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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]

Purpose: The aim of our study was to retrospectively evaluate the feasibility and clinical benefit of cyberknife stereotactic radiosurgery (CSRS) in patients treated at Florence University for recurrent, pre-irradiated brain lesions., Materials and Methods: Thirteen patients were retreated with cyberknife. Mean age was 47.1 years (range 33-77 years). Karnofsky performance status ranged from 60 to 100 (median 80). Eleven (84.6%) out of 13 patients had metastatic lesions: four (36.4%) had primary lung, three (27.2%) had primary breast cancer and four (36.4%) other types of solid malignancies. Two (15.4%) out of 13 patients had recurrent of glioblastoma., Results: In terms of compliance with CSRS, the majority of patients did not develop any acute side effects. However, two (15.4%) out of 13 patients developed acute grade 2 toxicity requiring an increase of steroid medication. At the time of the last follow-up, response rates were as follows: complete response in one case (16.6%), partial response in three (50%) and stable disease in two (33.4%)., Conclusions: Re-irradiation with CSRS is a feasible and effective option for pre-irradiated, recurrent brain lesions to obtain clinical benefit without excessive acute toxicity.

Aims and Background: We report the toxicity and preliminary clinical outcome in patients affected by locally recurrent head and neck cancer treated with stereotactic re-irradiation., Methods: Between February 2012 and August 2013, 17 patients were treated with CyberKnife as stereotactic re-irradiation for locally recurrent head and neck cancer. All patients had previously received a full dose radiation treatment with radical intent, with a median total dose of 66 Gy (range, 50-70) delivered with standard fractionation. The median interval between the primary radiotherapy and re-irradiation was 24 months (range, 10-168)., Results: All patients completed the prescribed treatment, which was delivered in 5 fractions. The median tumor dose administered was 30 Gy (range, 25-35) prescribed to the 80% isodose line. Treatment sites were as follows: neck lymph nodes in 5 patients, paranasal sinuses in 5, oropharynx in 2, nasopharynx, and larynx, oral cavity, nasal fossa and parotid gland each in 1 patient. The median target volume treated was 58.7 cm3 (range, 8.5-211.3). Sixteen patients (94%) were evaluated for response. At a median follow-up of 7.5 months (range, 2-17), 4 patients achieved complete response (25%), 5 had partial response (31%) and 7 showed stable disease (44%). No patient showed in-field progression after re-irradiation. Grade 3 acute toxicity was noted in one patient only; no late side effect was observed during the follow-up., Conclusions: Stereotactic re-irradiation with CyberKnife is an appealing non-surgical salvage treatment for selected patients with local-regionally recurrent head and neck cancer.

Purpose: To evaluate the effect of plan parameters on volumetric modulated arc therapy (VMAT) dosimetric accuracy, together with the possibility of scoring plan complexity., Methods: 142 clinical VMAT plans initially optimized using a 4° control point (CP) separation were evaluated. All plans were delivered by a 6 MV Linac to a biplanar diode array for patient-specific quality assurance (QA). Local Γ index analysis (3%, 3 mm and 2%, 2 mm) enabled the comparison between delivered and calculated dose. The following parameters were considered for each plan: average leaf travel (LT), modulation complexity score applied to VMAT (MCSv), MU value, and a multiplicative combination of LT and MCSv (LTMCS). Pearson's correlation analysis was performed between Γ passing rates and each parameter. The effects of CP angular separation on VMAT dosimetric accuracy were also analyzed by focusing on plans with high LT values. Forty out of 142 plans with LT above 350 mm were further optimized using a finer angle spacing (3° or 2°) and Γ analysis was performed. The average Γ passing rates obtained at 4° and at 3°∕2° sampling were compared. A further correlation analysis between all parameters and the Γ pass-rates was performed on 142 plans, but including the newly optimized 40 plans (CP every 3° or 2°) in place of the old ones (CP every 4°)., Results: A moderate significant (p < 0.05) correlation between each examined parameter and Γ passing rates was observed for the original 142 plans at 4° CP discretization. A negative correlation was found for LT with Pearson's r absolute values above 0.6, suggesting that a lower dosimetric accuracy may be expected for higher LT values when a 4° CP sampling is used. A positive correlation was observed for MCSv and LTMCS with r values above 0.5. In order to score plan complexity, threshold values of LTMCS were defined. The average Γ passing rates were significantly higher for the plans created using the finer CP spacing (3°∕2°) compared to the plans optimized using the standard 4° spacing (Student t-test p < 0.05). The correlation between LT and passing rates was strongly diminished when plans with finer angular separations were considered, yielding Pearson's r absolute values below 0.45., Conclusions: At 4° CP sampling, LT, MCSv, and LTMCS were found to be significantly correlated with VMAT dosimetric accuracy, expressed as Γ pass-rates. These parameters were found to be possible candidates for scoring plan complexity using threshold values. A finer CP separation (3°∕2°) led to a significant increase in dosimetric accuracy for plans with high leaf travel values, and to a decrease in correlation between LT and Γ passing rates. These results indicated that the influence of LT on VMAT dosimetric accuracy can be controlled by reducing CP separation. CP spacing for all plans requiring large leaf motion should not exceed 3°. The reported data were integrated to optimize our clinical workflow for plan creation, optimization, selection among rival plans, and patient-specific QA of VMAT treatments.

