Guidance document on delivery, treatment planning, and clinical implementation of IMRT: report of the IMRT Subcommittee of the AAPM Radiation Therapy Committee

Impact of beam complexity on plan delivery accuracy verification of a transmission detector in volumetric modulated arc therapy

OBJECTIVE

To study the effect of beam complexity on VMAT delivery accuracy evaluated by means of a transmission detector, together with the possibility of scoring plan complexity.

METHODS

43 clinical VMAT plans delivered by a TrueBeam linear accelerator to both Delta4 Discover and Delta4 Phantom+ for patient-specific quality assurance were evaluated. Global Dose-γ analysis, MLC-γ analysis, percentage of leaves with a deviation between planned and measured leaf tip position lower than 1 mm (LD) were computed. Modulation complexity score (MCSv), average leaf travel (LT), a multiplicative combination of LT and MCSv (LTMCS), percentage of leaves with speed lower than 5 mm/s (LS), from 5 to 20 mm/s (MS), higher than 20 mm/s (HS) and the average value of leaf speed (MLCSav) were evaluated by means of an home-made Matlab script.

RESULTS

Dose-γ passing rate showed a moderate correlation with MCSv, LT, MLCSav, LS and HS, while a stronger positive correlation was found with LTMCS. A strong correlation was observed between LD and both LT and leaves speed, while a weak correlation was observed with MCSv. A correlation between MLC-γ pass rate and plan complexity parameters was found except for MCSv; a moderate correlation with LS was observed, while all other parameters showed weak correlations.

CONCLUSIONS

The study confirmed the possibility to establish correlations between plan complexity indices versus dose distribution and MLC parameters measured by a transmissive detector. Further investigation is necessary to define specific values of the complexity indices to evaluate whether a VMAT plan is deliverable as intended.

Dosimetric effects of small field size, dose grid size, and variable split-arc methods on gamma pass rates in radiation therapy

This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.

Error detection for radiotherapy planning validation based on deep learning networks

BACKGROUND

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks.

PURPOSE

The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations.

METHOD

We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators.

RESULTS

The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors.

CONCLUSION

When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

New optically stimulated luminescence dosimetry film optimized for energy dependence guided by Monte Carlo simulations

Optically stimulated luminescence (OSL) film dosimeters, based on BaFBr:Eu2+phosphor material, have major dosimetric advantages such as dose linearity, high spatial resolution, film re-usability, and immediate film readout. However, they exhibit an energy-dependent over-response at low photon energies because they are not made of tissue-equivalent materials. In this work, the OSL energy-dependent response was optimized by lowering the phosphor grain size and seeking an optimal choice of phosphor concentration and film thickness to achieve sufficient signal sensitivity. This optimization process combines measurement-based assessments of energy response in narrow x-ray beams with various energy response calculation methods applied to different film metrics. Theoretical approaches and MC dose simulations were used for homogeneous phosphor distributions and for isolated phosphor grains of different dimensions, where the dose in the phosphor grain was calculated. In total 8 OSL films were manufactured with different BaFBr:Eu2+median particle diameters (D50): 3.2μm, 1.5μm and 230 nm and different phosphor concentrations (1.6%, 5.3% and 21.3 %) and thicknesses (from 5.2 to 49μm). Films were irradiated in narrow x-ray spectra (N60, N80, N-150 and N-300) and the signal intensity relative to the nominal dose-to-water value was normalized to Co-60. Finally, we experimentally tested the response of several films in Varian 6MV TrueBeam STx linear accelerator using the following settings: 10 × 10 cm2field, 0deggantry angle, 90 cm SSD, 10 cm depth. The x-ray irradiation experiment reported a reduced energy response for the smallest grain size with an inverse correlation between response and grain size. The N-60 irradiation showed a 43% reduction in the energy over-response when going from 3μm to 230 nm grain size for the 5% phosphor concentration. Energy response calculation using a homogeneous dispersion of the phosphor underestimated the experimental response and was not able to obtain the experimental correlation between grain size and energy response. Isolated grain size modeling combined with MC dose simulations allowed to establish a good agreement with experimental data, and enabled steering the production of optimized OSL-films. The clinical 6 MV beam test confirmed a reduction in energy dependence, which is visible in small-grain films where a decrease in out-of-field over-response was observed.

Verifying institutionally developed hybrid 3D-printed coaxial cylindrical phantom for patient-specific quality assurance in stereotactic body radiation therapy of hepatocellular carcinoma

An accurate and reliable patient-specific quality assurance (PSQA) is crucial to ensure the safety and precision of Stereotactic body radiation therapy (SBRT) in treating Hepatocellular carcinoma (HCC). This study examines the effectiveness of a novel hybrid 3D-printed hybrid coaxial cylindrical phantom for PSQA in the SBRT of HCC. The study compared three different point dose verification techniques for PSQA: a traditional solid water phantom, two dimensional detector array I'MatriXX, and a newly developed hybrid 3D-printed phantom. Thirty SBRT HCC liver cases were examined using these techniques, and point doses were measured and compared to planned doses using the perpendicular composite method with solid water and I'MatriXX phantoms. Unlike the other two methods, the point dose was compared in true composite geometry using the hybrid 3D-printed phantom, which enhanced the accuracy and consistency of PSQA. The study aims to assess the statistical significance and accuracy of the hybrid 3D-printed phantom compared to other methods. The results showed all techniques complied with the institutional threshold criteria of within ± 3% for point-dose measurement discrepancies. The hybrid 3D-printed phantom was found to have better consistency with a lower standard deviation than traditional methods. Statistical analysis using Student's t-test revealed the statistical significance of the hybrid 3D-printed phantom technique in patient-specific point-dose assessments with a p-value < 0.01. The hybrid 3D-printed phantom developed institutionally is cost-effective and easy to handle. It has been proven to be a valuable tool for PSQA in SBRT for the treatment of HCC and has demonstrated its practicality and reliability.

Heavy-to-light electron transition enabling real-time spectra detection of charged particles by a biocompatible semiconductor

The current challenge of wearable/implantable personal dosimeters for medical diagnosis and radiotherapy applications is lack of suitable detector materials possessing both excellent detection performance and biocompatibility. Here, we report a solution-grown biocompatible organic single crystalline semiconductor (OSCS), 4-Hydroxyphenylacetic acid (4HPA), achieving real-time spectral detection of charged particles with single-particle sensitivity. Along in-plane direction, two-dimensional anisotropic 4HPA exhibits a large electron drift velocity of 5 × 105 cm s-1 at "radiation-mode" while maintaining a high resistivity of (1.28 ± 0.003) × 1012 Ω·cm at "dark-mode" due to influence of dense π-π overlaps and high-energy L1 level. Therefore, 4HPA detectors exhibit the record spectra detection of charged particles among their organic counterparts, with energy resolution of 36%, (μt)e of (4.91 ± 0.07) × 10-5 cm2 V-1, and detection time down to 3 ms. These detectors also show high X-ray detection sensitivity of 16,612 μC Gyabs-1 cm-3, detection of limit of 20 nGyair s-1, and long-term stability after 690 Gyair irradiation.

Commissioning of Mobius3D/FX for patient-specific quality assurance: The CUIMC-NewYork Presbyterian Hospital experience

PURPOSE

To present the process undertaken by our institute in commissioning Mobius3D (M3D) for patient-specific quality assurance.

METHOD

168 plans were randomly selected to compare dose distribution measured with ArcCheck and dose calculated from M3D, both compared against the treatment planning system (TPS). The gamma criteria for measurement and M3D are 3%/2 mm with 10% and 50% dose thresholds, respectively. The effect of tissue inhomogeneity was investigated on 11 plans by recalculating the dose in M3D on a homogeneous phantom. Tolerance and action limits were established following the AAPM Task Group 218 recommendations.

RESULTS

The M3D showed high variability in gamma passing rate compared to the measurement. Twenty-three out of 168 plans had false negative dose comparisons. These plans fall under high tissue inhomogeneity like lung and metal implants, small field targets, and breast plans planned with high energy. One false negative case (0.6%) was observed. A single tolerance limit of 91% and 92% gamma passing rate for the M3D and measurement-based PSQA were established, respectively. Against the expectation, recalculating plans on the homogeneous phantom in M3D did not necessarily increase the gamma passing rate. These plans have a duty cycle >4.2, and the small field sizes combined with differences in slice thickness contributed to observed dose differences in the homogeneous phantom comparisons.

