, and right lung than the IMRT. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

To predict the gamma passing rate (GPR) of the three-dimensional (3D) detector array-based volumetric modulated arc therapy (VMAT) quality assurance (QA) for prostate cancer using a convolutional neural network (CNN) with the 3D dose distribution. One hundred thirty-five VMAT plans for prostate cancer were selected: 110 plans were used for training and validation, and 25 plans were used for testing. Verification plans were measured using a helical 3D diode array (ArcCHECK). The dose distribution on the detector element plane of these verification plans was used as input data for the CNN model. The measured GPR (mGPR) values were used as the training data. The CNN model comprises eighteen layers and predicted GPR (pGPR) values. The mGPR and pGPR values were compared, and a cumulative frequency histogram of the prediction error was created to clarify the prediction error tendency. The correlation coefficients of pGPR and mGPR were 0.67, 0.69, 0.66, and 0.73 for 3%/3-mm, 3%/2-mm, 2%/3-mm, and 2%/2-mm gamma criteria, respectively. The respective mean±standard deviations of pGPR-mGPR were -0.87±2.18%, -0.65±2.93%, -0.44±2.53%, and -0.71±3.33%. The probabilities of false positive error cases (pGPR < mGPR) were 72%, 60%, 68%, and 56% for each gamma criterion. We developed a deep learning-based prediction model of the 3D detector array-based VMAT QA for prostate cancer, and evaluated the accuracy and tendency of prediction GPR. This model can provide a proactive estimation for the results of the patient-specific QA before the verification measurement. [ABSTRACT FROM AUTHOR]

Introduction: The Pinnacle3 Auto-Planning (AP) package is an automated inverse planning tool employing a multi-sequence optimisation algorithm. The nature of the optimisation aims to improve the overall quality of radiotherapy plans but at the same time may produce higher modulation, increasing plan complexity and challenging linear accelerator delivery capability. Methods and materials: Thirty patients previously treated with intensity-modulated radiotherapy (IMRT) to the prostate with or without pelvic lymph node irradiation were replanned with locally developed AP techniques for step-and-shoot IMRT (AP-IMRT) and volumetric-modulated arc therapy (AP-VMAT). Each case was also planned with VMAT using conventional inverse planning. The patient cohort was separated into two groups, those with a single primary target volume (PTV) and those with dual PTVs of differing prescription dose levels. Plan complexity was assessed using the modulation complexity score. Results: Plans produced with AP provided equivalent or better dose coverage to target volumes whilst effectively reducing organ at risk (OAR) doses. For IMRT plans, the use of AP resulted in a mean reduction in bladder V50Gy by 4·2 and 4·7 % (p ≤ 0·01) and V40Gy by 4·8 and 11·3 % (p < 0·01) in the single and dual dose level cohorts, respectively. For the rectum, V70Gy, V60Gy and V40Gy were all reduced in the dual dose level AP-VMAT plans by an average of 2·0, 2·7 and 7·3 % (p < 0·01), respectively. A small increase in plan complexity was observed only in dual dose level AP plans. Findings: The automated nature of AP led to high quality treatment plans with improvement in OAR sparing and minimised the variation in achievable dose planning metrics when compared to the conventional inverse planning approach. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate and investigate the feasibility of flattening filter‐free (FFF) beam for the whole‐brain radiotherapy (WBRT) with hippocampus sparing. Methods: Eighteen patients with volumetric‐modulated arc therapy (VMAT) plans in FFF and conventional beam modes were included in this study. The prescribed dose was 30 Gy in 10 fractions. The conformity index (CI), heterogeneity index reported by TPS (HI‐M), and homogeneity index (HI) for planning target volume (PTV) were evaluated. Subsequently, the following parameters for PTV were calculated and compared: D2%, D98%; the mean dose, maximum dose, and minimal dose for OARs. Plan modulation index, total MUs, and the gamma index were used to evaluate the plan quality. Results: HI‐M results were similar for the two techniques (1.239 vs. 1.247, respectively, p = 0.048); FFF beam plans yielded lower D2% compared to FF beam plans (3,416.3 cGy vs. 3,437.2 cGy, p = 0.22), mean dose (3,177.5 cGy vs. 3,195.2 cGy, p = 0.009), and