Volumetric Modulated Arc Therapy Plans Research Articles

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

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. 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 6cm off-axis. SMC measured dose distributions were compared against AAA and VMC++ via gamma analysis (3%/1mm) with LDT of 10% to 80% using SNC Patient software. 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. 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.

Innovative regression model-based decision support tool for optimizing radiotherapy techniques in thoracic esophageal cancer.

Modern radiotherapy exemplified by intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), has transformed esophageal cancer treatment. Facing challenges in treating thoracic esophageal cancer near vital organs, this study introduces a regression model-based decision support tool for the optimal selection of radiotherapy techniques. We enrolled 106 patients diagnosed with locally advanced thoracic esophageal cancer in this study and designed individualized IMRT and VMAT radiotherapy plans for each patient. Detailed dosimetric analysis was performed to evaluate the differences in dose distribution between the two radiotherapy techniques across various thoracic regions. Single-factor and multifactorial logistic regression analyses were employed to establish predictive models (P1 and P2) and factors such as TLV/PTV ratio. These models were used to predict the compliance and potential advantages of IMRT and VMAT plans. External validation was performed in a validation group of 30 patients. Using predictive models, we developed a data-driven decision support tool. For upper thoracic cases, VMAT plans were recommended; for middle/lower thoracic cases, the tool guided VMAT/IMRT choices based on TLV/PTV ratio. Models P1 and P2 assessed IMRT and VMAT compliance. In validation, the tool showed high specificity (90.91%) and sensitivity (78.95%), differentiating IMRT and VMAT plans. Balanced performance in compliance assessment demonstrated tool reliability. In summary, our regression model-based decision support tool provides practical guidance for selecting optimal radiotherapy techniques for thoracic esophageal cancer patients. Despite a limited sample size, the tool demonstrates potential clinical benefits, alleviating manual planning burdens and ensuring precise, individualized treatment decisions for patients.

Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators.

To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets. Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow. Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09±02:06 (intact) and 07:12±01:04 (bed). This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

Pencil Beam Scanning Proton Therapy as Single Vocal Cord Irradiation for Early-Stage Glottic Cancer

PurposeSingle vocal cord irradiation (SVCI) is a promising technique to maintain excellent oncologic control and potentially improve upon toxicities for treatment of early-stage glottic squamous cell carcinomas. We sought to investigate whether pencil beam scanning (PBS) proton therapy could improve upon the already favorable dose gradients demonstrated with volumetric modulated arc therapy (VMAT) SVCI. Patients and MethodsA 64-year-old gentleman was treated in our department with 6X-flattening filter-free VMAT SVCI to 58.08 Gy in 16 fractions for a T1a well-differentiated squamous cell carcinoma of the left true vocal cord and tolerated it well with good local control. Comparative PBS plans were created in Raystation for the Varian ProBeam with clinical target volume (CTVs) generated to mimic the prescription target volume extent of the VMAT planning target volumes when accounting for PBS plan robustness (±3 mm translational shifts, 3.5% density perturbation). A 3-field single-field optimization plan was selected as dosimetrically preferable. Dosimetric variables were compared. ResultsSeveral organs at risk doses improved with PBS, including the maximum and mean dose to ipsilateral carotids, maximum and mean dose to contralateral carotid, maximum dose to the spinal cord, maximum and mean dose to inferior constrictor/cricopharyngeus, maximum and mean dose to the uninvolved vocal cord, and mean dose to the thyroid gland. There are tradeoffs in skin dose depending on location relative to the target—with the highest and lowest isodoses extending more into the skin with the VMAT plan but with the moderate isodose lines covering a wider area with the PBS plan, but we deemed it tolerable regardless. ConclusionSVCI is a promising strategy for maintaining the oncologic effectiveness of whole-larynx photon radiation while potentially improving upon the historic toxicity profile. The favorable dose distribution with PBS with respect to organs at risk dosimetry for PBS may allow for further improvements upon VMAT SVCI strategies. Clinical implementation of PBS SVCI may be considered.