CONCLUSION

Following the commissioning, M3D is adopted in our institute. Currently, the gamma criteria used for measurement and M3D are 3%/2 mm, 40% dose threshold, with gamma passing rates of 92% and 95%, respectively. A higher passing rate for M3D is adopted until more data is available. The combined effect of plan modulation, the field sizes, the tissue inhomogeneity, the dose algorithm, and the volume averaging effect from differences in slice thickness can contribute to the differences in dose in M3D and TPS.

Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction

Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

Is it still necessary to perform measured based pre-treatment patient-specific QA for SRS HyperArc treatments?

PURPOSE

The Mobius3D system was validated as a modern secondary check dosimetry system. In particular, our objective has been to assess the suitability of the M3D as pre-treatment patient-specific Quality Assurance (QA) tool for Stereotactic Radiosurgery (SRS) HyperArc (HA) treatments. We aimed to determine whether Mobius3D could safely replace the measurements-based patient-specific QA for this type of treatment.

METHODS

30 SRS HA treatment plans for brain were selected. The dose distributions, calculated by Mobius and our routinely used algorithm (AcurosXB v.15.6), were compared using gamma analysis index and DVH parameters based on the patient's CT dataset. All 30 plans were then delivered across the ionization chamber in a homogeneous phantom and the measured dose was compared with both M3D and TPS calculated one. The plans were delivered and verified in terms of PSQA using the electronic portal imaging device (EPID) with Portal Dosimetry (PD) and myQA SRS (IBA Dosimetry) detector. Plans that achieved a global gamma passing rate (GPR) ≥ 97% based on 2%/2 mm criteria, with both Mobius3D and the conventional methods were evaluated acceptable. Finally, we assessed the capability of the M3D system to detect errors related to the position of the Multi-Leaf Collimator (MLC) in comparison to the analyzed measurement-based systems.

RESULTS

No relevant differences were observed in the comparison between the dose calculated on the CT-dataset by M3D and the TPS. Observed discrepancies are imputable to different used algorithms, but no discrepancies related to goodness of plans have been found. Average differences between calculated (M3D and TPS) vs measured dose with ionization chamber were 2.5% (from 0.41% to 3.2%) and 1.81% (from 0.66% to 2.65%), for M3D and TPS, respectively. All plans passed with a gamma passing rate > 97% using conventional PSQA methods with a gamma criterion of 2% dose difference and 2 mm distance-to-agreement. The average gamma passing rate for the M3D system was determined to be 99.4% (from 97.3% to 100%). Results from this study also demonstrated Mobius has better error detectability than conventional measurement-based systems.

CONCLUSION

Our study shows Mobius3D could be a suitable alternative to conventional measured based QA methods for SRS HyperArc treatments.

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.

Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy

BACKGROUND

Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied.

PURPOSE

This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning.

METHODS

A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2).

RESULTS

The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47 Gy for bladder D50% ; 0.62 Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p < 0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7 Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7 Gy), rectum (1.0 Gy), and small intestine (0.6 Gy), while maintaining  PTV homogeneity and conformity.

CONCLUSION

FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.

Simulation of consequences of using nonideal detectors during beam data commissioning measurements

BACKGROUND

Beam data commissioning is a core task of radiotherapy physicists. Despite multiple detectors available, a feasible measurement program compromises between detector properties and time constraints. Therefore, it is important to understand how nonideal measurement data propagates into patient dose calculation.

PURPOSE

We simulated the effects of realistic errors, due to beam commissioning with presumably nonoptimal detectors, on the resulting patient dose distributions. Additionally, the detectability of such beam commissioning errors during patient plan quality assurance (QA) was evaluated.

METHODS

A clinically used beam model was re-commissioned introducing changes to depth dose curves, output factors, profiles or combinations of those. Seventeen altered beam models with incremental changes of the modelling parameters were created to analyze dose changes on simplified anatomical phantoms. Additionally, fourteen altered models incorporate changes in the order of signal differences reported for typically used detectors. Eighteen treatment plans of different types were recalculated on patient CT data sets using the altered beam models.

RESULTS

For the majority of clinical plans, dose distributions in the target volume recalculated on the patient computed tomography data were similar between the original and the modified beam models, yielding global 2%/2 mm gamma pass rates above 98.9%. Larger changes were observed for certain combinations of beam modelling errors and anatomical sites, most extreme for output factor changes in a small target volume plan with a pass rate of 80.6%. Modelling an enlarged penumbra as if measured with a 0.125 cm3 ion chamber had the largest effect on the dose distribution (average pass rate of 96.5%, lowest 85.4%). On different QA phantom geometries, dose distributions between calculations with modified and unmodified models typically changed too little to be detected in actual measurements.

CONCLUSION

While the simulated errors during beam modelling had little effect on most plans, in some cases changes were considerable. High-quality penumbra and small field output factor should be a main focus of commissioning measurements. Detecting modelling issues using standard patient QA phantoms is unlikely. Verification of a beam model should be performed especially for plans with high modulation and in different depths or geometries representing the variety of situations expected clinically.

Pretreatment patient-specific quality assurance prediction based on 1D complexity metrics and 3D planning dose: classification, gamma passing rates, and DVH metrics

PURPOSE

Highly modulated radiotherapy plans aim to achieve target conformality and spare organs at risk, but the high complexity of the plan may increase the uncertainty of treatment. Thus, patient-specific quality assurance (PSQA) plays a crucial role in ensuring treatment accuracy and providing clinical guidance. This study aims to propose a prediction model based on complexity metrics and patient planning dose for PSQA results.

MATERIALS AND METHODS

Planning dose, measurement-based reconstructed dose and plan complexity metrics of the 687 radiotherapy plans of patients treated in our institution were collected for model establishing. Global gamma passing rate (GPR, 3%/2mm,10% threshold) of 90% was used as QA criterion. Neural architecture models based on Swin-transformer were adapted to process 3D dose and incorporate 1D metrics to predict QA results. The dataset was divided into training (447), validation (90), and testing (150) sets. Evaluation of predictions was performed using mean absolute error (MAE) for GPR, planning target volume (PTV) HI and PTV CI, mean absolute percentage error (MAPE) for PTV D95, PTV D2 and PTV Dmean, and the area under the receiver operating characteristic (ROC) curve (AUC) for classification. Furthermore, we also compare the prediction results with other models based on either only 1D or 3D inputs.

RESULTS

In this dataset, 72.8% (500/687) plans passed the pretreatment QA under the criterion. On the testing set, our model achieves the highest performance, with the 1D model slightly surpassing the 3D model. The performance results are as follows (combine, 1D, and 3D transformer): The AUCs are 0.92, 0.88 and 0.86 for QA classification. The MAEs of prediction are 0.039, 0.046, and 0.040 for 3D GPR, 0.018, 0.021, and 0.019 for PTV HI, and 0.075, 0.078, and 0.084 for PTV CI. Specifically, for cases with 3D GPRs greater than 90%, the MAE could achieve 0.020 (combine). The MAPE of prediction is 1.23%, 1.52%, and 1.66% for PTV D95, 2.36%, 2.67%, and 2.45% for PTV D2, and 1.46%, 1.70%, and 1.71% for PTV Dmean.

CONCLUSION

The model based on 1D complexity metrics and 3D planning dose could predict pretreatment PSQA results with high accuracy and the complexity metrics play a leading role in the model. Furthermore, dose-volume metric deviations of PTV could be predicted and more clinically valuable information could be provided.

Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation

Objective. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy. Secondly, these are converted in multi-leaf collimator (MLC) apertures and monitor units (MUs) using a generic optimisation to obtain the final treatment plan. In contrast, we present a method to predict volumetric modulated arc therapy (VMAT) MLC apertures and MUs directly from patient anatomy using deep learning. The predicted plan is then provided as initialisation to the optimiser for fine-tuning.Approach. 148 patients (training: 101; validation: 23; test: 24), treated for right breast cancer, are replanned to obtain a homogeneous database of 3-arc VMAT plans (PTVBreast: 45.57 Gy; PTVBoost: 55.86 Gy) according to the clinical protocol, using RapidPlanTMwith automatic optimisation and extended convergence mode (clinical workflow). Projections of the CT and contours are created along the beam's eye view of all control points and given as input to a U-net type convolutional neural networks (CNN). The output are the MLC aperture and MU for all control points, from which a DICOM RTplan is built. This is imported and further optimised in the treatment planning system using automatic optimisation without convergence mode, with clinical PTV objectives and organs-at-risk (OAR) objectives based on the DVHs calculated from the imported plan (CNN workflow).Main results. Mean dose differences between the clinical and CNN workflow over the test set are 0.2 ± 0.5 Gy atD95%and 0.6 ± 0.4 Gy atD0.035ccof PTVBreastand -0.4 ± 0.3 Gy atD95%and 0.7 ± 0.3 Gy atD0.035ccof PTVBoost. For the OAR, they are -0.2 ± 0.2 Gy forDmean,heartand 0.04 ± 0.8 Gy forDmean,ipsilateral lung. The mean computation time is 60 and 25 min respectively.Significance. VMAT optimisation can be initialised by MLC apertures and MUs, directly predicted from patient anatomy using a CNN, reducing planning time with more than half while maintaining clinically acceptable plans. This procedure puts the planner in a supervising role over an AI-based treatment planning workflow.

Sensitivity and specificity of Monte Carlo based independent secondary dose computation for detecting modulation-related dose errors in intensity modulated radiotherapy

BACKGROUND

The recent availability of Monte Carlo based independent secondary dose calculation (ISDC) for patient-specific quality assurance (QA) of modulated radiotherapy requires the definition of appropriate, more sensitive action levels, since contemporary recommendations were defined for less accurate ISDC dose algorithms.

PURPOSE

The objective is to establish an optimum action level and measure the efficacy of a Monte Carlo ISDC software for pre-treatment QA of intensity modulated radiotherapy treatments.

METHODS

The treatment planning system and the ISDC were commissioned by their vendors from independent base data sets, replicating a typical real-world scenario. In order to apply Receiver-Operator-Characteristics (ROC), a set of treatment plans for various case classes was created that consisted of 190 clinical treatment plans and 190 manipulated treatment plans with dose errors in the range of 1.5-2.5%. All 380 treatment plans were evaluated with ISDC in the patient geometry. ROC analysis was performed for a number of Gamma (dose-difference/distance-to-agreement) criteria. QA methods were ranked according to Area under the ROC curve (AUC) and optimum action levels were derived via Youden's J statistics.

RESULTS

Overall, for original treatment plans, the mean Gamma pass rate (GPR) for Gamma(1%, 1 mm) was close to 90%, although with some variation across case classes. The best QA criterion was Gamma(2%, 1 mm) with GPR > 90% and an AUC of 0.928. Gamma criteria with small distance-to-agreement had consistently higher AUC. GPR of original treatment plans depended on their modulation degree. An action level in terms of Gamma(1%, 1 mm) GPR that decreases with modulation degree was the most efficient criterion with sensitivity = 0.91 and specificity = 0.95, compared with Gamma(3%, 3 mm) GPR > 99%, sensitivity = 0.73 and specificity = 0.91 as a commonly used action level.

CONCLUSIONS

ISDC with Monte Carlo proves highly efficient to catch errors in the treatment planning process. For a Monte Carlo based TPS, dose-difference criteria of 2% or less, and distance-to-agreement criteria of 1 mm, achieve the largest AUC in ROC analysis.

TransQA: deep hybrid transformer network for measurement-guided volumetric dose prediction of pre-treatment patient-specific quality assurance

Objective. Performing pre-treatment patient-specific quality assurance (prePSQA) is considered an essential, time-consuming, and resource-intensive task for volumetric modulated arc radiotherapy (VMAT) which confirms the dose accuracy and ensure patient safety. Most current machine learning and deep learning approaches stack excessive convolutional/pooling operations (CPs) to predict prePSQA with two-dimensional or one-dimensional information input. However, these models generally present limitations in explicitly modeling long-range dependency for volumetric dose prediction due to the loss of spatial dose features and the inherent locality of CPs. The purpose of this work is to construct a deep hybrid network by combining the self-attention mechanism-based Transformer with modified U-Net for predicting measurement-guided volumetric dose (MDose) of prePSQA.Approach. The enrolled 307 cancer patients underwent VMAT were randomly divided into 246 and 61 cases for training and testing the model. The input data included computed tomography images, radiotherapy dose images exported from the treatment planning system, as well as the MDose distribution from the verification system. The output was the predicted high-quality voxel-wise prePSQA dose distribution.Main results: qualitative and quantitative experimental results show that the proposed prediction method could achieve comparable or better performance on MDose prediction over other approaches in terms of spatial dose distribution, dose-volume histogram metrics, gamma passing rates, mean absolute error, root mean square error, and structural similarity.Significance. The preliminary results on multiple cancer sites show that our approach can be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.

A real-time contouring feedback tool for consensus-based contour training

Purpose

Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring.

Methods

We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group.

Results

For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV.

Conclusions

A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

IPEM topical report: guidance for the clinical implementation of online treatment monitoring solutions for IMRT/VMAT

This report provides guidance for the implementation of online treatment monitoring (OTM) solutions in radiotherapy (RT), with a focus on modulated treatments. Support is provided covering the implementation process, from identification of an OTM solution to local implementation strategy. Guidance has been developed by a RT special interest group (RTSIG) working party (WP) on behalf of the Institute of Physics and Engineering in Medicine (IPEM). Recommendations within the report are derived from the experience of the WP members (in consultation with manufacturers, vendors and user groups), existing guidance or legislation and a UK survey conducted in 2020 (Stevenset al2021). OTM is an inclusive term representing any system capable of providing a direct or inferred measurement of the delivered dose to a RT patient. Information on each type of OTM is provided but, commensurate with UK demand, guidance is largely influenced byin vivodosimetry methods utilising the electronic portal imager device (EPID). Sections are included on the choice of OTM solutions, acceptance and commissioning methods with recommendations on routine quality control, analytical methods and tolerance setting, clinical introduction and staffing/resource requirements. The guidance aims to give a practical solution to sensitivity and specificity testing. Functionality is provided for the user to introduce known errors into treatment plans for local testing. Receiver operating characteristic analysis is discussed as a tool to performance assess OTM systems. OTM solutions can help verify the correct delivery of radiotherapy treatment. Furthermore, modern systems are increasingly capable of providing clinical decision-making information which can impact the course of a patient's treatment. However, technical limitations persist. It is not within the scope of this guidance to critique each available solution, but the user is encouraged to carefully consider workflow and engage with manufacturers in resolving compatibility issues.

A structured FMEA approach to optimizing combinations of plan-specific quality assurance techniques for IMRT and VMAT QA

BACKGROUND

Many commercial tools are available for plan-specific quality assurance (QA) of radiotherapy plans, with their functionality assessed in isolation. However, multiple QA tools are required to review the full range of potential errors. It is important to assess their effectiveness in combination with each other to look for ways to both streamline the QA process and to make certain that errors of high impact and/or high occurrence are caught before reaching patient treatment.

PURPOSE

To develop a structured method to assess the effective risk reduction of combinations of QA methods for IMRT/VMAT treatments.

METHODS

First, a structured prospective risk assessment was performed to establish the major failure modes (FMs) of IMRT/VMAT QA, and assign occurrence (O), severity (S), and baseline detectability (BD) rankings to them. The baseline assumed that chart checks and linear accelerator QA was performed, but no plan-specific secondary dose calculation or measurement was done. Second, the detectability of each FM for two secondary dose calculation methods and four plan measurement methods (point-based dose calculation, Monte-Carlo-based dose calculation, 2D fluence-based measurement, 2.5D phantom-based measurement, log file analysis with dose recalculation, and log file analysis combined with MLC QA) was determined. Third, we used a minimum detectability approach in addition to each FM's occurrence and severity to determine the optimal combination of QA methods. We analyzed the cumulative risk priority number of eight combinations of QA methods. The analysis was done on (1) all FMs, (2) FMs with high severity, (3) FMs with high-risk priority numbers (RPN) of O*S*BD, and (4) on FMs with both high severity and high RPN.

