Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218

Optimization of the Dose Rate Effect in Tetrazolium Gellan Gel Dosimeters

Tetrazolium salts provide an appealing candidate for 3D gel dosimeters as they exhibit a low intrinsic color, no signal diffusion and excellent chemical stability. However, a previously developed commercial product (the ClearView 3D Dosimeter) based on a tetrazolium salt dispersed within a gellan gum matrix presented a noticeable dose rate effect. The goal of this study was to find out whether ClearView could be reformulated in order to minimize the dose rate effect by optimizing of the tetrazolium salt and gellan gum concentrations and by the addition a thickening agent, ionic crosslinkers, and radical scavengers. To that goal, a multifactorial design of experiments (DOE) was conducted in small-volume samples (4-mL cuvettes). It showed that the dose rate could be effectively minimized without sacrificing the integrity, chemical stability, or dose sensitivity of the dosimeter. The results from the DOE were used to prepare candidate formulations for larger-scale testing in 1-L samples to allow for fine-tuning the dosimeter formulation and conducting more detailed studies. Finally, an optimized formulation was scaled-up to a clinically relevant volume of 2.7 L and tested against a simulated arc treatment delivery with three spherical targets (diameter 3.0 cm), requiring different doses and dose rates. The results showed excellent geometric and dosimetric registration, with a gamma passing rate (at 10% minimum dose threshold) of 99.3% for dose difference and distance to agreement criteria of 3%/2 mm, compared to 95.7% in the previous formulation. This difference may be of clinical importance, as the new formulation may allow the quality assurance of complex treatment plans, relying on a variety of doses and dose rates; thus, expanding the potential practical application of the dosimeter.

A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system

PURPOSE

To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios.

METHODS

An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared.

RESULTS

For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA.

CONCLUSIONS

This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.

A method for adjusting MLC leaf positions outside a reference region for VMAT plans in a commercial treatment planning system

Multileaf collimators (MLC) leaf positions in a volumetric modulated arc therapy (VMAT) plan is determined from inverse treatment planning process. On the isocenter plane, leaf projections should not be set too far out of projected PTV boundary. In this study we developed an automatic method of adjusting leaf positions outside a reference region of interest (ROI) for VMAT plans generated in Pinnacle treatment planning system (TPS). The proposed method consisted of a Pinnacle script and a Python program. It checked each MLC leaf position for all control points in a VMAT arc relative to a reference ROI created by adding a small margin to PTV. For leaves opened outside the reference ROI, the method adjusted their positions to reduce dose to normal tissue while maintaining PTV coverage and satisfying leaf position constraints. The deliverability and dose accuracy of the method was verified by applying it to 15 VMAT plans which were delivered in five different linacs in our department. Dosimetric improvement of the proposed method was analyzed for another group of 16 randomly selected VMAT plans. The average gamma passing rate using a 3%/3 mm criteria for the verification group of VMAT plans was 98.3% and all passing rates were above our internal passing threshold. Dosimetric improvement was observed for the evaluation group of VMAT plans. The method can improve normal tissue protection for VMAT plans. It can be safely applied in routine clinic work.

Correction factors for commissioning and patient specific quality assurance of stereotactic fields in a Monte Carlo based treatment planning system : TPS correction factors

Validation of small field dosimetry is crucial for stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT). Accurate and considered measurement of linear accelerator dose must be compared to precise and accurate calculation by the treatment planning system (TPS). Monte Carlo calculated distributions contain statistical noise, reducing the reliance that should be given to single voxel doses. The average dose to a small volume of interest (VOI) can minimise the influence of noise, but for small fields introduces significant volume averaging. Similar challenges present during measurement of composite dose from clinical plans when a small volume ionisation chamber is used. This study derived correction factors for VOI averaged TPS doses calculated for small fields, allowing correction to an isocentre dose following account for statistical noise. These factors were used to determine an optimal VOI to represent small volume ionisation chambers during patient specific quality assurance (PSQA). A retrospective comparison of 82 SRS and 28 SBRT PSQA measurements to TPS calculated doses from varying VOI was completed to evaluate the determined volumes. Small field commissioning correction factors of under 5% were obtained for field sizes of 8 mm and larger. Optimal spherical VOI with radius between 1.5 and 1.8 mm and 2.5 to 2.9 mm were determined for IBA CC01 and CC04 ionisation chambers respectively. Review of PSQA confirmed an optimal agreement between CC01 measured doses and a volume of 1.5 to 1.8 mm while CC04 measured doses showed no variation with VOI.

Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment

Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup.

Predicted Inferior Outcomes for Lung SBRT With Treatment Planning Systems That Fail Independent Phantom-Based Audits

PURPOSE

More than 15% of radiation therapy clinics fail external audits with anthropomorphic phantoms conducted by Imaging and Radiation Oncology Core-Houston (IROC-H) while passing other industry-standard quality assurance (QA) tests. We seek to evaluate the predicted effect of such failed plans on outcomes for patients treated with stereotactic body radiation therapy (SBRT) for lung tumors.

METHODS AND MATERIALS

We conducted a retrospective study of 55 patients treated with SBRT for lung tumors with a prescription biologically equivalent dose (BED)₁₀ ≥100 Gy using a treatment planning system (TPS) that passed IROC-H phantom audits. Sample linear accelerator beam models with introduced errors were commissioned by varying the multileaf collimator leaf-tip offset parameter (ie, dosimetric leaf gap) over the range ±1.0 mm relative to the validated model. These models mimic TPS that pass internal QA measures but fail IROC-H tests. Patient plans were recalculated on sample beam models. The predicted tumor control probability (TCP) and normal tissue complication probability (NTCP) were calculated based on published dose-response models.

RESULTS

A leaf-tip offset value of -1.0 mm decreased the fraction of plans receiving a planning treatment volume of BED₁₀ ≥100 Gy from 95% to 27%. This translated to a significant decrease in 2-year TCP of 4.8% (95% CI: 2.0%-5.5%) with a decrease in TCP up to 21%. Conversely, a leaf-tip offset of +1.0 mm resulted in 36% of patients exceeding previously met organs at risk (OAR) constraints, including 2 instances of spinal cord and brachial plexus overdoses and a small increase in chest wall NTCP of 0.7%, (95% CI: 0.5%-0.8%).

CONCLUSIONS

Simulated treatment plans with modest MLC leaf offsets result in lung SBRT plans that significantly underdose tumor or exceed OAR constraints. These dosimetric endpoints translate to significant detriments in TCP. These simulated plans mimic planning systems that pass internal QA measures but fail independent phantom-based tests, underscoring the need for enhanced quality assurance including external audits of TPS.

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.

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.

Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology

The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.

A convolutional neural network model for EPID-based non-transit dosimetry

PURPOSE

To develop an alternative computational approach for EPID-based non-transit dosimetry using a convolutional neural network model.

METHOD

A U-net followed by a non-trainable layer named True Dose Modulation recovering the spatialized information was developed. The model was trained on 186 Intensity-Modulated Radiation Therapy Step & Shot beams from 36 treatment plans of different tumor locations to convert grayscale portal images into planar absolute dose distributions. Input data were acquired from an amorphous-Silicon Electronic Portal Image Device and a 6 MV X-ray beam. Ground truths were computed from a conventional kernel-based dose algorithm. The model was trained by a two-step learning process and validated through a five-fold cross-validation procedure with sets of training and validation of 80% and 20%, respectively. A study regarding the dependance of the amount of training data was conducted. The performance of the model was evaluated from a quantitative analysis based the ϒ-index, absolute and relative errors computed between the inferred dose distributions and ground truths for six square and 29 clinical beams from seven treatment plans. These results were also compared to those of an existing portal image-to-dose conversion algorithm.