Impact of delivery variations on 3D dose distributions for volumetric modulated arc therapy plans of various complexity.

Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

Optimization of Replanning Processes for Volumetric Modulated Arc Therapy Plans at Risk of QA Failure Predicted by a Machine Learning Model

Patient-specific quality assurance (PSQA) procedures ensure the safe delivery of volumetric modulated arc therapy (VMAT) plans. PSQA requires extensive time and resources and may cause treatment delays if replanning is needed due to failures. Recently, our group developed a machine learning (ML) model predicting gamma passing rate (GPR) for VMAT arcs. This study explores automatable replanning strategies for plans identified at risk of failure, aiming to improve deliverability while maintaining dosimetric quality. Between 2022 and 2023, our ML model analyzed 1252 VMAT plans. Ten patients having a predicted GPR (pGPR) <95% were selected. Replanning strategies consisted of limiting monitor units (MUlimit) and employing the aperture shape controller (ASC) tool. Re-optimized plans were compared with the originals in terms of dose volume constraints (DVCs) for the target and organs-at-risk (OARs), and deliverability using the modulation complexity score (MCS), pGPR, and measured GPR (mGPR). Forty-five re-optimizations were performed. Replanning led to an increase in DVCs for OARs and a reduction for the target. Complexity decreased, reflected by the increase in the MCS from 0.17 to 0.21 (MUlimit) and 0.20 (ASC). The deliverability improved, with the pGPR increasing from 93.3% to 94.4% (MUlimit) and 95.1% (ASC), and the mGPR from 99.3% to 99.7% (MUlimit) and 99.8% (ASC). Limiting the MUs or utilizing the ASC reduced the complexity of plans and improved the GPR without compromising the dosimetric quality. These strategies can be used to automate replanning procedures, reduce the workload related to PSQA, and improve patient safety.

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

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures. By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability. The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation. The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific. This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Development and validation of an automated Tomotherapy planning method for cervical cancer

PurposeThis study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness.Materials and methodsThe study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. We compared A-TOMO with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis, with a P-value < 0.05 indicating statistical significance.ResultsA-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+ < 0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V50Gy, V40Gy and Dmean for the bladder and rectum, as well as Dmean for the bowel bag, femoral heads, and kidneys (all P+ < 0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 min.ConclusionWe have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.

Evaluation of magnetic resonance imaging derived synthetic computed tomography for proton therapy planning in prostate cancer

Background and purposeMagnetic resonance imaging (MRI)-only workflow is used in photon radiotherapy (RT) today, but not yet for protons. To bring MRI-only proton RT into clinical use, proton dose calculation on MRI-derived synthetic CT (sCT) must be validated. We evaluated proton dose calculation accuracy of prostate cancer proton plans using a commercially available sCT generator already validated for photon planning. Materials and methodsThe retrospective planning study included 10 prostate cancer patients who underwent MRI and planning CT (pCT) before RT. sCT were generated from the MRI with MRI Planner v2.3, and compared to pCT using structural mean absolute error (MAE). The pCT was used to create one-arc volumetric modulated arc therapy (VMAT) photon plan and two-field intensity modulated proton therapy (IMPT) proton plan. Each plan was recalculated on the sCT and compared to pCT doses. Dose volume histogram parameters, gamma analyses and range differences were evaluated. ResultsMedian MAE for the body contour was 71 HU. Dose differences between pCT and sCT were small and similar for VMAT and IMPT plans. Median (range) gamma pass rates were lower for IMPT plans with 95.8 (89.3–98.7) % compared to VMAT plans with 99.4 (91.2–99.6) %. The proton range difference was 1.0 (interquartile range –0.1 – 0.2) mm deeper for sCT compared to the reference. ConclusionMRI-only IMPT planning for prostate cancer seems feasible in a clinical setting for the evaluated beam arrangement and sCT generator. More patients and evaluation of other beam arrangements are needed for a more general conclusion.