RESULTS

Our analysis resulted in 54 FMs, including commissioning, planning, data transfer, and linear accelerator failures. 1D secondary dose calculation plus measurement provided a 19%-22% risk reduction from baseline. 1D/3D secondary dose calculation plus log files created a 25%-32% reduction. 3D secondary dose calculation plus measurement resulted in a 27%-34% reduction. 3D secondary dose calculation plus log files with additional machine QA provided the greatest reduction of 31%-42%.

CONCLUSION

This novel structured approach to comparing combinations of QA methods will allow us to optimize our procedures, with the goal of detecting all clinically significant FMs. Our results show that log-file QA with 3D dose recalculation and supplemental machine QA provides better risk reduction than measurement-based QA. This work builds evidence to justify reducing or eliminating measurement-based PSQA with an independent 3D dose verification, log-file measurement, and appropriate supplementation of machine QA. The process also highlights FMs that cannot be caught by pre-treatment QA, prompting us to consider future directions for on-treatment QA.

Characterization and commissioning of a new collaborative multi-modality radiotherapy platform

TaiChi, a new multi-modality radiotherapy platform that integrates a linear accelerator, a focusing gamma system, and a kV imaging system within an enclosed O-ring gantry, was introduced into clinical application. This work aims to assess the technological characteristics and commissioning results of the TaiChi platform. The acceptance testing and commissioning were performed following the manufacturer's customer acceptance tests (CAT) and several AAPM Task Group (TG) reports/guidelines. Regarding the linear accelerator (linac), all applicable validation measurements recommended by the MPPG 5.a (basic photon beam model validation, intensity-modulated radiotherapy (IMRT)/volumetric-modulated arc therapy (VMAT) validation, end-to-end(E2E) tests, and patient-specific quality assurance (QA)) were performed. For the focusing gamma system, the absorbed doses were measured using a PTW31014 ion chamber (IC) and PTW60016 diode detector. EBT3 films and a PTW60016 diode detector were employed to measure the relative output factors (ROFs). The E2E tests were performed using PTW31014 IC and EBT3 films. The coincidences between the imaging isocenter and the linac/gamma mechanical isocenter were investigated using EBT3 films. The image quality was evaluated regarding the contrast-to-noise ratio (CNR), spatial resolution, and uniformity. All tests included in the CAT met the manufacturer's specifications. All MPPG 5.a measurements complied with the tolerances. The confidence limits for IMRT/VMAT point dose and dose distribution measurements were achieved according to TG-119. The point dose differences were below 1.68% and gamma passing rates (3%/2 mm) were above 95.1% for the linac E2E tests. All plans of patient-specific QA had point dose differences below 1.79% and gamma passing rates above 96.1% using the 3%/2 mm criterion suggested by TG-218. For the focusing gamma system, the differences between the calculated and measured absorbed doses were below 1.86%. The ROFs calculated by the TPS were independently confirmed within 2% using EBT3 films and a PTW60016 detector. The point dose differences were below 2.57% and gamma passing rates were above 95.3% using the 2%/1 mm criterion for the E2E tests. The coincidences between the imaging isocenter and the linac/gamma mechanical isocenter were within 0.5 mm. The image quality parameters fully complied with the manufacturer's specifications regarding the CNR, spatial resolution, and uniformity. The multi-modality radiotherapy platform complies with the CAT and AAPM commissioning criteria. The commissioning results demonstrate that this platform performs well in mechanical and dosimetry accuracy.

The Current Trend of Radiation Therapy for Patients with Localized Prostate Cancer

A recent approach to radiotherapy for prostate cancer is the administration of high doses of radiation to the prostate while minimizing the risk of side effects. Thus, image-guided radiotherapy utilizes advanced imaging techniques and is a feasible strategy for increasing the radiation dose. New radioactive particles are another approach to achieving high doses and safe procedures. Prostate brachytherapy is currently considered as a combination therapy. Spacers are useful to protect adjacent organs, specifically the rectum, from excessive radiation exposure.

Real-time, volumetric imaging of radiation dose delivery deep into the liver during cancer treatment

Ionizing radiation acoustic imaging (iRAI) allows online monitoring of radiation's interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We describe an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The method relies on a two-dimensional matrix array transducer and a matching multi-channel preamplifier board. The feasibility of imaging temporal 3D dose accumulation was first validated in a tissue-mimicking phantom. Next, semiquantitative iRAI relative dose measurements were verified in vivo in a rabbit model. Finally, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment.

Fully automated segmentally boosted VMAT

PURPOSE

Treatment planning for volumetric modulated arc therapy (VMAT) typically involves the use of multiple arcs to achieve sufficient intensity modulation. Alternatively, we can perform segment boosting to achieve similar intensity modulation while also reducing the number of control points used. Here, we propose the MetaPlanner Boosted VMAT (MPBV) approach, which generates boosted VMAT plans through a fully automated framework.

METHODS

The proposed MPBV approach is an open-source framework that consists of three main stages: meta-optimization of treatment plan hyperparameters, fast beam angle optimization on a coarse dose grid to select desirable segments for boosting, and final plan generation (i.e., constructing the boosted VMAT arc and performing optimization).

RESULTS

Performance for the MPBV approach is evaluated on 21 prostate cases and 6 head and neck cases using clinically relevant plan quality metrics (i.e., target coverage, dose conformity, dose homogeneity, and OAR sparing). As compared to two baseline methods with multiple arcs, MPBV maintains or improves dosimetric performance for the evaluated metrics while substantially reducing average estimated delivery times (from 2.6 to 2.1 min).

CONCLUSION

Our proposed MPBV approach provides an automated framework for producing high-quality VMAT plans that uses fewer control points and reduces delivery time as compared to traditional approaches with multiple arcs. MPBV applies automated treatment planning to segmentally boosted VMAT to address the beam utilization inefficiencies of traditional VMAT approaches that use multiple full arcs.

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.

Advances in Radiation Therapy for Primary Liver Cancer

During the past 30 years, several advances have been made allowing for safer and more effective treatment of patients with liver cancer. This report reviews recent advances in radiation therapy for primary liver cancers including hepatocellular carcinoma and intrahepatic cholangiocarcinoma. First, studies focusing on liver stereotactic body radiation therapy (SBRT) are reviewed focusing on lessons learned and knowledge gained from early pioneering trials. Then, new technologies to enhance SBRT treatments are explored including adaptive therapy and MRI-guided and biology-guided radiation therapy. Finally, treatment with Y-90 transarterial radioembolization is reviewed with a focus on novel approaches focused on personalized therapy.

Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance

OBJECTIVE

To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML).

METHODS

In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose image prediction (PDIP). Besides, 14 image-based features were extracted from RDD and EDD of portal dose predictions to obtain the feature vectors. In addition, a random forest was adopted for the multiclass classification task, and regression prediction for error magnitude.

RESULTS

The top five features extracted with the highest weight included 1) the relative displacement in the x direction, 2) the ratio of the absolute minimum residual error to the maximal RDD value, 3) the product of the maximum and minimum residuals, 4) the ratio of the absolute maximum residual error to the maximal RDD value, and 5) the ratio of the absolute mean residual value to the maximal RDD value. The relative displacement in the x direction had the highest weight. The overall accuracy of the five-class classification model was 99.85% for the validation set and 99.30% for the testing set. This model could be applied to the classification of the error-free plan, COLL, MU, MLCS, and MLCR with an accuracy of 100%, 98.4%, 99.9%, 98.0%, and 98.3%, respectively. MLCR had the worst performance in error magnitude prediction (70.1-96.6%), while others had better performance in error magnitude prediction (higher than 93%). In the error magnitude prediction, the mean absolute error (MAE) between predicted error magnitude and actual error ranged from 0.03 to 0.33, with the root mean squared error (RMSE) varying from 0.17 to 0.56 for the validation set. The MAE and RMSE ranged from 0.03 to 0.50 and 0.44 to 0.59 for the test set, respectively.