RESULTS

For the clinical beams, averages of ϒ-index and ϒ-passing rate (2%-2mm > 10% D_max ) of 0.24 (±0.04) and 99.29 (±0.70)% were obtained. For the same metrics and criteria, averages of 0.31 (±0.16) and 98.83 (±2.40)% were obtained with the six square beams. Overall, the developed model performed better than the existing analytical method. The study also showed that sufficient model accuracy can be achieved with the amount of training samples used.

CONCLUSION

A deep learning-based model was developed to convert portal images into absolute dose distributions. The accuracy obtained shows that this method has great potential for EPID-based non-transit dosimetry.

Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine

BACKGROUND

Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload.

METHODS

Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance.

RESULTS

For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%.

CONCLUSIONS

The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.

Optimised conformal total body irradiation: a heterogeneous practice, so where next?

The use of volumetric arc therapy and inverse planning has been in routine use in radiotherapy for two decades. However, use in total body irradiation (TBI) has been more recent and few guidelines exist as to how to plan or verify. This has led to heterogeneous approaches. The goal of this review is to provide an overview of current advanced planning and dosimetry verification protocols used in optimised conformal TBI as a basis for investigating the need for greater standardisation in TBI.

A hybrid method to improve efficiency of patient specific SRS and SBRT QA using 3D secondary dose verification

PURPOSE

Patient Specific QA (PSQA) by direct phantom measurement for all intensity modulated radiation therapy (IMRT) cases is labor intensive and an inefficient use of the Medical Physicist's time. The purpose of this work was to develop a hybrid quality assurance (QA) technique utilizing 3D dose verification as a screening tool to determine if a measurement is necessary.

METHODS

This study utilized Sun Nuclear DoseCHECK (DC), a 3D secondary verification software, and Fraction 0, a trajectory log IMRT QA software. Twenty-two Lung stereotactic body radiation therapy (SBRT) and thirty single isocentre multi-lesion SRS (MLSRS) plans were retrospectively analysed in DC. Agreement of DC and the TPS dose for selected dosimetric criteria was recorded. Calculated 95% confidence limits (CL) were used to establish action limits. All cases were delivered and measured using the Sun Nuclear stereotactic radiosurgery (SRS) MapCheck. Trajectory logs of the delivery were used to calculate Fraction 0 results for the same criteria calculated by DC. Correlation of DC and Fraction 0 results were calculated. Phantom measured QA was compared to Fraction 0 QA results for the cases which had DC criteria action limits exceeded.

RESULTS

Correlation of DC and Fraction 0 results were excellent, demonstrating the same action limits could be used for both and DC can predict Fraction 0 results. Based on the calculated action limits, zero lung SBRT cases and six MLSRS cases were identified as requiring a measurement. All plans that passed the DC screening had a passing measurement based PSQA and agreed with Fraction 0 results.

CONCLUSION

Using 95% CL action limits of dosimetric criteria, a 3D secondary dose verification can be used to determine if a measurement is required for PSQA. This method is efficient for it is part of the normal clinical workflow when verifying any clinical treatment. In addition, it can drastically reduce the number of measurements needed for PSQA.

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.

Effects of reconstruction methods on dose distribution for lung stereotactic body radiotherapy treatment plans

The aim of the present study was to investigate the effect of tumour motion on various imaging strategies as well as on treatment plan accuracy for lung stereotactic body radiotherapy treatment (SBRT) cases. The ExacTrac gating phantom and paraffin were used to investigate respiratory motion and represent a lung tumour, respectively. Four-dimensional computed tomography (4DCT) imaging was performed, while the phantom was moving sinusoidally with 4 s cycling time with three different amplitudes of 8, 16, and 24 mm. Reconstructions were done with maximum (MIP) and average intensity projection (AIP) methods. Comparisons of target density and volume were performed using two reconstruction techniques and references values. Volumetric modulated arc therapy (VMAT) and intensity modulated radiation therapy (IMRT) were planned based on reconstructed computed tomography (CT) sets, and it was examined how density variations affect the dose-volume histogram (DVH) parameters. 4D cone beam computed tomography (CBCT) was performed with the Elekta Versa HD linac imaging system before irradiation and compared with 3D CBCT. Thus, various combinations of 4DCT reconstruction methods and treatment alignment methods have been investigated. Point measurements as well as 2 and 3D dose measurements were done by optically stimulated luminescence (OSL), gafchromic films, and electronic portal imaging devices (EPIDs), respectively. The mean volume reduction was 7.8% for the AIP and 2.6% for the MIP method. The obtained Hounsfield Unit (HU) values were lower for AIP and higher for MIP when compared with the reference volume density. In DVH analysis, there were no statistical differences for D_95%, D_98%, and D_mean (p > 0.05). However, D_2% was significantly affected by HU changes (p < 0.01). A positional variation was obtained up to 2 mm in moving direction when 4D CBCT was applied after 3D CBCT. Dosimetric measurements showed that the main part of the observed dose deviation was due to movement. In lung SBRT treatment plans, D_2% doses differ significantly according to the reconstruction method. Additionally, it has been observed that setups based on 3D imaging can cause a positional error of up to 2 mm compared to setups based on 4D imaging. It is concluded that MIP has advantages over AIP in defining internal target volume (ITV) in lung SBRT applications. In addition, 4D CBCT and 3D EPID dosimetry are recommended for lung SBRT treatments.

AAPM Task Group Report 306: Quality control and assurance for tomotherapy: An update to Task Group Report 148

Since the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on-board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk-based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi-institutional analysis on tomotherapy-related failure modes and their effect ranking.

Study of dose dependence on density in planar 3D-printed applicators for HDR Ir192 surface brachytherapy

PURPOSE

This paper describes a method to evaluate the influence of 3D printed plesiotherapy applicators densities in the most clinically relevant dosimetric planes of these brachytherapy treatments. Studied densities range goes from that of water to that of air including the intermediate applicators densities made of acrylonitrile butadiene styrene and polylactic acid, materials used as Fused Deposition Modelling (FDM) filaments.

METHODS AND MATERIALS

All applicators were manufactured by means of FDM 3D printing and a special empty applicator of ABS walls was designed to be filled with water or air. In each of these applicators, the values of the dose and gamma index at the surface and at the prescription depth were measured in clinical conditions, using EBT films.

RESULTS

Analysis of results allow us to conclude that the influence of the applicators density on the dose value in the studied materials depends on the distance at which the dose is measured. Thus, at the prescription depth no influence is observed, however this influence becomes noticeable near the surface of the applicators with dose differences of more than 10% for densities close to 0.4 g/cm³.

CONCLUSION

Therefore, the density of FDM manufactured applicators should be taken into account when calculating surface dose for low density applicators, as variations caused by density can have clinical implications because is the surface dose that is associated with the toxicity of brachytherapy skin treatments.

