Portability of IMRT QA between matched linear accelerators.

PURPOSE: To determine if patient-specific IMRT quality assurance can be measured on any matched treatment delivery system (TDS) for patient treatment delivery on another. METHODS: Three VMAT plans of varying complexity were created for each available energy for head and neck, SBRT lung, and right chestwall anatomical sites. Each plan was delivered on three matched Varian TrueBeam TDSs to the same Scandidos Delta4 Phantom+ diode array with only energy-specific device calibrations. Dose distributions were corrected for TDS output and then compared to TPS calculations using gamma analysis. Round-robin comparisons between measurements from each TDS were also performed using point-by-point dose difference, median dose difference, and the percent of point dose differences within 2% of the mean metrics. RESULTS: All plans had more than 95% of points passing a gamma analysis using 3%/3 mm criteria with global normalization and a 20% threshold when comparing measurements to calculations. The tightest gamma analysis criteria where a plan still passed > 95% were similar across delivery systems-within 0.5%/0.5 mm for all but three plan/energy combinations. Median dose deviations in measurement-to-measurement comparisons were within 0.7% and 1.0% for global and local normalization, respectively. More than 90% of the point differences were within 2%. CONCLUSION: A set of plans spanning available energies and complexity levels were delivered by three matched TDSs. Comparisons to calculations and between measurements showed dose distributions delivered by each TDS using the same DICOM RT-plan file meet tolerances much smaller than typical clinical IMRT QA criteria. This demonstrates each TDS is modeled to a similar accuracy by a common class (shared) beam model. Additionally, it demonstrates that dose distributions from one TDS show small differences in median dose to the others. This is an important validation component of the common beam model approach, allowing for operational improvements in the clinic.

Geometric evaluation and quantifying dosimetric impact of diverse deformable image registration algorithms on abdomen images with biomechanically modeled deformations.

PURPOSE: Deformable image registration (DIR) has been increasingly used in radiation therapy (RT). The accuracy of DIR algorithms and how it impacts on the RT plan dosimetrically were examined in our study for abdominal sites using biomechanically modeled deformations. METHODS: Five pancreatic cancer patients were enrolled in this study. Following the guidelines of AAPM TG-132, a patient-specific quality assurance (QA) workflow was developed to evaluate DIR for the abdomen using the TG-132 recommended virtual simulation software ImSimQA (Shrewsbury, UK). First, the planning CT was deformed to simulate respiratory motion using the embedded biomechanical model in ImSimQA. Additionally, 5 mm translational motion was added to the stomach, duodenum, and small bowel. The original planning CT and the deformed CT were then imported into Eclipse and MIM to perform DIR. The output displacement vector fields (DVFs) were compared with the ground truth from ImSimQA. Furthermore, the original treatment plan was recalculated on the ground-truth deformed CT and the deformed CT (with Eclipse and MIM DVF). The dose errors were calculated on a voxel-to-voxel basis. RESULTS: Data analysis comparing DVF from Eclipse versus MIM show the average mean DVF magnitude errors of 2.8 ± 1.0  versus 1.1 ± 0.7 mm for stomach and duodenum, 5.2 ± 4.0  versus 2.5 ± 1.0 mm for small bowel, and 4.8 ± 4.1  versus 2.7 ± 1.1 mm for the gross tumor volume (GTV), respectively, across all patients. The mean dose error on stomach+duodenum and small bowel were 2.3 ± 0.6% for Eclipse, and 1.0 ± 0.3% for MIM. As the DIR magnitude error increases, the dose error range increase, for both Eclipse and MIM. CONCLUSION: In our study, an initial assessment was conducted to evaluate the accuracy of DIR and its dosimetric impact on radiotherapy. A patient-specific DIR QA workflow was developed for pancreatic cancer patients. This workflow exhibits promising potential for future implementation as a clinical workflow.