CONCLUSION

It could be demonstrated in this study that the image-based features extracted from RDD and EDD can be employed to identify different types of delivery errors and accurately predict error magnitude with the assistance of ML techniques. They can be used to associate traditional gamma analysis with clinically based analysis for error classification and magnitude prediction in patient-specific IMRT quality assurance.

Adaptive Volumetric-Modulated Arc Radiation Therapy for Head and Neck Cancer: Evaluation of Benefit on Target Coverage and Sparing of Organs at Risk

Background: Radiotherapy is essential in the management of head–neck cancer. During the course of radiotherapy, patients may develop significant anatomical changes. Re-planning with adaptive radiotherapy may ensure adequate dose coverage and sparing of organs at risk. We investigated the consequences of adaptive radiotherapy on head–neck cancer patients treated with volumetric-modulated arc radiation therapy compared to simulated non-adaptive plans: Materials and methods: We included in this retrospective dosimetric analysis 56 patients treated with adaptive radiotherapy. The primary aim of the study was to analyze the dosimetric differences with and without an adaptive approach for targets and organs at risk, particularly the spinal cord, parotid glands, oral cavity and larynx. The original plan (OPLAN) was compared to the adaptive plan (APLAN) and to a simulated non-adaptive dosimetric plan (DPLAN). Results: The non-adaptive DPLAN, when compared to OPLAN, showed an increased dose to all organs at risk. Spinal cord D2 increased from 27.91 (21.06–31.76) Gy to 31.39 (27.66–38.79) Gy (p = 0.00). V15, V30 and V45 of the DPLAN vs. the OPLAN increased by 20.6% (p = 0.00), 14.78% (p = 0.00) and 15.55% (p = 0.00) for right parotid; and 16.25% (p = 0.00), 18.7% (p = 0.00) and 20.19% (p = 0.00) for left parotid. A difference of 36.95% was observed in the oral cavity V40 (p = 0.00). Dose coverage was significantly reduced for both CTV (97.90% vs. 99.96%; p = 0.00) and PTV (94.70% vs. 98.72%; p = 0.00). The APLAN compared to the OPLAN had similar values for all organs at risk. Conclusions: The adaptive strategy with re-planning is able to avoid an increase in dose to organs at risk and better target coverage in head–neck cancer patients, with potential benefits in terms of side effects and disease control.

Correlation between patient-specific quality assurance in volumetric modulated arc therapy and 2D dose image features

In radiotherapy, air-filled ion chamber detectors are ubiquitously used in routine dose measurements for treatment planning. However, its use has been restricted by intrinsic low spatial resolution barriers. We developed one procedure for patient-specific quality assurance (QA) in arc radiotherapy by coalescing two adjacent measurement images into a single image to improve spatial resolution and sampling frequency, and investigated how different spatial resolutions affect the QA results. PTW 729 and 1500 ion chamber detectors were used for dosimetric verification via coalescing two measurements with 5 mm-couch shift and the isocenter, and only isocenter measurement, which we call coalescence and standard acquisition (SA). Statistical process control (SPC), process capability analysis (PCA), and receiver operating characteristic (ROC) curve were used to compare the performance of the two procedures in determining tolerance levels and identifying clinically relevant errors. By analyzing 1256 γ values calculated on interpolated data points, our results indicated that detector 1500 showed higher averages in coalescence cohorts at different tolerance criteria and the dispersion degrees were spread out smaller. Detector 729 yielded a slightly lower process capability of 0.79, 0.76, 1.10, and 1.34, but detector 1500 exhibited somewhat different results of 0.94, 1.42, 1.19, and 1.60 in magnitude. The results of SPC individual control chart showed that cases in coalescence cohorts with γ values lowering its lower control limit (LCL) were greater than those in SA cohorts for detector 1500. A combination of the width of multi-leaf collimator (MLC) leaf, the cross-sectional area of the single detector, and the spacing between adjacent detectors might lead to discrepancies in percent γ values across diverse spatial resolution scenarios. The accuracy of reconstructed volume dose is mainly determined by the interpolation algorithm used in dosimetric systems. The magnitude of filling factor in the ion chamber detectors determined its ability to detect dose deviations. SPC and PCA results indicated that coalescence procedure could detect more potential failure QA results than SA while enhancing action thresholds.

An effective and optimized patient-specific QA workload reduction for VMAT plans after MLC-modelling optimization

INTRODUCTION

Many complexity metrics characterize modulated plans. First, this study aimed at identify the optimal complexity metrics to reduce workload associated to patient-specific quality assurance (PSQA) for our equipment and processes. Second, it intended to optimize our MLC modelling to improve measurement and calculation agreement with expectation of further reducing PSQA workload.

METHODS

Correlation and sensitivity at specificity equals to 1 were evaluated for PSQA results and different complexity metrics. Thresholds to stop PSQA were determined. After validation of the optimal complexity metric and threshold for our equipment and process, the MLC modelling was reviewed with a recently published methodology. This method is based on measurements with a Farmer-type ionization chamber of synchronous and asynchronous sweeping gap plans. Effect on the PSQA results and the identified threshold was investigated.

RESULTS

In our center, the most appropriate complexity metric for reducing our PSQA workload was the Modulation Complexity Score for VMAT (MCSv). The optimization of the MLC modelling significantly reduced the number of controlled plans, specifically for one of our two Varian Clinac. Any plan with a MCSv >= 0.34 is treated without PSQA.

CONCLUSION

This study rationalized and reduced our PSQA workload by approximately 30%. It is a continuing work with new TPS, machine or PSQA equipment. It encourages centers to re-evaluate their MLC modelling as well as assess the benefit of complexity metrics to streamline their PSQA workflow. An easier access, at least for reporting, at best for optimizing plans, into the TPS would be beneficial for the community.

Hypofractionation and SABR: 25 years of evolution in medical physics and a glimpse of the future

As we were invited to write an article for celebrating the 50^th Anniversary of Medical Physics journal, on something historically significant, commemorative, and exciting happening in the past decades, the first idea came to our mind is the fascinating radiotherapy paradigm shift from conventional fractionation to hypofractionation and stereotactic ablative radiotherapy (SABR). It is historically and clinically significant since as we all know this RT treatment revolution not only reduces treatment duration for patients, but also improves tumor control and cancer treatment outcomes. It is also commemorative and exciting for us medical physicists since the technology development in medical physics has been the main driver for the success of this treatment regimen which requires high precision and accuracy throughout the entire treatment planning and delivery. This article provides an overview of the technological development and clinical trials evolvement in the past 25 years for hypofractionation and SABR, with an outlook to the future improvement.

Research progress and application of artificial intelligence in thyroid associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO) is a complicated orbitopathy related to dysthyroid, which severely destroys the facial appearance and life quality without medical interference. The diagnosis and management of thyroid-associated ophthalmopathy are extremely intricate, as the number of professional ophthalmologists is limited and inadequate compared with the number of patients. Nowadays, medical applications based on artificial intelligence (AI) algorithms have been developed, which have proved effective in screening many chronic eye diseases. The advanced characteristics of automated artificial intelligence devices, such as rapidity, portability, and multi-platform compatibility, have led to significant progress in the early diagnosis and elaborate evaluation of these diseases in clinic. This study aimed to provide an overview of recent artificial intelligence applications in clinical diagnosis, activity and severity grading, and prediction of therapeutic outcomes in thyroid-associated ophthalmopathy. It also discussed the current challenges and future prospects of the development of artificial intelligence applications in treating thyroid-associated ophthalmopathy.

Rotational Set Up Uncertainly in Non-6D Couch and its Effects in Clinical Target Volume- Planning Target Volume Margin Calculation for Different Sites

Purpose

The purpose of this study was to estimate and incorporate rotational error to translational error for clinical target volume (CTV) to planning target volume (PTV) margin calculations for non-6D couch.