Evaluation of HyperArc™ using film and portal dosimetry quality assurance

HyperArc™ is a stereotactic radiotherapy modality designed for targeting multiple brain metastases using a single isocenter with multiple non-coplanar arcs. This study aimed to assess the efficacy of two patient-specific quality assurance methods, film and the Varian Portal Dosimetry System with Varian's HyperArc™ technique and raise important considerations in the customisation of patient-specific quality assurance to accommodate HyperArc™ delivery. Assessment criteria included gamma analysis and mean dose at full width half maximum. The minimum metastasis size, maximum off-axis distance and suitable energy were identified and validated. Patient-specific quality assurance procedures were applied to a range of clinically relevant brain metastasis plans. Initial investigation into energy selection showed no significant differences in gamma pass rates using 6MV, 6MV FFF, or 10MV FFF for metastasis sizes greater than 15 mm diameter at the isocenter. Gamma pass rates (2%/2mm) for 15 mm metastases at the isocenter for all energies were greater than 96.0% for portal dosimetry and greater than 98.7% for film. Fields of size 15 mm placed at various distances (10-70 mm) from the isocenter resulted in a maximum mean dose difference of 1.5% between film and planned. Clinically relevant plans resulted in a maximum mean dose difference for selected metastases of 1.0% between film and plan and a maximum point dose difference of 2.9% between portal dose and plan. Portal dose image prediction was a quick and convenient quality assurance tool for metastases larger than 15 mm near the isocenter but provided diminished geometrical relevance for off-axis metastases. Film QA required exacting procedures but offered the ability to assess the accuracy of geometrical targeting for off-axis metastases and provided dosimetric accuracy for metastases to well below 15 mm diameter.

Clinical Feasibility of Using Single-isocentre Non-coplanar Volumetric Modulated Arc Therapy Combined with Non-coplanar Cone Beam Computed Tomography in Hypofractionated Stereotactic Radiotherapy for Five or Fewer Multiple Intracranial Metastases

AIMS

To evaluate the clinical feasibility of single-isocentre non-coplanar volumetric modulated arc therapy (NC-VMAT) with non-coplanar cone beam computed tomography (NC-CBCT) in hypofractionated stereotactic radiotherapy (HSRT) for five or fewer multiple brain metastases.

MATERIALS AND METHODS

Ten patients with multiple brain metastases who underwent single-isocentre NC-VMAT HSRT with limited couch rotations (within ±45°) and NC-CBCT with a limited scanning range (150-200°) were included in the current analysis. Conventional single-isocentre coplanar VMAT (C-VMAT) plans were generated and compared with NC-VMAT plans. The intracranial response and toxicities of single-isocentre NC-VMAT HSRT were also evaluated.

RESULTS

Compared with C-VMAT, NC-VMAT generated better target conformity (P < 0.05), a lower gradient index (P < 0.05) and better normal brain tissue sparing, especially for volume ≥12 Gy, with a median reduction of 12.65 cm³. For 45° couch rotation, NC-CBCT produced sufficient image quality to differentiate bony anatomy, even with a 150° scanning range, which could be successfully used for patient set-up correction. After NC-CBCT, 57.1% of the measured non-coplanar set-up errors exceeded the threshold value. The median gamma passing rate of NC-VMAT was higher than that of C-VMAT plans (P < 0.05). The non-coplanar beam of NC-VMAT with NC-CBCT corrections exhibited superior gamma passing rate to that without NC-CBCT corrections. The intracranial objective response rate and disease control rate for all patients were 80% (8/10) and 100% (10/10), respectively, and the most common toxicities were headache (20%) and dizziness (20%).

CONCLUSION

NC-VMAT with limited couch rotation (within ±45°) combined with NC-CBCT with a limited scanning range (150-200°) markedly improves the plan quality and set-up accuracy in single-isocentre multiple-target HSRT.

Effect of angular dependence for small-field dosimetry using seven different detectors

BACKGROUND

Small-field dosimetry is challenging for radiotherapy dosimetry because of the loss of lateral charged equilibrium, partial occlusion of the primary photon source by the collimating devices, perturbation effects caused by the detector materials and their design, and the detector size relative to the radiation field size, which leads to a volume averaging effect. Therefore, a suitable tool for small-field dosimetry requires high spatial resolution, tissue equivalence, angular independence, and energy and dose rate independence to achieve sufficient accuracy. Recently, with the increasing use of combinations of coplanar and non-coplanar beams for small-field dosimetry, there is a need to clarify angular dependence for dosimetry where the detector is oriented at various angles to the incident beam. However, the effect of angular dependence on small-field dosimetry with coplanar and non-coplanar beams has not been fully clarified.

PURPOSE

This study clarified the effect of angular dependence on small-field dosimetry with coplanar and non-coplanar beams using various detectors.

METHODS

Seven different detectors were used: CC01, RAZOR, RAZOR Nano, Pinpoint 3D, stereotactic field diode (SFD), microSilicon, and microDiamond. All measurements were taken using a TrueBeam STx with 6 MV and 10 MV flattening filter-free (FFF) energies using a water-equivalent spherical phantom with a source-to-axis distance of 100 cm. The detector was inserted in a perpendicular orientation, and the gantry was rotated at 15° increments from the incidence beam angle. A multi-leaf collimator (MLC) with four field sizes of 0.5 × 0.5, 1 × 1, 2 × 2, and 3 × 3 cm² , and four couch angles from 0°, 30°, 60°, and 90° (coplanar and non-coplanar) were adopted. The angular dependence response (AR) was defined as the ratio of the detector response at a given irradiation gantry angle normalized to the detector response at 0°. The maximum AR differences were calculated between the maximum and minimum AR values for each detector, field size, energy, and couch angle.

RESULTS

The maximum AR difference for the coplanar beam was within 3.3% for all conditions, excluding the maximum AR differences in 0.5 × 0.5 cm² field for CC01 and RAZOR. The maximum AR difference for non-coplanar beams was within 2.5% for fields larger than 1 × 1 cm² , excluding the maximum AR differences for RAZOR Nano, SFD, and microSilicon. The Pinpoint 3D demonstrated stable AR tendencies compared to other detectors. The maximum difference was within 2.0%, except for the 0.5 × 0.5 cm² field and couch angle at 90°. The tendencies of AR values for each detector were similar when using different energies.

CONCLUSION

This study clarified the inherent angular dependence of seven detectors that were suitable for small-field dosimetry. The Pinpoint 3D chamber had the smallest angular dependence of all detectors for the coplanar and non-coplanar beams. The findings of this study can contribute to the calculation of the AR correction factor, and it may be possible to adapt detectors with a large angular dependence on coplanar and non-coplanar beams. However, note that the gantry sag and detector-specific uncertainties increase as the field size decreases.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans

Background and purpose

Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans.

Materials and methods

A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans' sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate P R γ (3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans.

Results

The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%.

Conclusions

The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Evaluation of stereotactic VMAT lung treatment plans for small moving targets

PURPOSE

The aim of this study is to perform patient quality controls and end-to-end tests for stereotactic VMAT lung treatment plans and to investigate the influence of various parameters on the results.

METHOD

18 plans were defined by an experimental design methodology to cover a large variety of stereotactic VMAT lung treatments including different doses per fraction, target diameters, target movements and respiratory parameters. Plans were first controlled using portal dosimetry and a homogeneous static cylindrical phantom. End-to-end tests were then performed in a dynamic respiratory thorax phantom. Measurements were conducted with ionization chamber and films. Calculations were performed with the AcurosXB and AAA algorithms in 6 FFF.

RESULTS

Portal dosimetry gave excellent gamma pass rates (greater than 97.1 %) and dose deviations between measurement and calculations in a homogeneous static phantom were smaller than 2 %. The methodology followed for comparing calculated and measured doses in a moving target was validated in static fields (largest deviation smaller than  2 %). End-to-end tests showed mean deviations of 1.9 %, 3.3 % and 6.6 % for the 3, 2 and 1 cm diameter's target respectively. Deviations increased for larger movements for the 1 cm lesion.