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

Background and purposeRadiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity. Materials and methodsLean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013–2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool’s impact was assessed after nine months of clinical use. ResultsAmong 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization. ConclusionsML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.

Evaluation of Normal Tissue Objective Function for Treatment Planning of Solitary Brain Metastasis Using Intensity-modulated Radiosurgery Techniques

Purpose: The purpose of this study was to systematically examine the normal tissue objective (NTO) function by comparing its variations for planning solitary brain metastasis with intensity-modulated and volumetric-modulated arc radiosurgery techniques. Materials and Methods: Twenty-two cases were retrospectively planned with two NTO parameter sets named A and B using intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques. The Type A set used slope, k = 0.4 mm−1 plus end dose, De = 20%, whereas the Type B set used k = 1.0 mm−1 plus De = 10%. The resulting four plan types were assessed using mean dose to 5 mm exterior ring, normal brain receiving 12 Gy (V12), 5 Gy total brain dose volume (V5), gradient index (R50%), focal index (FI), Paddick conformity index (PCI), prescription isodose surface (PIDS), and MU/Gy. Results: Brain doses were significantly lower for VMAT than for IMRT. R50% was more favorable for VMAT than for IMRT for each planning target volume (PTV). The mean FI was comparable between the corresponding IMRT and VMAT plan types. PCI was better for the IMRT_A plan type. PIDS was significantly lower for Type B plans than Type A for both techniques. For PTVs &lt;3 cm3, IMRT plans showed poor dosimetry and required NTO settings stricter than Type B. Conclusions: The application of NTO variations demonstrated varied dosimetry for IMRT and VMAT techniques. The NTO parameter variations produced field size and/or beamlet size/shape variations. The strict NTO parameter set generated more conformal beam apertures to reduce the brain dose. VMAT plan types showed significantly lower brain doses and better dosimetry for all target sizes.

Experiencing In-Vivo Dosimetry Verification in Malaysia: A Patient-Specific EPID-Based Approach

Real-time dosimetry is crucial for patient-specific quality assurance in radiotherapy facilities across Europe, and the same demand extends to advanced countries like Malaysia. Our study aimed to assess the need for in vitro diagnostic (IVD) testing in Malaysia, exploring the use of Electronic Portal Imaging Device (EPID)-based dose verification to simplify complex methods. In this research, we assessed the accuracy of in-vivo dose reconstruction using EPIgrayTM (DOSIsoft, Cachan, France) for 20 cohorts of patients who underwent breast and prostate Intensity-Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT), and compared the results with the dose calculated by the Treatment Planning System (TPS). Initially, we set a default tolerance level of 7-10% for dose acceptance. Our study found that the majority of patients in the cohort met the 10% tolerance level when validated with EPIgrayTM in vivo dosimetry. Specifically, 12 out of 15 patients who underwent VMAT treatment were within this tolerance range. Additionally, for IMRT treatment, 4 out of 6 patients achieved the 10% tolerance level. These initial findings demonstrate the potential of EPIgrayTM as a valuable tool for verifying in vivo dosimetry for advanced treatment techniques. For VMAT plans, more than 85% of the points were in agreement with the 10% tolerance level for 11 out of 15 patients. For IMRT plans, more than 90% of the points met the set tolerance level for 4 out of 15 patients. By understanding the agreement and variability of EPID-based IVD, we can further refine the utilization of EPID-based IVD as a relevant tool in ensuring accurate and reliable dose delivery during radiotherapy treatments.

Dose conformity and falloff in single-lesion intracranial SRS with DCA and VMAT methods.