Impact of Reference Selection on Gamma Analysis in Patient-Specific Quality Assurance for Monte Carlo-Based Treatment Planning Systems.

OBJECTIVE: Our study aimed to establish a standardized methodology for selecting "reference" and "evaluated" distributions in gamma analysis for Monte Carlo (MC) based intensity modulated treatment plans. Evaluation of importance of reference selection in MC based and non MC based treatment planning systems were analysed using a study classification. METHODS: Three categories were utilized to analyzed gamma passing rates across using different treatment planning systems (TPS) and detectors for thirty five patients. Category 1 utilized MC-based Monaco TPS plans and a 2 dimensional(2D) I'mRTMatriXX detector. Category 2 employed non-MC-based Eclipse TPS plans, assessed with a 2D I'mRTMatriXX detector. In Category 3, MC-based Monaco TPS plans were validated using a Dolphin detector. All plans were subjected to analysis using gamma criteria, which considered a dose difference of 3% and a distance to agreement of 3mm. Additionally, another set of gamma criteria was employed, with a dose difference of 3% and a distance to agreement of 2mm. An introduced Asymmetric factors in both 2D and 3D analysis will quantify the asymmetric nature of gamma based on the choice of "reference" distribution. RESULT: For 2D Gamma analysis, MC-based Monaco TPS and I'mRTMatriXX showed a consistent positive Zk2D trend for all patients, with significant p-values below 0.01 for both gamma passing criteria. In contrast, non-MC based Eclipse TPS exhibited varied Zk2D results, with non-significant p-values. In 3D Gamma analysis, all patients exhibited positive Zk3D values with significant p-values below 0.01 when "references" were swapped. The Pearson correlation between asymmetricity and isodose volumes was notably high at 0.99 for both gamma criteria. CONCLUSION: Our study highlights the imperative of using MC-based TPS as the definitive "reference" in gamma analysis for patient specific quality assurance of intensity modulated radiation therapy, emphasizing that variations can mislead results, especially given gamma analysis's sensitivity to MC calculation noise.

Investigating and Analyzing Prognostic Factors and Their Impact on Recurrent Cervical Cancers.

The incidence of cervical cancer in India is significantly high, and the average recurrence age is much less. The standard line of treatment consists of concurrent chemoradiotherapy. If a recurrence occurs, the treatment options or set of interventions are limited and suboptimal. Through this review, we have analyzed and classified the possible prognostic factors for cervical cancer into three broad categories, viz., (a) disease-related factors, (b) patient-related factors, and (c) treatment-related factors. Disease-related factors include tumor histology, tumor size, stage, parametrial involvement (PMI), Prognostic Nutritional Index (PNI), lymphovascular space invasion (LVSI), and nodal status. Patient-related factors include overall treatment time (OTT), nutritional status, hemoglobin level, comorbidities, and age. Treatment-related factors include addition of chemotherapy, techniques of external beam radiotherapy (EBRT), techniques of brachytherapy, and quality assurance for radiation therapy delivery. Out of these, extremely significant prognostic factors were tumor size and stage, nodal status, PMI, nutritional status, and addition of chemotherapy. Impactful factors include younger age, histology, LVSI, associated comorbidities, hemoglobin level, OTT, and patient-specific quality assurance. The factor that is not related or significant is the technique used for EBRT and brachytherapy delivery.

Effect of modulation factor and low dose threshold level on gamma pass rates of single isocenter multi-target SRT treatment plans.

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.

Unifying gamma passing rates in patient-specific QA for VMAT lung cancer treatment based on data assimilation.

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.

MU variability in CBCT-guided online adaptive radiation therapy.

PURPOSE: CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large. METHODS: Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array. RESULTS: MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose. CONCLUSIONS: MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.

Assessment of stereotactic high-resolution detectors for stereotactic body radiotherapy: comparative analysis between myQA® SRS and Gafchromic EBT-XD films.