Materials and Methods

The study involved cone-beam computed tomography (CBCT) images of the patients who already had treatment in Varian Trilogy Clinac. The different sites studied were brain (70 patients, 406 CBCT images), head and neck (72 patients, 356 CBCT images), pelvis (83 patients, 606 CBCT images), and breast (45 patients, 163 CBCT images). Rotational and translational patient shifts were measured with the help of Varian eclipse offline review. The rotational shift introduces translational shift as it resolved along craniocaudal and mediolateral directions. Both rotational and translational error follow normal distribution and their respective errors were used to calculate CTV-PTV margin using van Herk model.

Results

Rotational effect on CTV-PTV margin contribution increases with increase in size of CTV. It also increases with increase in distance between center of mass of CTV and isocenter. These margins were more pronounce in single isocenter supraclavicular fossa-Tangential Breast plans.

Conclusions

There is always rotational error in all sites and it causes shift and rotation of the target. Rotational contribution to the CTV-PTV margin depends upon geometric center of CTV and isocenter distance and also on size of CTV. CTV-PTV margins should incorporate rotational error along with transitional error.

Predicting the complexity of head-and-neck volumetric-modulated arc therapy planning using a radiation therapy planning quality assurance software

Background/Aim

The more complex the treatment plan, the higher the possibility of errors in dose verification. Recently, a treatment planning quality assurance (QA) software (PlanIQ) with a function to objectively evaluate the quality of volumetric-modulated arc therapy (VMAT) treatment plans by scoring and calculating the ideal dose-volume histogram has been marketed. This study aimed to assess the association between the scores of ideal treatment plans identified using PlanIQ and the results of dose verification and to investigate whether the results of dose verification can be predicted based on the complexity of treatment plans.

Materials and methods

Dose verification was performed using an ionization chamber dosimeter, a radiochromic film, and a three-dimensional dose verification system, Delta4 PT. Correlations between the ideal treatment plan scores obtained by PlanIQ and the results of the absolute dose verification and dose distribution verification were obtained, and it was examined whether dose verifications could be predicted from the complexity of the treatment plans.

Results

Even when the score from the ideal treatment plan was high, the results of absolute dose verification and dose distribution verification were sometimes poor. However, even when the score from the ideal treatment plan was low, the absolute volume verification and dose distribution verification sometimes yielded good results.

Conclusions

Treatment plan complexity can be determined in advance from the ideal treatment plan score calculated by PlanIQ. However, it is difficult to predict the results of dose verification using an ideal treatment plan.

AI-assisted clinical decision making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN

Purpose

AI modeling physicians' clinical decision-making (CDM) can improve the efficiency and accuracy of clinical practice or serve as a surrogate to provide initial consultations to patients seeking secondary opinions. In this study, we developed an AI network to model radiotherapy CDM and used dose prescription as an example to demonstrate its feasibility.

Materials/Methods

152 patients with brain metastases treated by radiosurgery from 2017 to 2021 were included. CT images and tumor and organ-at-risk (OAR) contours were exported. Eight relevant clinical parameters were extracted and digitized, including age, numbers of lesions, performance status (ECOG), presence of symptoms, arrangement with surgery (pre- or post-surgery radiation therapy), re-treatment, primary cancer type, and metastasis to other sites. A 3D convolutional neural network (CNN) architecture was built using three encoding paths with the same kernel and filters to capture the different image and contour features. Specifically, one path was built to capture the tumor feature, including the size and location of the tumor, another path was built to capture the relative spatial relationship between the tumor and OARs, and the third path was built to capture the clinical parameters. The model combines information from three paths to predict dose prescription. The actual prescription in the patient record was used as ground truth for model training. The model performance was assessed by 19-fold-cross-validation, with each fold consisting of randomly selected 128 training, 16 validation, and 8 testing subjects.

Result

The dose prescriptions of 152 patient cases included 48 cases with 1 × 24 Gy, 48 cases with 1 × 20-22 Gy, 32 cases with 3 × 9 Gy, and 24 cases with 5 × 6 Gy prescribed by 8 physicians. The AI model prescribed correctly for 124 (82 %) cases, including 44 (92 %) cases with 1 × 24 Gy, 36 (75 %) cases with 1 × 20-22 Gy, 25 (78 %) cases with 3 × 9 Gy, and 19 (79 %) cases with 5 × 6 Gy. Analysis of the failed cases showed the potential cause of practice variations across individual physicians, which were not accounted for in the model trained by the group data. Including clinical parameters improved the overall prediction accuracy by 20 %.

Conclusion

To our best knowledge, this is the first study to demonstrate the feasibility of AI in predicting dose prescription in CDM in radiation therapy. Such CDM models can serve as vital tools to address healthcare disparities by providing preliminary consultations to patients in underdeveloped areas or as a valuable quality assurance (QA) tool for physicians to cross-check intra- and inter-institution practices.

Hybrid Cerenkov-scintillation detector validation using Monte Carlo simulations

Objective.This study aimed at investigating through Monte Carlo simulations the limitations of a novel hybrid Cerenkov-scintillation detector and the associated method for irradiation angle measurements.Approach.Using Monte Carlo simulations, previous experimental irradiations of the hybrid detector with a linear accelerator were replicated to evaluate its general performances and limitations. Cerenkov angular calibration curves and irradiation angle measurements were then compared. Furthermore, the impact of the Cerenkov light energy dependency on the detector accuracy was investigated using the energy spectra of electrons travelling through the detector.Main results.Monte Carlo simulations were found to be in good agreement with experimental values. The irradiation angle absolute mean error was found to be less than what was obtained experimentally, with a maximum value of 1.12° for the 9 MeV beam. A 0.4% increase of the ratio of electrons having an energy below 1 MeV to the total electrons was found to impact the Cerenkov light intensity collected as a function of the incident angle. The effect of the Cerenkov intensity variation on the measured angle was determined to vary according to the slope of the angular calibration curve. While the contribution of scattered electrons with a lower energy affects the detector accuracy, the greatest discrepancies result from the limitations of the calculation method and the calibration curve itself.Significance.A precise knowledge of the limitations of the hybrid detector and the irradiation angle calculation method is crucial for a clinical implementation. Moreover, the simulations performed in this study also corroborate hypotheses made regarding the relations between multiple Cerenkov dependencies and observations from the experimental measurements.

Quality and Safety Considerations in Intensity Modulated Radiation Therapy: An ASTRO Safety White Paper Update

PURPOSE

This updated report on intensity modulated radiation therapy (IMRT) is part of a series of consensus-based white papers previously published by the American Society for Radiation Oncology (ASTRO) addressing patient safety. Since the first white papers were published, IMRT went from widespread use to now being the main delivery technique for many treatment sites. IMRT enables higher radiation doses to be delivered to more precise targets while minimizing the dose to uninvolved normal tissue. Due to the associated complexity, IMRT requires additional planning and safety checks before treatment begins and, therefore, quality and safety considerations for this technique remain important areas of focus.

METHODS AND MATERIALS

ASTRO convened an interdisciplinary task force to assess the original IMRT white paper and update content where appropriate. Recommendations were created using a consensus-building methodology, and task force members indicated their level of agreement based on a 5-point Likert scale, from "strongly agree" to "strongly disagree." A prespecified threshold of ≥75% of raters who select "strongly agree" or "agree" indicated consensus.

CONCLUSIONS

This IMRT white paper primarily focuses on quality and safety processes in planning and delivery. Building on the prior version, this consensus paper incorporates revised and new guidance documents and technology updates. IMRT requires an interdisciplinary team-based approach, staffed by appropriately trained individuals as well as significant personnel resources, specialized technology, and implementation time. A comprehensive quality assurance program must be developed, using established guidance, to ensure IMRT is performed in a safe and effective manner. Patient safety in the delivery of IMRT is everyone's responsibility, and professional organizations, regulators, vendors, and end-users must work together to ensure the highest levels of safety.

Efficient dose-volume histogram-based pretreatment patient-specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy

BACKGROUND

Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly.

PURPOSE

To develop a DVH-based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA.

METHODS

A DL model with a three-dimensional squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head-and-neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training.

RESULTS

The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for D_mean of PTV6900 (p = 0.001), D₅₀ of PTV6000 (p = 0.014), D₂ of PTV5400 (p = 0.009), D₅₀ of left parotid (p = 0.015), and D_max of left inner ear (p = 0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases.