CONCLUSION

End-to-end tests revealed that stereotactic VMAT lung treatment plans for moving targets can be delivered within 5 % for 3 and 2 cm diameter targets and amplitudes up to 1.5 cm. The AcurosXB and AAA algorithms however tend to underestimate the dose to the target. Even with satisfactory patient quality controls like portal dosimetry, extra care should be taken for GTV lesions smaller than 2 cm.

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

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

Dosimetric comparison of two dose expansion methods in intensity modulated radiotherapy for breast cancer

BACKGROUND

To explore the dosimetric difference between IMRT-VB plan based on the establishment of external expansion structure and virtual bolus (VB) and IMRT-SF based on the skin flash (SF) tool of the Eclipse treatment planning system in postoperative chest wall target intensity modulation radiotherapy plan of breast cancer.

METHODS

Twenty patients with breast cancer were randomly selected as subjects to develop IMRT-VB plan based on virtual bolus and IMRT-SF plan based on skin flash tool of Eclipse treatment planning system. The planning target volume, monitor unit (MU) of every single treatment and the dosimetric parameters of organ at risk (OARs) were recorded. Paired t-test was used for normal distribution data while nonparametric paired Wilcoxon rank sum test was used for non-normal distribution data.

RESULTS

Both IMRT-VB and IMRT-SF plan can expand outward to the chest wall skin and meet the dose requirements of clinical prescription. The conformal index, the homogeneity index, D_2%, D_98% and D_50% were significantly better in IMRT-SF plan than those in IMRT-VB plan (P < 0.05). The average MU of the IMRT-SF plan was much higher than that of the IMRT-VB plan (866.0 ± 68.1 MU vs. 760.9 ± 50.4 MU, P < 0.05). In terms of organ at risk protection, IMRT-SF plan had more advantages in the protection of ipsilateral lung and spinal cord than IMRT-VB plan (P < 0.05).

CONCLUSION

Our study indicated that IMRT-SF plan displayed clinical application superiority compared to IMRT-VB plan, and the operation steps of which are simpler and faster. Besides, IMRT-SF plan took advantages in achieve effective external expansion of skin dose intensity and OARs protection.

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

PURPOSE

Ethos CBCT-based adaptive radiotherapy (ART) system can generate an online adaptive plan by re-optimizing the initial reference plan based on the patient anatomy at the treatment. The optimization process is fully automated without any room for human intervention. Due to the change in anatomy, the ART plan can be significantly different from the initial plan in terms of plan parameters such as the aperture shapes and number of monitor units (MUs). In this study, we investigated the feasibility of using calculation-based patient specific QA for ART plans in conjunction with measurement-based and calculation-based QA for initial plans to establish an action level for the online ART patient-specific QA.

METHODS

A cohort of 98 cases treated on CBCT-based ART system were collected for this study. We performed measurement-based QA using ArcCheck and calculation-based QA using Mobius for both the initial plan and the ART plan for analysis. For online the ART plan, Mobius calculation was conducted prior to the delivery, while ArcCheck measurement was delivered on the same day after the treatment. We first investigated the modulation factors (MFs) and MU numbers of the initial plans and ART plans, respectively. The γ passing rates of initial and ART plan QA were analyzed. Then action limits were derived for QA calculation and measurement for both initial and online ART plans, respectively, from 30 randomly selected patient cases, and were evaluated using the other 68 patient cases.

RESULTS

The difference in MF between initial plan and ART-plan was 12.9% ± 12.7% which demonstrates their significant difference in plan parameters. Based on the patient QA results, pre-treatment calculation and measurement results are generally well aligned with ArcCheck measurement results for online ART plans, illustrating their feasibility as an indicator of failure in online ART QA measurements. Furthermore, using 30 randomly selected patient cases, the γ analysis action limit derived for initial plans and ART plans are 89.6% and 90.4% in ArcCheck QA (2%/2 mm) and are 92.4% and 93.6% in Mobius QA(3%/2 mm), respectively. According to the calculated action limits, the ArcCheck measurements for all the initial and ART plans passed QA successfully while the Mobius calculation action limits flagged seven and four failure cases respectively for initial plans and ART plans, respectively.

CONCLUSION

An ART plan can be substantially different from the initial plan, and therefore a separate session of ART plan QA is needed to ensure treatment safety and quality. The pre-treatment QA calculation via Mobius can serve as a reliable indicator of failure in online ART plan QA. However, given that Ethos ART system is still relatively new, ArcCheck measurement of initial plan is still in practice. It may be skipped as we gain more experience and have better understanding of the system.

Calibration and time fading characterization of a new optically stimulated luminescence film dosimeter

BACKGROUND

Optically stimulated luminescence (OSL) dosimeters produce a signal linear to the dose, which fades with time due to the spontaneous recombination of energetically unstable electron/hole traps. When used for radiotherapy (RT) applications, fading affects the signal-to-dose conversion and causes an error in the final dose measurement. Moreover, the signal fading depends to some extent on treatment-specific irradiation conditions such as irradiation times.

PURPOSE

In this work, a dose calibration function for a novel OSL film dosimeter was derived accounting for signal fading. The proposed calibration allows to perform dosimetry evaluation for different RT treatment regimes.

METHODS

A novel BaFBr:Eu²⁺ -based OSL film (Z_{eff , 6 MV}  = 4.7) was irradiated on a TrueBeam STx using a 6 MV beam with setup: 0° gantry angle, 90 cm SSD, 10 cm depth, 10 × 10 cm² field. A total of 86 measurements were acquired for dose-rates ( D ̇ $\dot{D}$ ) of 600, 300, and 200 MU/min for irradiation times (t_ir ) of 0.2, 1, 2, 4.5, 12, and 23 min and various readout times (t_scan ) between 4 and 1440 min from the start of the exposure (beam-on time). The OSL signal, S ( D ̇ , t i r , t s c a n ) $S(\dot{D},{t}_{ir},{t}_{scan})$ , was modeled via robust nonlinear regression, and two different power-law fading models were tested, respectively, independent (linear model) and dependent on the specific t i r ${t}_{ir}$ (delivery-dependent model).

RESULTS

After 1 day from the exposure, the error on the dose measurement can be as high as 48% if a fading correction is not considered. The fading contribution was characterized by two accurate models with adjusted-R² of 0.99. The difference between the two models is <4.75% for all t i r ${t}_{ir}$ and t s c a n ${t}_{scan}$ . For different beam-on times, 3, 10.5, and 20 min, the optimum t s c a n ${t}_{scan}$ was calculated in order to achieve a signal-to-dose conversion with a model-related error <1%. In the case of a 3 min irradiation, this condition is already met when the OSL-film is scanned immediately after the end of the irradiation. For an irradiation of 10.5 and 20 min, the minimum scanning time to achieve this model-related error increases, respectively, to 30 and 90 min. Under these conditions, the linear model can be used for the signal-to-dose conversion as an approximation of the delivery-dependent model. The signal-to-dose function, D(M_{i , j} , t s c a n $\ {t}_{scan}$ ), has a residual mean error of 0.016, which gives a residual dose uncertainty of 0.5 mGy in the region of steep signal fading (i.e., t s c a n ${t}_{scan}\ $ = 4 min). The function of two variables is representable as a dose surface depending on the signal (M_{i , j} ) measured for each i,j-pixel and the time of scan ( t s c a n ${t}_{scan}$ ).