The study aimed to evaluate dosimetry systems used for stereotactic body radiotherapy (SBRT), specifically 2D array dosimetry and film dosimetry systems, for exploring their characteristics and clinical suitability. For this, high-resolution myQA SRS detectors and Gafchromic EBT-XD films were employed. Film analysis included net optical density (OD) values depending on energy, dose rate, scanner orientation, scanning side, and post-exposure growth. For myQA SRS, signal values were evaluated in terms of dose rate (400-1400 MU/min) and angular dependence (0-180° at 30° intervals) along with couch angles of 0°, 45°, and 90°. Pre-treatment verification included 32 SBRT patients for whom myQA SRS results were compared with those obtained with Gafchromic EBT-XD films. Analysis revealed less than 1% deviation in net OD for energy and dose rate dependence. Scanner orientation caused 2.5% net OD variation, with minimal differences between film front and back scan orientations (variance < 1.0%). A rapid OD rise occurred within six hours post-exposure, followed by gradual increase. The myQA SRS detector showed - 3.7% dose rate dependence (400 MU/min), while the angular dependence at 90° was - 26.7%. A correction factor effectively reduced these differences to < 1%. For myQA SRS, gamma passing rates were-93.6% (2%/1 mm), while those for EBT-XD films were-92.8%. Improved rates were observed with 3%/1 mm: for myQA SRS-97.9%, and for EBT-XD film-98.16%. In contrast, for 2%/2 mm with 10% threshold, for myQA SRS-97.7% and for EBT-XD film-98.97% were obtained. It is concluded that both myQA SRS detectors and EBT-XD films are suitable for SBRT pre-treatment verification, ensuring accuracy and reliability. However, myQA SRS detectors are preferred over EBT-XD film due to the fact that they offer real-time measurements and user-friendly features.

Predicting the error magnitude in patient-specific QA during radiotherapy based on ResNet.

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude. OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet. METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude. RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS. CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.

PURPOSE: This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT). METHODS: A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison. RESULTS: The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category. CONCLUSION: The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

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

PURPOSE: Machine learning (ML) models have been demonstrated to be beneficial for optimizing the workload of patient-specific quality assurance (PSQA). Implementing them in clinical routine frequently requires third-party applications beyond the treatment planning system (TPS), slowing down the workflow. To address this issue, a PSQA outcomes predictive model was carefully selected and validated before being fully integrated into the TPS. MATERIALS AND METHODS: Nine ML algorithms were evaluated using cross-validation. The learning database was built by calculating complexity metrics (CM) and binarizing PSQA results into "pass"/"fail" classes for 1767 VMAT arcs. The predictive performance was evaluated using area under the ROC curve (AUROC), sensitivity, and specificity. The ML model was integrated into the TPS via a C# script. Script-guided reoptimization impact on PSQA and dosimetric results was evaluated on ten VMAT plans with "fail"-predicted arcs. Workload reduction potential was also assessed. RESULTS: The selected model exhibited an AUROC of 0.88, with a sensitivity and specificity exceeding 50 % and 90 %, respectively. The script-guided reoptimization of the ten evaluated plans led to an average improvement of 1.4 ± 0.9 percentage points in PSQA results, while preserving the quality of the dose distribution. A yearly savings of about 140 h with the use of the script was estimated. CONCLUSIONS: The proposed script is a valuable complementary tool for PSQA measurement. It was efficiently integrated into the clinical workflow to enhance PSQA outcomes and reduce PSQA workload by decreasing the risk of failing QA and thereby, the need for repeated replanning and measurements.

Is it necessary to perform measurement-based patient-specific quality assurance for online adaptive radiotherapy with Elekta Unity MR-Linac?