CONCLUSIONS

The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Predictive gamma passing rate of 3D detector array-based volumetric modulated arc therapy quality assurance for prostate cancer via deep learning

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

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE

This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.

MATERIALS AND METHODS

Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.

RESULTS

The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.

CONCLUSIONS

Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

Comprehensive clinical evaluation of TomoEQA for patient-specific pre-treatment quality assurance in helical tomotherapy

Background

Based on a previous study on the feasibility of TomoEQA, an exit detector-based patient-specific pre-treatment quality assurance (QA) method for helical tomotherapy, an in-depth clinical evaluation was conducted.

Methods

Data of one hundred patients were analyzed to evaluate the clinical usefulness of TomoEQA for patient-specific pre-treatment QA in comparison with the conventional phantom-based method. Additional investigations were also performed under unusual measurement conditions to validate the off-axis region. In addition to the clinical evaluation of TomoEQA, a statistical analysis was conducted to determine the plan parameters that affect the pass/failure results of pre-treatment QA.

Results

The average and standard deviations of the gamma passing rate and point dose error for TomoEQA were comparable to those of the conventional QA method. For TomoEQA, the average values of the gamma passing rate and point dose error were 96.32% (standard deviation (1 sigma) = 3.94; 95% confidence interval (CI), 95.55 to 97.09) and − 1.12% (standard deviation (1 sigma) = 1.04; CI, − 1.32 to − 0.92), respectively. For the conventional QA method, the average values of the gamma passing rate and point dose error were 95.95% (standard deviation (1 sigma) = 4.35; 95% confidence interval (CI), 95.10 to 96.80) and − 1.20% (standard deviation (1 sigma) = 1.61; CI, − 1.52 to − 0.88), respectively. Further experiments on the off-axis region demonstrated that TomoEQA can provide accurate results for 3D dose analysis, which is inherently difficult in the conventional QA method. Through a statistical analysis based on the results of TomoEQA, it was validated that the total fraction (Total Fx), monitor units, beam-on-time, leaf-of-time below 100 ms, and planning target volume diameter were statistically significant for the pass/failure of the pre-treatment QA results.

Conclusions

TomoEQA is a clinically beneficial alternative to the conventional phantom-based QA method.

An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

PURPOSE

To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced.

METHODS

The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units.

RESULTS

The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work.

CONCLUSIONS

An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.

2-Dimensional IMRT dose audit: An Indonesian multicenter study

Intensity modulated radiation therapy (IMRT) is an advanced technique in radiation therapy delivery. IMRT depends on the accuracy of the multileaf collimator during treatment. Hence, the actual dose distribution can deviate from the treatment planning system's calculation. This study aimed to perform a multicentre planar dosimetry audit of radiotherapy centres in Indonesia, using the structure sets from AAPM TG-119. The gamma index used to evaluate the dose distribution was 3%/3 mm and 3%/2 mm. We observed 100% gamma index passing rates mostly in the 3%/3 mm evaluations. The gamma index passing rates dropped in the 3%/2 mm analysis. Most of the radiotherapy centres participating in this audit satisfied each criterion's tolerance limit of the action level. This study may become a first result for the next multicenter IMRT audit by using a standardized protocol.

Application report of automatic unlocking baseplate in radiotherapy

Purpose

To reduce the potential risk during radiotherapy treatment of patients with head and neck tumors, we improved upon the design of an existing immobilization device by adding a feature to improve patient safety during emergency releases, and we verified its clinical application.

Method

We designed an improved automatic unlocking baseplate (AUB), and conducted a dosimetry comparison with Solo Align Full Body System (SAFBS, Klarity, China). The dosimetry comparison included dose‐attenuation measurements and results from human simulation. We selected four points for measurement to allow comparison between the SAFBS and our AUB. A simulated human body model was used for CT scanning, whereby the target area and structure and simulated radiotherapy plan were conducted according to the American Academy of Pain Medicine Task Group–119 report (TG‐119), whereby the dose differences were compared. The purpose of the clinical test was to verify the reliability of the AUB system in practical clinical applications. The application tests were conducted in CT simulation (CT‐sim) and treatment rooms. The test included assessments of the stability of the system and the reliability of our device.

Results

The dose‐attenuation measurements of the two baseplates were as follows: The transmission values with our unlocking system were 0.10% higher at the first point and 0.67% lower at the third. The same dose was obtained at points 2 and 4. In the simulation study, the PTV of the AUB was lower than that of the SAFBS, including 0.39% lower D₉₉ and 0.18% lower D₉₀. Among the organ‐at‐risk doses, the average dose of the AUB in the spinal cord was 0.6% higher than that of the SAFBS, and the average dose in the left and right parotid glands was more than 1.4% lower than that of SAFBS. The clinical test results were applied in treatment room and a CT‐sim room, which show a 100% success rate after being unlocked more than 5000 times.

Conclusion

The AUB designed for head and neck patients had good functional versatility, the dose distribution met the requirements, and the automatic unlocking function was demonstrated to be stable and reliable.

Statistical analysis of correlation of gamma passing results for two quality assurance phantoms used for patient-specific quality assurance in volumetric modulated arc radiotherapy

Patient-specific quality assurance (QA) data must be migrated from outdated QA systems to new ones to produce objective results that can be understood by oncologists. We aimed to evaluate a method for obtaining a high correlation of dose distributions according to various gamma passing rates among two types of 2D detectors for the migration of patient-specific QA data of volumetric modulated arc therapy (VMAT). The patient-specific QA of 20 patients undergoing VMAT was measured in two different modes: standard single measurement (SM) mode and multiple merged measurements (MM) techniques using ArcCHECK (AC) and OCTAVIUS (OT). The correlation of the measured and calculated dose distributions was evaluated according to varying gamma passing rates (3%/3 mm, 2%/3 mm, 2%/2 mm, and 1%/1 mm). The gamma passing rates were analyzed using the Anderson-Darling normality test. Treatment plan dose distributions were calculated by intentionally shifting the calculation isocenter position (x,y,z ± 0.5, ± 1.0, ± 1.5, and ± 2.0 mm). The highest correlation between the SM and MM was observed with a gamma passing rate of 1%/1 mm with AC (r = 0.866) and 3%/2 mm with OT (r = 0.916). However, SM and MM did not follow a normal distribution with a rate of 3%/2 mm in OT. The second-highest correlation was obtained with a rate of 2%/2 mm (r = 0.900). Among the two 2D detectors, the highest correlation between the calculated and measured dose distributions was obtained for a gamma passing rate of 1%/1 mm using SM in AC and 2%/2 mm using MM in OT (r = 0.716). Adjusting the gamma passing rate and measurement mode of AC and OT resulted in higher correlations between measured and calculated dose distributions. The high correlation between different 2D detectors objectively indicated a potential migration method. This enabled the sharing of more accurate patient-specific QA data from 2D detectors with different phantoms. A high correlation was observed between the two types of detectors in this study (r = 0.716); therefore, the proposed method should be useful for oncologists to share information regarding patient-specific QA for VMAT.

To Study the Impact of Different Optimization Methods on Intensity-Modulated Radiotherapy and Volumetric-Modulated Arc Therapy Plans for Hip Prosthesis Patients

Purpose

To study the impact of different optimization methods in dealing with metallic hip implant using intensity-modulated radiotherapy (IMRT) and volumetric-modulated arc therapy (VMAT) techniques.

Materials and Methods

A cohort of 16 patients having metallic implants was selected for the study. Three sets of IMRT and VMAT plans were generated. Set 1 IMRT (IM_Base), VMAT (VM_Base) without any restrictions on beam entry and exit, set 2 (IM_ENT and VM_ENT) optimizer restricts the beam entry and set 3 (IM_EXT+ENT), neither entry nor exit doses were allowed toward the metallic implant.