CONCLUSIONS

The calibration of a novel OSL-film usable for dosimetry in different RT treatments was corrected for its signal fading with two different models. A linear calibration model independent from the treatment-specific irradiation condition results in a model-related error <1% if a proper scanning time is used for each irradiation length. This model is more practical than the delivery-dependent model because it does not need a pixel-to-pixel fading correction for different t i r ${t}_{ir}$ .

Preliminary evaluation of a novel secondary check tool for intensity modulated radiotherapy treatment planning

PURPOSE

To evaluate the dosimetric accuracy of the Delta4 Insight (DI) secondary-check dosimetry system.

METHODS

Absolute dosimetry in reference conditions, output factors, percent depth doses normalized and off-axis dose profiles for different field sizes calculated by DI were compared with measurements. Dose calculations for 20 clinical IMRT/VMAT plans generated in the TPS using both AAA or AcurosXB algorithms were compared with measurements. The average difference between calculated and measured point dose in high-dose region was calculated for all cases. 3D dose measurements were performed in Delta4 Phantom+ and a comparison between calculated and measured dose distributions was performed by means of the gamma analysis with 3 %/2 mm criteria. The dose distributions calculated by DI for 20 IMRT/VMAT plans were compared with those calculated by the TPS.

RESULTS

The absolute dosimetry computed by DI showed dose value in agreement with the measured one within 0.3 %. The average differences between measured and calculated output factors were less than 2.5 %. The average PDD differences were less than 0.6 %. An excellent agreement between calculations and off-axis measurements is found. The point doses calculated for the 20 recalculated plan showed good agreement with measurements with average differences less than 0.5 %. The average gamma pass rate values for the Delta4 Phantom + 3D dose analysis was greater than 97.%. The comparison of DI with theTPS showed good agreement for the used metrics.

CONCLUSIONS

Delta4 Insight may provide a useful independent secondary dose verification system for IMRT/VMAT plans, complementing the traditional global QA protocols.

Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac

BACKGROUND

Patient-specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C-arm linac of single-layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O-ring gantry linac "Halcyon" equipped with unique jawless stacked-and-staggered dual-layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual-layered MLC system.

PURPOSE

To evaluate the performance of ML to predict PSQA results of fixed field intensity-modulated radiation therapy (FF-IMRT) plans for linac equipped with dual-layered MLC.

METHODS AND MATERIALS

A total of 213 FF-IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1%/1, 2%/2, and 3%/2 mm criteria with a 10% threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features.

RESULTS

The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93%, 1.16%, 0.78% under 1%/1, 2%/2, and 3%/2 mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30% improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree-based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact.

CONCLUSION

For linac equipped with novel dual-layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual-layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PURPOSE

This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system.

METHODS AND MATERIALS

A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely.

RESULTS

All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification.

CONCLUSION

The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Technical note: A method to evaluate the effect of scanning beam delivery error on 3D dose and its utilization on carbon ion radiotherapy for prostate cancer

PURPOSE

To establish a method for evaluating the effect of scanning ion beam delivery error on three-dimensional (3D) dose reconstructed on patients' CT based on log file.

MATERIALS AND METHODS

This study used the MATLAB program to reconstruct the 3D dose on the patient's CT based on the log file (Dose_log ) for treatment delivery accuracy check. In addition, differences between the parameters in the log file and the treatment plan, such as the spot position, spot size, and particle number, were analyzed, as well as their effects on the dose distribution. The accuracy of the dose reconstruction algorithm was verified by comparing dose from TPS (Dose_TPS ) and the dose recalculated based on the treatment plan (Dose_rec ). Twenty treatment plans of ten prostate cancer patients received carbon ion therapy, and their corresponding 160 log files were selected for analysis and treatment delivery accuracy check. The regions with dose higher than 10% of the maximum dose were selected and 2 mm/2% criteria were used for global gamma analysis. Multiple linear regression was used to evaluate the relation between dose deviation and delivery errors.

RESULTS

For the algorithm accuracy verification, the mean relative dose difference is 1.02% ± 0.12%. For prostate cancer patients treated in our facility using carbon ion radiotherapy, the average passing rate of the gamma analysis between the Dose_log and the Dose_TPS was 95.3%. The dose deviation caused by the difference in the spot position and the number of particles was smaller than that caused by the spot size deviation.

CONCLUSION

This study established a 3D dose verification method based on log files to evaluate the accuracy of daily delivered treatment doses. In our facility, the daily delivered dose accuracy of carbon ion therapy for prostate cancer was mainly affected by the spot size deviation in terms of the machine delivery part.

Cylindrical ionization chamber response in static and dynamic 6 and 15 MV photon beams

Purpose.To investigate the response of the CC13 ionization chamber under non-reference photon beam conditions, focusing on penumbra and build-up regions of static fields and on dynamic intensity-modulated beams.Methods. Measurements were performed in 6 MV 100 × 100, 20 × 100, and 20 × 20 mm²static fields. Monte Carlo calculations were performed for the static fields and for 6 and 15 MV dynamic beam sequences using a Varian multi-leaf collimator. The chamber was modelled using EGSnrc egs_chamber software. Conversion factors were calculated by relating the absorbed dose to air in the chamber air cavity to the absorbed dose to water. Correction and point-dose correction factors were calculated to quantify the conversion factor variations.Results. The correction factors for positions on the beam central axis and at the penumbra centre were 0.98-1.02 for all static fields and depths investigated. The largest corrections were obtained for chamber positions beyond penumbra centre in the off-axis direction. Point-dose correction factors were 0.54-0.71 at 100 mm depth and their magnitude increased with decreasing field size and measurement depth. Factors of 0.99-1.03 were obtained inside and near the integrated penumbra of the dynamic field at 100 mm depth, and of 0.92-0.94 beyond the integrated penumbra centre. The variations in the ionization chamber response across the integrated dynamic penumbra qualitatively followed the behaviour across penumbra of static fields.Conclusions. Without corrections, the CC13 chamber was of limited usefulness for profile measurements in 20-mm-wide fields. However, measurements in dynamic small irregular beam openings resembling the conditions of pre-treatment patient quality assurance were feasible. Uncorrected ionization chamber response could be applied for dose verification at 100 mm depth inside and close to large gradients of dynamically accumulating high- and low-dose regions assuming 3% tolerance between measured and calculated doses.

Adaptive hypofractionted and stereotactic body radiotherapy for lung tumors with real-time MRI guidance

The treatment of central and ultracentral lung tumors with radiotherapy remains an ongoing clinical challenge. The risk of Grade 5 toxicity with ablative radiotherapy doses to these high-risk regions is significant as shown in recent prospective studies. Magnetic resonance (MR) image-guided adaptive radiotherapy (MRgART) is a new technology and may allow the delivery of ablative radiotherapy to these high-risk regions safely. MRgART is able to achieve this by utilizing small treatment margins, real-time gating/tracking and on-table plan adaptation to maintain dose to the tumor but limit dose to critical structures. The process of MRgART is complex and has nuances and challenges for the treatment of lung tumors. We outline the critical steps needed for appropriate delivery of MRgART for lung tumors safely and effectively.