This study aimed to investigate the necessity of measurement-based patient-specific quality assurance (PSQA) for online adaptive radiotherapy by analyzing measurement-based PSQA results and calculation-based 3D independent dose verification results with Elekta Unity MR-Linac. There are two workflows for Elekta Unity enabled in the treatment planning system: adapt to position (ATP) and adapt to shape (ATS). ATP plans are those which have relatively slighter shifts from reference plans by adjusting beam shapes or weights, whereas ATS plans are the new plans optimized from the beginning with probable re-contouring targets and organs-at-risk. PSQA gamma passing rates were measured using an MR-compatible ArcCHECK diode array for 78 reference plans and corresponding 208 adaptive plans (129 ATP plans and 79 ATS plans) of Elekta Unity. Subsequently, the relationships between ATP, or ATS plans and reference plans were evaluated separately. The Pearson's r correlation coefficients between ATP or ATS adaptive plans and corresponding reference plans were also characterized using regression analysis. Moreover, the Bland-Altman plot method was used to describe the agreement of PSQA results between ATP or ATS adaptive plans and reference plans. Additionally, Monte Carlo-based independent dose verification software ArcherQA was used to perform secondary dose check for adaptive plans. For ArcCHECK measurements, the average gamma passing rates (ArcCHECK vs. TPS) of PSQA (3%/2 mm criterion) were 99.51% ± 0.88% and 99.43% ± 0.54% for ATP and ATS plans, respectively, which were higher than the corresponding reference plans 99.34% ± 1.04% (p < 0.05) and 99.20% ± 0.71% (p < 0.05), respectively. The Pearson's r correlation coefficients were 0.720 between ATP and reference plans and 0.300 between ATS and reference plans with ArcCHECK, respectively. Furthermore, >95% of data points of differences between both ATP and ATS plans and reference plans were within ±2σ (standard deviation) of the mean difference between adaptive and reference plans with ArcCHECK measurements. With ArcherQA calculation, the average gamma passing rates (ArcherQA vs. TPS) were 98.23% ± 1.64% and 98.15% ± 1.07% for ATP and ATS adaptive plans, separately. It might be unnecessary to perform measurement-based PSQA for both ATP and ATS adaptive plans for Unity if the gamma passing rates of both measurements of corresponding reference plans and independent dose verification of adaptive plans have high gamma passing rates. Periodic machine QA and verification of adaptive plans were recommended to ensure treatment safety.

Development and Quality Assurance of Multileaf Collimator (MLC) Auto-Feathering Junctions for Multi-Isocenter Supine Volumetric Modulated Arc Therapy (VMAT) Craniospinal Axis Irradiation on Halcyon.

Currently, there is a lack of methods and tools that efficiently evaluate the auto-feathering junctions created by multileaf collimator (MLCs) for supine volumetric modulated arc therapy (VMAT) craniospinal irradiation (CSI) plans. We have investigated the feasibility of stitching together multi-isocenter fluence maps to then analyze the feathered junctions for patient-specific quality assurance (QA). Furthermore, we investigated the capability of Halcyon for the treatment of CSI patients. Three patients, who previously underwent VMAT CSI treatment on TrueBeam (6-MV flattening filter-free (FFF)) for 36 Gy in 20 fractions were replanned for Halcyon. A multi-isocenter approach with only translational superior-inferior shifts was used for both platforms. Each isocenter consists of two full arcs with anterior avoidance sectors, ±5° collimator rotations between arcs, and 5-8 cm of overlapping MLC auto-feathering junctions. All plans were QA'd via electronic portal imaging device (EPID) portal dosimetry and analyzed with a gamma criteria of 3%/3 mm. A variety of plan quality metrics were analyzed to evaluate dose distributions to the target, doses to organs at risk (OARs), and integral dose to the patient. A MATLAB script was developed to stitch the calculated and measured fluence maps in order to perform patient-specific QA for the composite fluence. The Halcyon plans provided highly conformal and homogenous dose distributions to the entire CSI target, superior to the clinical TrueBeam plans, while sparing critical organs with significantly lower values of V10Gy and V18Gy by up to 2% and 2.5%, respectively. Qualitative depictions of vertical dose profiles from the stitched DICOM of the entire CSI target for both planned and delivered fluence maps demonstrated equivalency, with slightly lower average pass rates with Halcyon (97%) compared to TrueBeam (99.9%). This approach to stitch multiple measured versus calculated EPID fluence maps has shown to be a feasible and accurate method and will be helpful for comprehensive VMAT CSI QA on both platforms. Further implementation of this script will be used in examining dosimetric impacts of daily patient positioning errors at MLC auto-feathering junctions.