Intracranial stereotactic radiosurgery (SRS) aims at achieving highly conformal dose distribution and, at the same time, attaining rapid dose falloff outside the treatment target. SRS is performed using different techniques including dynamic conformal arcs (DCA) and volumetric modulated arc therapy (VMAT). In this study, we compare dose conformity and falloff in DCA and VMAT plans for SRS with a single target. To compare dose conformity in SRS plans, we employ a novel conformity index , RTOG conformity index ( ), and Riet-Paddick conformity index ( ). In addition, we use indices , , and to evaluate dose falloff. For each of the considered 118 cases of SRS, two plans were created using DCA and VMAT. A two-tailed Student's t-test was used to evaluate the difference between the employed indices for the DCA and VMAT plans. The studied VMAT plans were characterized by higher dose conformity than the DCA plans. The differences between the conformity indices for the DCA plans and VMAT plans were statistically significant. The DCA plans had a smaller number of monitor units (MUs) and smaller indices R50%, V10 Gy, and V12 Gy than the VMAT plans. However, the differences between R50%, V10 Gy, and V12 Gy for the DCA and VMAT plans were not statistically significant. Although the studied VMAT plans had higher dose conformity, they also had larger MUs than the DCA plans. In terms of dose falloff characterized by parameters R50%, V10 Gy, and V12 Gy, DCA serves as a reasonable alternative to VMAT in the case of a single brain metastasis.

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%/2mm), 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.

Short course irradiation in rectal cancer: Dosimetry study comparing 3D conformal radiotherapy versus intensity-modulated radiotherapy versus volumetric modulated arc therapy

The standard radiotherapy planning technique for locally advanced rectal cancer (LARC) was 3D Conformal Radiotherapy (3DCRT). Modern radiation delivery techniques, including Intensity-Modulated Radiotherapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) techniques, have been developed in recent years in terms of dose delivery to the target while minimizing the dose to the organ at risk (OAR). The objective of this study was to identify the most suitable treatment plan technique from the available options of 3DCRT, IMRT and VMAT for LARC using short course radiotherapy (SCRT). Therefore, twenty-five adult patients with LARC in the last five-year period (2017–2021) were involved in this study. For each patient, three radiotherapy treatment plans were generated for dosimetric comparison of short course irradiation (25 Gy/5 fractions, which is the new standard of care) using Varian Eclipse software to generate 3DCRT in comparison with IMRT and VMAT plans. The conformity index (CI), the homogeneity index (HI), and the organs at risk (OAR), including bladder, small bowel, and femoral heads, were compared between the three plans. The results revealed that VMAT and IMRT plans had a higher level of conformity for the planning target volume (PTV) with 0.996 and 0.994, respectively, compared to the 3DCRT planning of 0.947 (both p < 0.005). The PTV dose distribution was less homogeneous with 3DCRT than with VMAT or IMRT plans. VMAT offered superior homogeneity to IMRT and 3DCRT plans with 1.055, 1.066, and 1.089, respectively (p < 0.005). The findings showed that VMAT is superior to IMRT and 3DCRT in almost all parameters, including dose distribution conformity and homogeneity of the target volume, as well as providing better OAR sparing in LARC patients.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. 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%/2mm 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. 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. 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.

Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve.

CT Hounsfield Units (HUs) are converted to electron density using a calibration curve obtained from physical measurements of an electron density phantom. HU values assigned to an MRI-derived synthetic computed tomography (sCT) may present a different relationship with electron density compared to CT HU. Correct assignment of sCT HU values is critical for accurate dose calculation and delivery. The goals of this work were to develop a sCT calibration curve using patient data acquired on a clinically commissioned CT scanner and assess for CyberKnife- and volumetric modulated arc therapy (VMAT)-based MR-only treatment planning of prostate SBRT. Same-day CT and MRI simulation in the treatment position were performed on 10 patients treated with SBRT to the prostate. Dixon in-phase and out-of-phase MRIs were acquired on a 3T scanner using a 3D T1-weighted gradient-echo sequence to generate sCTs using a commercial sCT algorithm. CT and sCT datasets were co-registered and HU values compared using mean absolute error (MAE). An optimized HU-to-density calibration curve was created based on average HU values across an institutional patient database for each of the four sCT tissue types. Clinical CyberKnife and VMAT treatment plans were generated on each patient CT and recomputed onto corresponding sCTs. Dose distributions computed using CT and sCT were compared using gamma criteria and dose-volume-histograms. For the optimized calibration curve, HU values were -96, 37, 204, and 1170 and relative electron densities were 0.95, 1.04, 1.1, and 1.7 for adipose, soft tissue, inner bone, and outer bone, respectively. The proposed sCT protocol produced total MAE of 94±20HU. Gamma values mean±std (min-max) were 98.9%±0.9% (97.1%-100%) and 97.7%±1.3% (95.3%-99.3%) for VMAT and CyberKnife plans, respectively. MRI-derived sCT using the proposed approach shows excellent dosimetric agreement with conventional CT simulation, demonstrating the feasibility of MRI-derived sCT for prostate SBRT treatment planning.