Evaluation of OrthoChromic OC-1 films for photon radiotherapy application

A new film dosimetry system consists of the new OrthoChromic™ OC-1 film, and a novel calibration procedure was evaluated. Two films, C1 and C2, were exposed simultaneously using the 6FFF beam with a step-wedge pattern of five steps ranging from 590 to 3000 cGy. C1 was used for calibration, and C2 was used for calibration curve validation. The second scan of C2 was done by rotating the film by 90-deg. To evaluate the effectiveness of the non-uniform scanner response correction with the new system, a film was exposed to a 20 × 20 cm2 field. The beam profile measured with the film was compared to the IBA cc04 measurements in water. Films were irradiated to characterize the energy response, dynamic range and temporal growth effect. Open (MLC-defined) and clinical fields were radiated to evaluate the overall performance of the new system. The new calibration procedure was validated with an average dose difference of 1.6% and a gamma (2%,2 mm) passing rate of 100%. With C2 scanned 90-deg rotated, the average dose difference was 1.3%. The average difference between cc04 and film was 0.4%. The St between films and diode/cc04 were within -0.3% difference for 1 × 1 to 14 × 14 cm2 and -2.8% for 0.5 × 0.5 cm2. For clinical fields, the average gamma (3%,2 mm) was 98.8%. These results were consistent with EBT3 film and MapCheck measurements with a dose > 400 cGy. The results have shown that the OC-1 film system can achieve accurate results for QA measurements, but more considerable uncertainty was observed within the low dose range.

Prediction of portal dosimetry quality assurance results using log files-derived errors and machine learning techniques

Objective

This work aims to use machine learning models to predict gamma passing rate of portal dosimetry quality assurance with log file derived features. This allows daily treatment monitoring for patients and reduce wear and tear on EPID detectors to save cost and prevent downtime.

Methods

578 VMAT trajectory log files selected from prostate, lung and spine SBRT were used in this work. Four machine learning models were explored to identify the best performing regression model for predicting gamma passing rate within each sub-site and the entire unstratified data. Predictors used in these models comprised of hand-crafted log file-derived features as well as modulation complexity score. Cross validation was used to evaluate the model performance in terms of R² and RMSE.

Result

Using gamma passing rate of 1%/1mm criteria and entire dataset, LASSO regression has a R² of 0.121 ± 0.005 and RMSE of 4.794 ± 0.013%, SVM regression has a R² of 0.605 ± 0.036 and RMSE of 3.210 ± 0.145%, Random Forest regression has a R² of 0.940 ± 0.019 and RMSE of 1.233 ± 0.197%. XGBoost regression has the best performance with a R² and RMSE value of 0.981 ± 0.015 and 0.652 ± 0.276%, respectively.

Conclusion

Log file-derived features can predict gamma passing rate of portal dosimetry with an average error of less than 2% using the 1%/1mm criteria. This model can potentially be applied to predict the patient specific QA results for every treatment fraction.

Optimal threshold of a control parameter for tomotherapy respiratory tracking: A phantom study

BACKGROUND

Radixact Synchrony^® , a real-time motion tracking and compensating modality, is used for helical tomotherapy. Control parameters are used for the accurate application of irradiation. Radixact Synchrony^® uses the potential difference, which is an index of the accuracy of the prediction model of target motion and is represented by a statistical prediction of the 3D distance error. Although there are several reports on Radixact Synchrony^® , few have reported the appropriate settings of the potential difference threshold.

PURPOSE

This study aims to determine the optimal threshold of the potential difference of Radixact Synchrony^® during respiratory tumor-motion-tracking irradiation.

METHODS

The relationship among the dosimetric accuracy, motion tracking accuracy, and control parameter was evaluated using a moving platform, a phantom with a basic respiratory model (the fourth power of a sinusoidal wave), and several irregular respiratory model waveforms. The dosimetric accuracy was evaluated by gamma analysis (3%, 1 mm, 10% dose threshold). The tracking accuracy was measured by the distance error of the difference between the tracked and driven positions of the phantom. The largest potential difference for 95% of treatment time was evaluated, and its correlation with the gamma-pass ratio and distance error was investigated. The optimal threshold of the potential difference was determined by receiver operating characteristic (ROC) analysis.

RESULTS

A linear correlation was identified between the potential difference and the gamma-pass ratio (R = -0.704). A linear correlation was also identified between the potential difference and distance error (R = 0.827). However, as the potential difference increased, it tended to underestimate the distance error. The ROC analysis revealed that the appropriate cutoff value of the potential difference was 3.05 mm.

CONCLUSION

The irradiation accuracy with motion tracking by Radixact Synchrony^® could be predicted from the potential difference, and the threshold of the potential difference should be set to ∼3 mm.

Radiotherapy planning in a prostate cancer phantom model with intraprostatic dominant lesions using stereotactic body radiotherapy with volumetric modulated arcs and a simultaneous integrated boost

Aim

In the treatment of prostate cancer with radiation therapy, the addition of a simultaneous integrated boost (SIB) to the dominant intraprostatic lesions (DIL) may improve local control. In this study, we aimed to determine the optimal radiation strategy in a phantom model of prostate cancer using volumetric modulated arc therapy for stereotactic body radiotherapy (SBRT-VMAT) with a SIB of 1-4 DILs.

Methods

We designed and printed a three-dimensional anthropomorphic phantom pelvis to simulate individual patient structures, including the prostate gland. A total of 36.25 Gy (SBRT) was delivered to the whole prostate. The DILs were irradiated with four different doses (40, 45, 47.5, and 50 Gy) to assess the influence of different SIB doses on dose distribution. The doses were calculated, verified, and measured using both transit and non-transit dosimetry for patient-specific quality assurance using a phantom model.

Results

The dose coverage met protocol requirements for all targets. However, the dose was close to violating risk constraints to the rectum when four DILs were treated simultaneously or when the DILs were located in the posterior segments of the prostate. All verification plans passed the assumed tolerance criteria.

Conclusions

Moderate dose escalation up to 45 Gy seems appropriate in cases with DILs located in posterior prostate segments or if there are three or more DILs located in other segments.

Radio-luminescent imaging for rapid, high-resolution eye plaque loading verification

BACKGROUND

Eye plaque brachytherapy is currently an optimal therapy for intraocular cancers. Due to the lack of an effective and practical technique to measure the seed radioactivity distribution, current quality assurance (QA) practice according to the American Association of Physicists in Medicine TG129 only stipulates that the plaque assembly be visually inspected. Consequently, uniform seed activity is routinely adopted to avoid possible loading mistakes of differential seed loading. However, modulated dose delivery, which represents a general trend in radiotherapy to provide more personalized treatment for a given tumor and patient, requires differential activities in the loaded seeds.

PURPOSE

In this study, a fast and low-cost radio-luminescent imaging and dose calculating system to verify the seed activity distribution for differential loading was developed.

METHODS

A proof-of-concept system consisting of a thin scintillator sheet coupled to a camera/lens system was constructed. A seed-loaded plaque can be placed directly on the scintillator surface with the radioactive seeds facing the scintillator. The camera system collects the radioluminescent signal generated by the scintillator on its opposite side. The predicted dose distribution in the scintillator's sensitive layer was calculated using a Monte Carlo simulation with the planned plaque loading pattern of I-125 seeds. Quantitative comparisons of the distribution of relative measured signal intensity and that of the relative predicted dose in the sensitive layer were performed by gamma analysis, similar to intensity-modulated radiation therapy QA.

RESULTS

Data analyses showed high gamma (3%/0.3 mm, global, 20% threshold) passing rates for correct seed loadings and low passing rates with distinguished high gamma value area for incorrect loadings, indicating that possible errors may be detected. The measurement and analysis only required a few extra minutes, significantly shorter than the time to assay the extra verification seeds the physicist already must perform as recommended by TG129.