CI (0.884 vs. 0.876, p = 0.001) for PTV. Significant differences were observed between the two beam modes (FF model vs. FFF model) for the maximum dose (1,612.9 cGy vs. 1,470.2 cGy, respectively, p < 0.001), minimum dose (987.6 cGy vs. 898.8 cGy, respectively, p < 0.001), and the mean dose (1144.4 cGy vs. 1047.3 cGy, respectively, p < 0.001) to the hippocampus, and the maximum dose to the eyes (2,792.6 cGy vs. 2,751.3 cGy, respectively, p < 0.001). The average total MUs for FFF‐VMAT plans was significantly greater than FF‐VMAT plans. However, differences for the plan modulation index and the gamma index were negligible. Conclusion: In comparison with FF beam, the FFF beam mode offers a clear benefit with respect to WBRT with hippocampal sparing. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the effects of different multi-leaf collimator (MLC) speed constraints in volumetric modulated radiotherapy (VMAT) on the robustness of treatment plans for central lung cancer patients. Method and Materials: Twenty patients with central lung tumor who underwent stereotactic body radiotherapy (SBRT) with the VMAT technique at our hospital were included in this retrospective study. The reference plans were created with 3 different MLC speed constraints (Plan A: 0.1 cm/deg., Plan B: 0.3 cm/deg., and Plan C: 0.5 cm/deg.) with a 50-Gy/8Fr, planning target volume (PTV) D95% prescription. In each of these plans, setup errors from 1 to 5 mm were intentionally added in the direction of the central organ at 1-mm intervals (300 plans [20 cases × 3 MLC speeds × 5 error plans] were created in total). Each plan was then calculated by the same beam conditions as each reference plan. The actual average MLC speed and dose difference between the reference plan and the error-added plan were then calculated and compared among the 3 MLC speeds. Results: In the reference plans, the actual average MLC speeds were 0.25 ± 0.04, 0.34 ± 0.07, and 0.39 ± 0.12 cm/deg. for Plan A, Plan B, and Plan C, respectively (P <.05). For PTV and OARs, many dose indices tended to improve as the MLC speed increased, while no significant differences were observed among the 3 MLC speed constraints. However, in assessments of robustness, no significant differences in dose difference were observed among the 3 MLC speed constraints for most of the indices. Conclusions: When necessary, increasing the MLC speed constraint with a priority on improving the quality of the dose distribution is an acceptable approach for central lung cancer patients. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to investigate the effect of plan complexity on the dosimetry, delivery accuracy, and interplay effect in lung stereotactic body radiation therapy (SBRT) using volumetric modulated arc therapy (VMAT) with 6 MV flattening-filter-free (FFF) beam. Methods: Twenty patients with early stage non-small cell lung cancer were included. For each patient, high-complexity (HC) and low-complexity (LC) three-partial-arc VMAT plans were optimized by adjusting the normal tissue objectives and the maximum monitoring units (MUs) for a Varian TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA, USA) using 6 MV FFF beam. The effect of plan complexity was comprehensively evaluated in three aspects: (1) The dosimetric parameters, including CI, D2cm, R50, and dose–volume parameters of organs at risk were compared. (2) The delivery accuracy was assessed by pretreatment quality assurance for two groups of plans. (3) The motion-induced dose deviation was evaluated based on point dose measurements near the tumor center by using a programmable phantom. The standard deviation (SD) and maximum dose difference of five measurements were used to quantify the interplay effect. Results: The dosimetry of HC and LC plans were similar except the CI (1.003 ± 0.032 and 1.026 ± 0.043, p = 0.030) and Dmax to the spinal cord (10.6 ± 3.2 and 9.9 ± 3.0, p = 0.012). The gamma passing rates were significantly higher in LC plans for all arcs (p < 0.001). The SDs of HC and LC plans ranged from 0.5–16.6% and 0.03–2.9%, respectively, under the conditions of one-field, two-field, and three-field delivery for each plan with 0.5, 1, 2, and 3 cm motion amplitudes. The maximum dose differences of HC and LC plans were 34.5% and 9.1%, respectively. Conclusion: For lung VMAT SBRT, LC plans have a higher delivery accuracy and a lower motion-induced dose deviation with similar dosimetry compared with HC plans. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a predictive model for patient‐specific VMAT QA results using multileaf collimator (MLC) effect and texture analysis. The MLC speed, acceleration and texture analysis features were extracted from 106 VMAT plans as predictors. Gamma passing rate (GPR) was collected as a response class with gamma criteria of 2%/2 mm and 3%/2 mm. The model was trained using two machine learning methods: AdaBoost classification and bagged regression trees model. GPR was classified into the "PASS" and "FAIL" for the classification model using the institutional warning level. The accuracy of the model was assessed using sensitivity and specificity. In addition, the accuracy of the regression model was determined using the difference between predicted and measured GPR. For the AdaBoost classification model, the sensitivity/specificity was 94.12%/100% and 63.63%/53.13% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. For the bagged regression trees model, the sensitivity/specificity was 94.12%/91.89% and 61.18%/68.75% at gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The root mean square error (RMSE) of difference between predicted and measured GPR was found at 2.44 and 1.22 for gamma criteria of 2%/2 mm and 3%/2 mm, respectively. The promising result was found at tighter gamma criteria 2%/2 mm with 94.12% sensitivity (both bagged regression trees and AdaBoost classification model) and 100% specificity (AdaBoost classification model). [ABSTRACT FROM AUTHOR]

Aim: Studying the use of Aperture Shape Controller (ASC) and Convergence Mode (CM) in Eclipse (Varian Medical System) in terms of plan quality and complexity of volumetric modulated arc therapy (VMAT). Materials and methods: Forty VMAT plans were re-optimised for the prostate, prostate + lymph nodes, breast and head & neck patients retrospectively, changing the ASC settings (off, moderate, very high) and CM settings (off, on and extended). Results: Using 'on' or 'extended' CM increased plan quality in terms of planning target volume homogeneity and low-dose spread to the organs at risk (OAR). 'Extended' CM increased the optimisation time 4·3-fold compared to 'on', and deteriorated the plan quality in several simple planning cases. 'Moderate' ASC decreased plan complexity with minor effect on plan quality compared to 'off', but 'very high' ASC had larger adverse dosimetric effects. However, the ASC decreased the plan complexity only if the CM was turned 'on'. Findings: Using 'on' CM increases the plan quality but using 'extended' CM is not recommended. The 'moderate' ASC decreased complexity without significant adverse effects on plan quality, and even 'very high' ASC may be used when plan simplicity is prioritised. However, if CM is not used, the ASC should also be turned off. [ABSTRACT FROM AUTHOR]

Aim: We have investigated the influence in volumetric-modulated arc therapy (VMAT) plans by a sequence of increment of gantry angle (IGA) in definitive radiotherapy treatment for cervical cancer. The plans are quantitatively analysed in terms of conformity index (CI), heterogeneity index (HI), dose–gradient index (DGI), target coverage (TC) by prescription dose, monitor unit (MU) usage, control points (CPs) and dose to organs. Materials and Methods: In this retrospective study, we selected 27 patients with cervical cancer having aged between 54 and 69. All the patients enrolled in this study were at T3N1M0 stage of cervical cancer. The prescription dose to planning target volume (PTV) was 50 Gy and was administered in 2 Gy/fraction through VMAT technique. VMAT plans were optimised by varying the parameter 'IGA' as 10, 20, 30 and 40°. Results: Homogenous dose distribution within PTV and TC by prescription dose was significantly enhanced (p < 0·05) with larger IGA. The difference between volume receiving 15 Gy (V15Gy) in bowel was up to 10% with larger IGA (30 and 40°) and V25Gy in femoral head was up to 3% with smaller IGA (10 and 20°). CPs were enhanced and MU usage was reduced with larger IGA (30 and 40°). IGA 40° had reduced the MU usage than IGA 30° but the CI and DGI were compromised due to large MLC field segments. Conclusion: This study recommends that the larger IGA could yield better results when the number of sectors is even, for a cervical cancer patient. However, more data from more patients need to be obtained and analysed to make this an evidence-based hypothesis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]