Patient-specific QA for the HyperArc technique using gafchromic film with multiple calibration methods.

PURPOSE: This study aims to evaluate different methods for calibrating EBT-XD films to develop a precise pre-treatment verification method for stereotactic radiotherapy (SRT) patients using the HyperArc (HA, Varian Medical System) technique. METHODS: Gafchromic EBT-XD films were calibrated using three different approaches: manual calibration, EDW calibration, and PDD calibration. Films were digitalized with an Epson V850 Pro scanner applying the local scanning protocol. Three clinical treatment plans were selected for evaluation. Patient-specific QA films were irradiated in the Mobius MVP phantom and the STEEV phantom. Scanned film images were converted into dose images using the calibration curves. Gamma analysis was performed to compare film dose and TPS calculated dose with various criteria. RESULTS: The scan-to-scan variation was evaluated to be ≤ 0.2%. The accuracy of the calibration curves was verified and the deviation from the converted dose deviates ≤ 3% from the known delivered dose. The gamma passing rate for all calibration methods was found to be over 94% with clinically relevant criteria. EDW calibration demonstrated higher average gamma passing rates compared to the manual method for single target plans, which is 99% ± 1.2% and 98.8% ± 1.5%, respectively. PDD method demonstrated improved agreement for multiple targets with the result of 99.3% ± 0.8%. CONCLUSIONS: The three calibration methods were validated, and they produced accurate calibration curves for EBT-XD films to enable pre-treatment patient-specific QA for stereotactic radiotherapy.

Patient specific quality assurance in SBRT: a systematic review of measurement-based methods.

This topical review focuses on Patient-Specific Quality Assurance (PSQA) approaches to stereotactic body radiation therapy (SBRT). SBRT requires stricter accuracy than standard radiation therapy due to the high dose per fraction and the limited number of fractions. The review considered various PSQA methods reported in 36 articles between 01/2010 and 07/2022 for SBRT treatment. In particular comparison among devices and devices designed for SBRT, sensitivity and resolution, verification methodology, gamma analysis were specifically considered. The review identified a list of essential data needed to reproduce the results in other clinics, highlighted the partial miss of data reported in scientific papers, and formulated recommendations for successful implementation of a PSQA protocol.

Angular correction methodology and characterization of a high-resolution CMOS array for patient specific quality assurance on a robotic arm linac.

PURPOSE: To develop an angular correction methodology and characterize a high-resolution complementary metal-oxide-semiconductor (CMOS) array for patient specific quality assurance on a robotic arm linear accelerator. METHODS: Beam path files from the treatment planning software (TPS) were used to calculate the angle of radiation beam with respect to the detector plane. Beams from multiple discrete angles were delivered to the CMOS detector array and an angular dependency look up table (LUT) was created. The LUT was then used to correct for the angular dependency of the detector. An iso-centric 5 mm fixed cone, non iso-centric multi-target fixed cone, 10 mm Iris and a multi-leaf collimator (MLC) based collimated plan were delivered to the phantom and compared to the TPS with and without angular correction applied. Additionally, the CMOS array was compared to gafchromic film and a diode array. RESULTS: Large errors of up to 30% were observed for oblique angles. When angular correction was applied, the gamma passing rate increased from 99.2% to 100% (average gamma value decreased from 0.29 to 0.14) for the 5-mm iso-centric cone plan. Similarly, the passing rate increased from 84.0% to 100% for the Iris plan and from 49.98% to 98.4% for the MLC plan when angular correction was applied. For the multi-target plan, applying angular correction improved the gamma passing rate from 94% to 99.6%. The 5 mm iso-centric fixed cone plan was also delivered to film, and the gamma passing rate was 91.3% when using gafchromic film as the reference dataset, whereas the diode array provided insufficient sampling for this plan. CONCLUSION: A methodology of calculating the beam angle based on the beam path files was developed and validated. The array was demonstrated to be superior to other quality assurance tools because of its sub-millimeter spatial resolution and immediate read out of the results.