CONCLUSIONS

Radio-luminescent QA can be used to facilitate and assure the implementation of intensity-modulated, customized plaque loading.

Development of Automated Delivery Quality Assurance Analysis Software for Helical Tomotherapy

BACKGROUND

To develop a fully automated in-house gamma analysis software for the "Cheese" phantom-based delivery quality assurance (QA) of helical tomotherapy plans.

METHODS

The developed in-house software was designed to automate several procedures, which need to be manually performed using commercial software packages. The region of interest for the analysis was automatically selected by cropping out film edges and thresholding dose values (>10% of the maximum dose). The film-measured dose was automatically aligned to the computed dose using an image registration algorithm. An optimal film scaling factor was determined to maximize the percentage of pixels passing gamma (gamma passing rate) between the measured and computed doses (3%/3 mm criteria). This gamma analysis was repeated by introducing setup uncertainties in the anterior-posterior direction. For 73 tomotherapy plans, the gamma analysis results using the developed software were compared to those analyzed by medical physicists using a commercial software package.

RESULTS

The developed software successfully automated the gamma analysis for the tomotherapy delivery quality assurance. The gamma passing rate (GPR) calculated by the developed software was higher than that by the clinically used software by 3.0%, on average. While, for 1 of the 73 plans, the GPR by the manual gamma analysis was higher than 90% (pass/fail criteria), the gamma analysis using the developed software resulted in fail (GPR < 90%).

CONCLUSIONS

The use of automated and standardized gamma analysis software can improve both the clinical efficiency and veracity of the analysis results. Furthermore, the gamma analyses with various film scaling factors and setup uncertainties will provide clinically useful information for further investigations.

Patient specific evaluation of breathing motion induced interplay effects

PURPOSE

In lung SABR, interplay between target motion and dynamically changing beam parameters can affect the target coverage. To identify the potential need for motion-management techniques, a comprehensive methodology for pre-treatment estimation of interplay effects has been implemented.

METHODS

In conjunction with an alpha-version of VeriSoft and OCTAVIUS 4D (PTW-Freiburg, Germany), a method is presented to calculate a virtual, motion-simulated 3D dose distribution based on measurement data acquired in a stationary phantom and a subsequent correction with time-dependent target-motion patterns. In-house software has been developed to create user-defined motion patterns based on either simplistic or real patient-breathing patterns including the definition of the exact beam starting phase. The approach was validated by programmed couch and phantom motion during beam delivery. Five different breathing traces with extremely altered beam-on phases (0 % and 50 % respiratory phase) and a superior-inferior motion altitude of 25 mm were used to probe the influence of interplay effects for 14 lung SABR plans. Gamma analysis (2 %/2mm) was used for quantification.

RESULTS

Validation measurements resulted in >98 % pass rates. Regarding the interplay effect evaluation, gamma pass rates of <92 % were observed for sinusoidal breathing patterns with <25 number of breaths per delivery time (NBs) and realistic patterns with <18 NBs.

CONCLUSION

The potential influence of interplay effects on the target coverage is highly dependent on the patient's breathing behaviour. The presented moving-platform-free approach can be used for verification of ITV-based treatment plans to identify whether the clinical goals are achievable without explicit use of a respiratory management technique.

Electronic Portal Imaging Device in Pre-Treatment Patient-Specific Quality Assurance of volumetric-modulated arc therapy delivery

Background:

Radiotherapy treatment delivery is evaluated by a pre-treatment patient-specific quality assurance (PSQA) procedure to ensure the patient receives an accurate radiation dose. The current PSQA practice by using conventional phantoms requires more set-up time and cost of purchasing the tools. Therefore, this study aimed to investigate the efficiency of an electronic portal imaging device (EPID) of linear accelerator (LINAC) as a PSQA tool for volumetric-modulated arc therapy (VMAT) planning technique for nasopharyngeal carcinoma (NPC) treatment delivery.

Methods:

A NPC VMAT plan on a Rando phantom was performed by following the Radiation Therapy Oncology Group (RTOG) 0615 protocol. The gamma passing rate of the EPID and PSQA phantom (ArcCHECK) were compared among the gamma criteria of 3%/3 mm, 2%/2 mm and 1%/1 mm, respectively.

Results:

Both EPID and ArcCHECK phantom had distinguishable gamma passing rates in 3%/3 mm and 2%/2 mm with a difference of 0·87% and 0·30%, respectively. Meanwhile, the EPID system had a lower gamma passing rate than the ArcCHECK phantom in 1%/1 mm (21·23% difference). Furthermore, the sensitivity of the EPID system was evaluated and had the largest deviation in gamma passing rate from the reference position in gamma criteria of 2%/2 mm (41·14%) compared to the 3%/3 mm (25·45%) and 1%/1 mm (31·78%), discretely. The best fit line of the linear regression model for EPID was steeper than the ArcCHECK phantom in 3%/3 mm and 2%/2 mm, and vice versa in gamma criteria of 1%/1 mm. This indicates that the EPID had a higher sensitivity than the ArcCHECK phantom in 3%/3 mm and 2%/2 mm but less sensitivity in 1%/1 mm.

Conclusions:

The EPID system was efficient in performing the PSQA test of VMAT treatment in HUSM with the gamma criteria of 3%/3 mm and 2%/2 mm.

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.

Do we need patient-specific QA for adaptively generated plans? Retrospective evaluation of delivered online adaptive treatment plans on Varian Ethos

BACKGROUND

The clinical introduction of dedicated treatment units for online adaptive radiation therapy (OART) has led to widespread adoption of daily adaptive radiotherapy. OART allows for rapid generation of treatment plans using daily patient anatomy, potentially leading to reduction of treatment margins and increased normal tissue sparing. However, the OART workflow does not allow for measurement of patient-specific quality assurance (PSQA) during treatment delivery sessions and instead relies on secondary dose calculations for verification of adapted plans. It remains unknown if independent dose verification is a sufficient surrogate for PSQA measurements.

PURPOSE

To evaluate the plan quality of previously treated adaptive plans through multiple standard PSQA measurements.

METHODS

This IRB-approved retrospective study included sixteen patients previously treated with OART at our institution. PSQA measurements were performed for each patient's scheduled and adaptive plans: five adaptive plans were randomly selected to perform ion chamber measurements and two adaptive plans were randomly selected for ArcCHECK measurements. The same ArcCHECK 3D dose distribution was also sent to Mobius3D to evaluate the second-check dosimetry system.

RESULTS

All (n = 96) ion chamber measurements agreed with the planned dose within 3% with a mean of 1.4% (± 0.7%). All (n = 48) plans passed ArcCHECK measurements using a 95% gamma passing threshold and 3%/2 mm criteria with a mean of 99.1% (± 0.7%). All (n = 48) plans passed Mobius3D second-check performed with 95% gamma passing threshold and 5%/3 mm criteria with a mean of 99.0% (± 0.2%).

CONCLUSION

Plan measurement for PSQA may not be necessary for every online-adaptive treatment verification. We recommend the establishment of a periodic PSQA check to better understand trends in passing rates for delivered adaptive treatments.