Using dose volume histogram (DVH) predictions to improve the plan quality of helical tomotherapy (HT)

This study aimed to investigate whether the RapidPlan (RP) model configured by volumetric modulated arc therapy (VMAT) plans of nasopharyngeal carcinoma (NPC) could be used to assist in the optimization of HT plans and improve their quality.An RP model was trained using 100 clinically accepted VMAT plans of NPC patients. The predicted dose constraints of the VMAT trained RP model were used to reoptimize 25 consecutive clinically accepted HT plans (HT_clinical) and perform new VMAT plans based on the same computed topography (CT). The dosimetric quality of the reoptimized HT plans (HT_reoptimized), HT_clinical, and VMAT group were compared. The minimum dose encompassing 2% target (D2%), the minimum dose encompassing 98% target (D98%), homogeneity index (HI) and conformity index (CI) were similar for most targets between the HT_clinical and HT_reoptimized plans, although certain targets in the HT_reoptimized plans had higher D2% and HI and lower D98%. The HT_reoptimized plans outperformed the HT_clinical plans in the Dmax and D1cc of the spinal cord, V40Gy of the left temporal lobe, Dmean and V30Gy of the oral cavity, Dmean of the larynx and thyroid, and the differences were statistically significant. HT plans had higher CI and HI than VMAT plans. HT plans outperformed VMAT plans in the Dmax of the spinal cord and lenses, V30Gy of the oral cavity and parotids, and V40Gy of the temporal lobes, but underperformed in the Dmax and D1cc of the brainstem, D1cc of the spinal cord and Dmean of the oral cavity.The VMAT-based RP model can be used to assist in the planning of HT plans and improve the dosimetry quality of HT plans.

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.

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.

The delivery assessment for small targets on Halcyon radiotherapy system - Measured and calculated dose comparison.

With the ever-increasing requirements of accuracy and personalization of radiotherapy treatments, stereotactic radiotherapy (SRT) with volumetric modulated arc therapy (VMAT) on O-ring Halcyon radiotherapy system could potentially provide a fast, safe, and feasible treatmentoption. The purpose of this study was to assess the delivery of Halcyon VMAT plans for small targets. Well-defined VMAT-SRT plans were created on Halcyon radiotherapy system with the stacked and staggered dual-layer MLC design for the film measurement set-up and the target sizes and shapes designed to emulate the targets of the stereotactic treatments. The planar dose distributions were acquired with film measurements and compared to a current clinical reference dose calculation with AcurosXB (v18.0, Varian Medical Systems) and to Monte Carlo simulations. With the collapsed arc versions of the VMAT-SRT plans, the uncertainty in dose delivery due to the multileaf collimator (MLC) without the gantry rotation could be separated andanalyzed. The target size was mainly limited by the resolution originated from the design of the MLC leaves. The results of the collapsed arc versions of the plans show good consistency among measured, calculated, and simulated dose distributions. With the full VMAT plans, the agreement between calculated and simulated dose distributions was consistent with the collapsed arc versions. The measured dose distribution agreed with the calculated and simulated dose distributions within the target regions, but considerable local differences were observed in the margins of the target. The largest differences located in the steep gradient regions presumably originating from the deviation of the isocenter. The potential of the Halcyon radiotherapy system for VMAT-SRT delivery was evaluated and the study revealed valuable insights on the machine characteristics with thedelivery.