Delivery accuracy of VMAT on two beam-matched linacs provided by accelerated go live service.

INTRODUCTION: Dosimetric accuracy is critical when a patient treated with volumetric modulated arc therapy (VMAT) is transferred to another beam-matched linac. To evaluate the performance of Accelerated Go Live (AGL) service, the measured beam characteristics and patient specific quality assurance (QA) results between two AGL-matched linacs were compared. MATERIALS AND METHODS: Two VersaHD linacs were installed using the AGL service. After the installation, the beam data such as percentage depth dose (PDD), lateral profiles and output factors for all photon beams were measured. Relative doses were also measured as a function of the multi-leaf collimator (MLC) leaf gap width. Subsequently, VMAT plans were created for prostate, pelvis, head and neck, liver, lung cancers and multiple brain metastases. Dose distributions and point doses were measured by multi-dimensional detectors and ionization chambers for patient specific quality assurance, and comparisons were made between the two linacs. RESULTS: Dose differences in PDDs were all within ± 1% except the entrance region, and the averaged gamma indices of the lateral profiles were within 0.3. The differences in doses as a function of the MLC leaf gap width between the two linacs were within ±0.5%. For all the plans, gamma passing rates were all higher than 95% with criteria of 2%/2 mm. The average and the SD of dose differences on the multi-dimensional detector between both measurements was 0.06 ± 2.12%, and the average of point dose differences was -0.03 ± 0.33%. CONCLUSION: We have evaluated the AGL performance in the context of beam characteristics and patient specific QA. It was demonstrated that the AGL service provides an accurate VMAT treatment reproducibility for many tumor sites with gamma pass rates greater than 95% under criteria of 2%/2 mm.

Development of a deep learning-based error detection system without error dose maps in the patient-specific quality assurance of volumetric modulated arc therapy.

To detect errors in patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), we proposed an error detection method based on dose distribution analysis using unsupervised deep learning approach and analyzed 161 prostate VMAT beams measured with a cylindrical detector. For performing error simulation, in addition to error-free dose distribution, dose distributions containing nine types of error, including multileaf collimator (MLC) positional errors, gantry rotation errors, radiation output errors and phantom setup errors, were generated. Only error-free data were employed for the model training, and error-free and error data were employed for the tests. As a deep learning model, the variational autoencoder (VAE) was adopted. The anomaly of test data was quantified by calculating Mahalanobis distance based on the feature vectors acquired from a trained encoder. Based on this anomaly, test data were classified as 'error-free' or 'any-error.' For comparison with conventional approaches, gamma (γ)-analysis was performed, and supervised learning convolutional neural network (S-CNN) was constructed. Receiver operating characteristic curves were obtained to evaluate their performance with the area under the curve (AUC). For all error types, except systematic MLC positional and radiation output errors, the performance of the methods was in the order of S-CNN ˃ VAE-based ˃ γ-analysis (only S-CNN required error data for model training). For example, in random MLC positional error simulation, the AUC of our method, S-CNN and γ-analysis were 0.699, 0.921 and 0.669, respectively. Our results showed that the VAE-based method has the potential to detect errors in patient-specific VMAT QA.

Clinical experience on patient-specific quality assurance for CBCT-based online adaptive treatment plan.