Machine Learning Based Prediction of Gamma Passing Rate for VMAT Radiotherapy Plans

The use of machine learning algorithms (ML) in radiotherapy is becoming increasingly popular. More and more groups are trying to apply ML in predicting the so-called gamma passing rate (GPR). Our team has developed a customized approach of using ML algorithms to predict global GPR for electronic portal imaging device (EPID) verification for dose different 2% and distance to agreement 2 mm criteria for VMAT dynamic plans. Plans will pass if the GPR is greater than 98%. The algorithm was learned and tested on anonymized clinical data from 13 months which resulted in more than 3000 treatment plans. The obtained results of GPR prediction are very interesting. Average specificity of the algorithm based on an ensemble of 50 decision tree regressors is 91.6% for our criteria. As a result, we can reduce the verification process by 50%. The novel approach described by our team can offer a new insight into the application of ML and neural networks in GPR prediction and dosimetry.

Quantitative assessment of helical tomotherapy plans complexity

PURPOSE

An unnecessary amount of complexity in radiotherapy plans affects the efficiency of the treatments, increasing the uncertainty of dose deposition and its susceptibility to anatomical changes or setup errors. To date, tools for quantitatively assessing the complexity of tomotherapy plans are still limited. In this study, new metrics were developed to characterize different aspects of helical tomotherapy (HT) plans, and their actual effectiveness was investigated.

METHODS

The complexity of 464 HT plans delivered on a Radixact platform was evaluated. A new set of metrics was devised to assess beam geometry, leaf opening time (LOT) variability, and modulation over space and time. Sixty-five complexity metrics were extracted from the dataset using the newly in-house developed software library TCoMX: 29 metrics already proposed in the literature and 36 newly developed metrics. Their reciprocal relation is discussed. Their effectiveness was evaluated through correlation analyses with patient-specific quality assurance (PSQA) results.

RESULTS

An inverse linear relation was found between the average number of closed leaves and the average number of MLC openings and closures as well as between the choice of the modulation factor and the discontinuity of the field, suggesting some intrinsic link between the LOT distribution and the geometrical complexity of the MLC openings. The newly proposed metrics were at least as correlated as the existing ones to the PSQA results. Metrics describing the geometrical complexity of the MLC openings showed the strongest connection to the PSQA results. Significant correlations were found between at least one of the new metrics and the γ index passing rate P R γ % ( 3 % G , 2 mm ) $P{R}_{\gamma}\%(3\%G,2\textit{mm})$ for six out of seven groups of plans considered.

CONCLUSION

The new metrics proposed were shown to be effective to characterize more comprehensively the complexity of HT plans. A software library for their automatic extraction is described and made available.

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.

Using machine learning to predict gamma passing rate in volumetric-modulated arc therapy treatment plans

PURPOSE

This study aims to develop an algorithm to predict gamma passing rate (GPR) in the volumetric-modulated arc therapy (VMAT) technique.

MATERIALS AND METHODS

A total of 118 clinical VMAT plans, including 28 mediastina, 25 head and neck, 40 brains intensity-modulated radiosurgery, and 25 prostate cases, were created in RayStation treatment planning system for Edge and TrueBeam linacs. In-house scripts were developed to compute Modulation indices such as plan-averaged beam area (PA), plan-averaged beam irregularity (PI), total monitor unit (MU), leaf travel/arc length, mean dose rate variation, and mean gantry speed variation. Pretreatment verifications were performed on ArcCHECK phantom with SNC software. GPR was calculated with 3%/2 mm and 10% threshold. The dataset was randomly split into a training (70%) and a test (30%) dataset. A random forest regression (RFR) model and support vector regression (SVR) with linear kernel were trained to predict GPR using the complexity metrics as input. The prediction performance was evaluated by calculating the mean absolute error (MAE), R² , and root mean square error (RMSE).

RESULTS

RMSEs at γ 3%/2 mm for RFR and SVR were 1.407 ± 0.103 and 1.447 ± 0.121, respectively. MAE was 1.14 ± 0.084 for RFR and 1.101 ± 0.09 for SVR. R² was equal to 0.703 ± 0.027 and 0.689 ± 0.053 for RFR and SVR, respectively. GPR of 3%/2 mm with a 10% threshold can be predicted with an error smaller than 3% for 94% of plans using RFR and SVR models. The most important metrics that had the greatest impact on how accurately GPR can be predicted were determined to be the PA, PI, and total MU.

CONCLUSION

In terms of its prediction values and errors, SVR (linear) appeared to be comparable with RFR for this dataset. Based on our results, the PA, PI, and total MU calculations may be useful in guiding VMAT plan evaluation and ultimately reducing uncertainties in planning and radiation delivery.

Craniospinal Irradiation: A Dosimetric Comparison Between O-Ring Linac and Conventional C-arm Linac

Purpose

The aim of this study was to perform a dosimetric evaluation between craniospinal irradiation volumetric modulated arc therapy plans designed for an O-Ring and a conventional C-arm Linac.

Methods and Materials

Two adult patients were selected for this study. Two plans were designed one for a TrueBeam Edge and one for Halcyon O-ring Linac for each patient. The evaluation of the plans was conducted in terms of dose volume histogram analysis of the target volume and organs at risk (OARs) along with total plan monitor units and beam-on time. Paired sample t test was performed to compare D_max and D_mean of OARs for each plan's comparison. The delivery of volumetric modulated arc therapy plans was evaluated using Octavius 4D phantom.

Results

All plans demonstrated dose distributions with sufficient planned target volume conformity and homogeneity. The Homogeneity Index and Conformity Index for all plans were found comparable. The paired sample t test did not demonstrate significant difference in terms of D_max and D_mean of OARs between plans for both patients. All plans met the threshold of 90%, with Halcyon plans having higher gamma passing rates.

Conclusions

Craniospinal irradiation plans generated for Halcyon and Edge are equivalent in terms of plan quality and dose sparing at OARs. The small variations may have originated from the differences in beam profile or mean energy, the degree of the modulation for each plan and characteristics of multi leaf collimators for each treatment unit. Halcyon is able to deliver a distinctly faster treatment, but Edge provides an automatic rotational correction for patient positioning.

Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images

Machine learning (ML) methods have been implemented in radiotherapy to aid virtual specific-plan verification protocols, predicting gamma passing rates (GPR) based on calculated modulation complexity metrics because of their direct relation to dose deliverability. Nevertheless, these metrics might not comprehensively represent the modulation complexity, and automatically extracted features from alternative predictors associated with modulation complexity are needed. For this reason, three convolutional neural networks (CNN) based models were trained to predict GPR values (regression and classification), using respectively three predictors: (1) the modulation maps (MM) from the multi-leaf collimator, (2) the relative monitor units per control point profile (MUcp), and (3) the composite dose image (CDI) used for portal dosimetry, from 1024 anonymized prostate plans. The models' performance was assessed for classification and regression by the area under the receiver operator characteristic curve (AUC_ROC) and Spearman's correlation coefficient (r). Finally, four hybrid models were designed using all possible combinations of the three predictors. The prediction performance for the CNN-models using single predictors (MM, MUcp, and CDI) were AUC_ROC = 0.84 ± 0.03, 0.77 ± 0.07, 0.75 ± 0.04, andr= 0.6, 0.5, 0.7. Contrastingly, the hybrid models (MM + MUcp, MM + CDI, MUcp+CDI, MM + MUcp+CDI) performance were AUC_ROC = 0.94 ± 0.03, 0.85 ± 0.06, 0.89 ± 0.06, 0.91 ± 0.03, andr= 0.7, 0.5, 0.6, 0.7. The MP, MUcp, and CDI are suitable predictors for dose deliverability models implementing ML methods. Additionally, hybrid models are susceptible to improving their prediction performance, including two or more input predictors.