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]

Objectives: This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers., Materials and Methods: The plans were first generated using automated RapidPlan with scripting application programming interface (API) and then further refined through manual optimization (RP+MP) to improve plan quality. The quality of 20 plans was assessed, taking into account dose statistics and clinical plan acceptability. The impact of beam complexity on mechanical performance was analyzed using parameters such as leaf speed (LS), leaf acceleration (LA), mean-field area (MFA), cross-axis score (CAS), closed leaf score (CLS), small aperture score (SAS), and monitor units per control point (MU/CP). Patient-specific quality assurance (PSQA) was conducted to determine differences between the RP+MP and original plans., Results: No differences in the heterogeneity index and conformity number were observed between the RP+MP and original plans. The RP+MP plan was superior to the original plan for sparing the left cochlea, left and right internal auditory canals, chiasm, and left optic nerve. Significant differences (p < 0.05) were identified in CAS, SAS 1   mm , SAS 2   mm , and SAS 10mm . However, there was no significant difference in PSQA between the RP+MP and original plans. The RP+MP plan without any modifications was clinically acceptable in 45% of cases., Conclusion: The automated RP with scripting API followed by MP (RP+MP) yielded a high-quality plan in terms of dose statistics and clinical acceptability. The RP+MP plan yielded a higher CAS and SAS compared with the original plan. Nevertheless, there was no significant difference in PSQA between the RP+MP and original plans., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

This study aimed to identify systematic errors in measurement-, calculation-, and prediction-based patient-specific quality assurance (PSQA) methods for volumetric modulated arc therapy (VMAT) on lung cancer and to standardize the gamma passing rate (GPR) by considering systematic errors during data assimilation. This study included 150 patients with lung cancer who underwent VMAT. VMAT plans were generated using a collapsed-cone algorithm. For measurement-based PSQA, ArcCHECK was employed. For calculation-based PSQA, Acuros XB was used to recalculate the plans. In prediction-based PSQA, GPR was forecasted using a previously developed GPR prediction model. The representative GPR value was estimated using the least-squares method from the three PSQA methods for each original plan. The unified GPR was computed by adjusting the original GPR to account for systematic errors. The range of limits of agreement (LoA) were assessed for the original and unified GPRs based on the representative GPR using Bland-Altman plots. For GPR (3%/2 mm), original GPRs were 94.4 ± 3.5%, 98.6 ± 2.2% and 93.3 ± 3.4% for measurement-, calculation-, and prediction-based PSQA methods and the representative GPR was 95.5 ± 2.0%. Unified GPRs were 95.3 ± 2.8%, 95.4 ± 3.5% and 95.4 ± 3.1% for measurement-, calculation-, and prediction-based PSQA methods, respectively. The range of LoA decreased from 12.8% for the original GPR to 9.5% for the unified GPR across all three PSQA methods. The study evaluated unified GPRs that corrected for systematic errors. Proposing unified criteria for PSQA can enhance safety regardless of the methods used., Competing Interests: Declarations. Ethics approval: This study (R1446) was approved by the Institutional Review Board of the Kyoto University Hospital on January 30, 2018. Competing interests: The authors have no relevant conflicts of interest to disclose., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

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]

Purpose: This study aimed to assess the dosimetric advantages of dual arc increments in head and neck volumetric-modulated arc therapy (VMAT) in the Monaco treatment planning system (TPS)., Methods: Three VMAT plans were created for each of the 10 patients by prescribing 70 Gy in 35 fractions with arc increment combinations of 30°/30°, 15°/15°, and 30°/15° in the Monaco TPS. The dose to 0.03 cm 3 volume (D 0.03cc ), conformity number, and homogeneity and gradient indices was compared for the planning target volume (PTV), and the D 0.03cc and mean dose (D mean ) of the spinal cord, brain stem, parotid glands were compared. For plan complexity evaluation, the monitor unit and various related metrics were compared. Wilcoxon signed-rank tests were performed across plans for the evaluated indicators., Results: For PTV, plans with 30°/15° showed comparable D 0.03cc and homogeneity and gradient indices to those of plans with 30°/30° and 15°/15° while exhibiting a better conformity number. The D 0.03cc for spinal cord and brain stem for plans with 30°/15° were 26.0% and 20.8% less than those with 30°/30° and 16.8% and 19.0% less than those with 15°/15°, respectively. The D mean for the left and right parotid glands under plans with 30°/15° were 17.4% and 13.2% less than those with 30°/30° and 14.0% and 9.8% less than those with 15°/15°, respectively. The total monitor unit in plans with 30°/15° was less than that in other plans but with no significance. The plans with 30°/15° showed higher modulation complexity and plan-averaged irregularity, while no significant differences observed in both plan-averaged area and modulation compared with other plans., Conclusion: In head and neck VMAT, a dual arc increment of 30°/15° seems advisable because it can substantially reduce doses to normal tissues with comparable delivery efficiency while maintaining target dose coverage., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

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]