Results

There was no significant difference in target (D95%) and organ-at-risk doses between IM_Base and IM_ENT. There were significant (P = 0.002) improvements in planning target volume (PTV) V95% and homogeneity from IM_EXT+ENT to IM_ENT. There was no significant difference in plan quality between VM_Base and VM_ENT. There were significant (P = 0.005) improvements in PTV, V95%, homogeneity from VM_EXT+ENT to VM_ENT. V40Gy, V30Gy for bladder, rectum, bowel, and bowel maximum dose decreases significantly (P < 0.005) in IM_ENT compared to IM_EXT+ENT, but not significant for VMAT plans. Similarly, there was a significant decrease in dose spill outside target (P < 0.05) comparing 40%, 50%, 60%, and 70% dose spills for IM_ENT compared to IM_EXT+ENT, but variations among VMAT plans are insignificant. VMAT plans were always superior to IMRT plans for the same optimization methods.

Conclusion

The best approach is to plan hip prosthesis cases with blocked entry of radiation beam for IMRT and VMAT. The VMAT plans had more volumetric coverage, fewer hotspots, and lesser heterogeneity.

Isodoses-a set theory-based patient-specific QA measure to compare planned and delivered isodose distributions in photon radiotherapy

BACKGROUND

The gamma index and dose-volume histogram (DVH)-based patient-specific quality assurance (QA) measures commonly applied in radiotherapy planning are unable to simultaneously deliver detailed locations and magnitudes of discrepancy between isodoses of planned and delivered dose distributions. By exploiting statistical classification performance measures such as sensitivity or specificity, compliance between a planned and delivered isodose may be evaluated locally, both for organs-at-risk (OAR) and the planning target volume (PTV), at any specified isodose level. Thus, a patient-specific QA tool may be developed to supplement those presently available in clinical radiotherapy.

MATERIALS AND METHODS

A method was developed to locally establish and report dose delivery errors in three-dimensional (3D) isodoses of planned (reference) and delivered (evaluated) dose distributions simultaneously as a function the dose level and of spatial location. At any given isodose level, the total volume of delivered dose containing the reference and the evaluated isodoses is locally decomposed into four subregions: true positive-subregions within both reference and evaluated isodoses, true negative-outside of both of these isodoses, false positive-inside the evaluated isodose but not the reference isodose, and false negatives-inside the reference isodose but not the evaluated isodose. Such subregions may be established over the whole volume of delivered dose. This decomposition allows the construction of a confusion matrix and calculation of various indices to quantify the discrepancies between the selected planned and delivered isodose distributions, over the complete range of values of dose delivered. The 3D projection and visualization of the spatial distribution of these discrepancies facilitates the application of the developed method in clinical practice.

RESULTS

Several clinical photon radiotherapy plans were analyzed using the developed method. In some plans at certain isodose levels, dose delivery errors were found at anatomically significant locations. These errors were not otherwise highlighted-neither by gamma analysis nor by DVH-based QA measures. A specially developed 3D projection tool to visualize the spatial distribution of such errors against anatomical features of the patient aids in the proposed analysis of therapy plans.

CONCLUSIONS

The proposed method is able to spatially locate delivery errors at selected isodose levels and may supplement the presently applied gamma analysis and DVH-based QA measures in patient-specific radiotherapy planning.

Insensitivity of machine log files to MLC leaf backlash and effect of MLC backlash on clinical dynamic MLC motion: An experimental investigation

Purpose

Multi‐leaf‐collimator (MLC) leaf position accuracy is important for accurate dynamic radiotherapy treatment plan delivery. Machine log files have become widely utilized for quality assurance (QA) of such dynamic treatments. The primary aim is to test the sensitivity of machine log files in comparison to electronic portal imaging device (EPID)‐based measurements to MLC position errors caused by leaf backlash. The secondary aim is to investigate the effect of MLC leaf backlash on MLC leaf motion during clinical dynamic plan delivery.

Methods

The sensitivity of machine log files and two EPID‐based measurements were assessed via a controlled experiment, whereby the length of the “T” section of a series of 12 MLC leaf T‐nuts in a Varian Millennium MLC for a Trilogy C‐series type linac was reduced by sandpapering the top of the “T” to introduce backlash. The built‐in machine MLC leaf backlash test as well as measurements for two EPID‐based dynamic MLC positional tests along with log files were recorded pre‐ and post‐T‐nut modification. All methods were investigated for sensitivity to the T‐nut change by assessing the effect on measured MLC leaf positions. A reduced version of the experiment was repeated on a TrueBeam type linac with Millennium MLC.

Results

No significant differences before and after T‐nut modification were detected in any of the log file data. Both EPID methods demonstrated sensitivity to the introduced change at approximately the expected magnitude with a strong dependence observed with gantry angle. EPID‐based data showed MLC positional error in agreement with the micrometer measured T‐nut length change to 0.07 ± 0.05 mm (1 SD) using the departmental routine QA test. Backlash results were consistent between linac types.

Conclusion

Machine log files appear insensitive to MLC position errors caused by MLC leaf backlash introduced via the T‐nut. The effect of backlash on clinical MLC motions is heavily gantry angle dependent.

Dosimetric Evaluation of the Treatment Plan on Indigenous Heterogeneous Phantoms using Analytical Anisotropic Algorithm and Acuros-XB Algorithm for Different Photon Energies

BACKGROUND

Modern radiotherapy techniques are using advanced algorithms; however, phantoms used for quality assurance have homogeneous density; accordingly, the development of heterogeneous phantom mimicking human body sites is imperative to examine variation between planned and delivered doses.

OBJECTIVE

This study aimed to analyze the accuracy of planned dose by different algorithms using indigenously developed heterogeneous thoracic phantom (HT).

MATERIAL AND METHODS

In this experimental study, computed tomography (CT) of HT was done, and the density of different parts was measured. The plan was generated on CT images of HCP with 6 and 15 Megavoltage (MV) photon beams using different treatment techniques, including three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiation therapy (IMRT), and volumetric modulated arc therapy (VMAT). Plans were delivered by the linear accelerator, and the dose was measured using the ion chamber (IC) placed in HT; planned and measured doses were compared.

RESULTS

Density patterns for different parts of the fabricated phantom, including rib, spine, scapula, lung, chest wall, and heart were 1.849, 1.976, 1.983, 0.173, 0.855, and 0.833 g/cc, respectively. Variation between planned and IC estimated doses with the tolerance (±5%) for all photon energies using different techniques. Acuros-XB (AXB) showed a slightly higher variation between computed and IC estimated doses using HCP compared to the analytical anisotropic algorithm (AAA).

CONCLUSION

The indigenous heterogeneous phantom can accurately simulate the dosimetric scenario for different algorithms (AXB or AAA) and be also utilized for routine patient-specific QA.

External beam irradiation angle measurement using a hybrid Cerenkov-scintillation detector

Objective. In this study, we propose a novel approach designed to take advantage of the Cerenkov light angular dependency to perform a direct measurement of an external beam irradiation angle. Approach. A Cerenkov probe composed of a 10 mm long filtered sensitive volume of clear PMMA optical fibre was built. Both filtered and raw Cerenkov signals from the transport fibre were collected through a single 1 mm diameter transport fibre. An independent plastic scintillation detector composed of 10 mm BCF12 scintillating fibre was also used for simultaneous dose measurements. A first series of measurements aimed at validating the ability to account for the Cerenkov electron energy spectrum dependency by simultaneously measuring the deposited dose, thus isolating signal variations resulting from the angular dependency. Angular calibration curve for fixed dose irradiations and incident angle measurements using electron and photon beams where also achieved. Main results. The beam nominal energy was found to have a significant impact on the shapes of the angular calibration curves. This can be linked to the electron energy spectrum dependency of the Cerenkov emission cone. Irradiation angle measurements exhibit an absolute mean error of 1.86° and 1.02° at 6 and 18 MV, respectively. Similar results were obtained with electron beams and the absolute mean error reaches 1.97°, 1.66°, 1.45° and 0.95° at 9, 12, 16 and 20 MeV, respectively. Reducing the numerical aperture of the Cerenkov probe leads to an increased angular dependency for the lowest energy while no major changes were observed at higher energy. This allowed irradiation angle measurements at 6 MeV with a mean absolute error of 4.82°. Significance. The detector offers promising perspectives as a potential tool for future quality assurance applications in radiotherapy, especially for stereotactic radiosurgery (SRS), magnetic resonance image-guided radiotherapy (MRgRT) and brachytherapy applications.