PURPOSE: Ethos CBCT-based adaptive radiotherapy (ART) system can generate an online adaptive plan by re-optimizing the initial reference plan based on the patient anatomy at the treatment. The optimization process is fully automated without any room for human intervention. Due to the change in anatomy, the ART plan can be significantly different from the initial plan in terms of plan parameters such as the aperture shapes and number of monitor units (MUs). In this study, we investigated the feasibility of using calculation-based patient specific QA for ART plans in conjunction with measurement-based and calculation-based QA for initial plans to establish an action level for the online ART patient-specific QA. METHODS: A cohort of 98 cases treated on CBCT-based ART system were collected for this study. We performed measurement-based QA using ArcCheck and calculation-based QA using Mobius for both the initial plan and the ART plan for analysis. For online the ART plan, Mobius calculation was conducted prior to the delivery, while ArcCheck measurement was delivered on the same day after the treatment. We first investigated the modulation factors (MFs) and MU numbers of the initial plans and ART plans, respectively. The γ passing rates of initial and ART plan QA were analyzed. Then action limits were derived for QA calculation and measurement for both initial and online ART plans, respectively, from 30 randomly selected patient cases, and were evaluated using the other 68 patient cases. RESULTS: The difference in MF between initial plan and ART-plan was 12.9% ± 12.7% which demonstrates their significant difference in plan parameters. Based on the patient QA results, pre-treatment calculation and measurement results are generally well aligned with ArcCheck measurement results for online ART plans, illustrating their feasibility as an indicator of failure in online ART QA measurements. Furthermore, using 30 randomly selected patient cases, the γ analysis action limit derived for initial plans and ART plans are 89.6% and 90.4% in ArcCheck QA (2%/2 mm) and are 92.4% and 93.6% in Mobius QA(3%/2 mm), respectively. According to the calculated action limits, the ArcCheck measurements for all the initial and ART plans passed QA successfully while the Mobius calculation action limits flagged seven and four failure cases respectively for initial plans and ART plans, respectively. CONCLUSION: An ART plan can be substantially different from the initial plan, and therefore a separate session of ART plan QA is needed to ensure treatment safety and quality. The pre-treatment QA calculation via Mobius can serve as a reliable indicator of failure in online ART plan QA. However, given that Ethos ART system is still relatively new, ArcCheck measurement of initial plan is still in practice. It may be skipped as we gain more experience and have better understanding of the system.

Comparing log file to measurement-based patient-specific quality assurance.

Recent technological advances have allowed the possibility of performing patient-specific quality assurance (QA) without time-intensive measurements. The objectives of this study are to: (1) compare how well the log file-based Mobius QA system agrees with measurement-based QA methods (ArcCHECK and portal dosimetry, PD) in passing and failing plans, and; (2) evaluate their error sensitivities. To these ends, ten phantom plans and 100 patient plans were measured with ArcCHECK and PD on VitalBeam, while log files were sent to Mobius for dose recalculation. Gamma evaluation was performed using criteria 3%/2 mm, per TG218 recommendations, and non-inferiority of the Mobius recalculation was determined with statistical testing. Ten random plans were edited to include systematic errors, then subjected to QA. Receiver operating characteristic curves were constructed to compare error sensitivities across the QA systems, and clinical significance of the errors was determined by recalculating dose to patients. We found no significant difference between Mobius, ArcCHECK, and PD in passing plans at the TG218 action limit. Mobius showed good sensitivity to collimator and gantry errors but not MLC bank shift errors, but could flag discrepancies in treatment delivery. Systematic errors were clinically significant only at large magnitudes; such unacceptable plans did not pass QA checks at the TG218 tolerance limit. Our results show that Mobius is not inferior to existing measurement-based QA systems, and can supplement existing QA practice by detecting real-time delivery discrepancies. However, it is still important to maintain rigorous routine machine QA to ensure reliability of machine log files.