Commissioning considerations for the Bravos high-dose-rate afterloader: Towards improving treatment delivery accuracy.

BACKGROUND AND PURPOSE: The upgrade of major equipment can be disruptive to clinical operations and introduce risk as policy and procedures need to adapt to new technical possibilities and constraints. We describe here the transition from GammaMedPlus-iX to Bravos in a busy brachytherapy clinic, involving four afterloaders across two sites. MATERIAL AND METHODS: Our clinic employs three high-dose-rate remote afterloaders in four dedicated treatment vaults at the main site and a fourth afterloader at a regional location. Of more than 600 new HDR treatment plans performed annually, most are planned and treated intraoperatively. Most treatments are for prostate cancer, followed by GYN, intraoperative brachytherapy, GI, and other sites. Applicators used include vendor-provided applicators as well as third party applicators and in-house 3D-printed devices to provide interstitial, intracavitary, intraluminal, and surface treatments. All applicators were commissioned according to recommended guidelines. The choice of tolerances and the design of new procedures were informed by current guidelines and leveraged new HDR afterloader functionalities. A review of clinical operations in the 4 months postupgrade was conducted to evaluate the feasibility of new tolerances and the effectiveness of new procedures. RESULTS: The procedures outlined improved and standardized afterloader QA and treatment protocols with clear actionable steps for staff to follow to ensure treatments are delivered as planned. Re-commissioning of applicators yielded results similar to those previously reported by other investigators. A review of initial treatment data revealed that in one case, due to the implementation of tight tolerances, obstruction near the tip of the channel was detected and corrected prior to treatment. It confirms that the implementation of the tolerances adopted is feasible and effective in flagging treatment deviations. CONCLUSION: Enhanced procedures and QA processes were implemented successfully. We established clear actionable steps to follow by staff to ensure that treatments are delivered accurately.

Clinical applicability of Linac-integrated CBCT based NIPAM 3D dosimetry: a dual-institutional investigation.

Objective.To develop and benchmark a novel 3D dose verification technique consisting of polymer gel dosimetry (PGD) with cone-beam-CT (CBCT) readout through a two-institution study. The technique has potential for wide and robust applicability through reliance on CBCT readout.Approach. Three treatment plans (3-field, TG119-C-shape spine, 4-target SRS) were created by two independent institutions (Institutions A and B). A Varian Truebeam linear accelerator was used to deliver the plans to NIPAM polymer gel dosimeters produced at both institutions using an identical approach. For readout, a slow CBCT scan mode was used to acquire pre- and post-irradiation images of the gel (1 mm slice thickness). Independent gel analysis tools were used to process the PGD images (A: VistaAce software, B: in-house MATLAB code). Comparing planned and measured doses, the analysis involved a combination of 1D line profiles, 2D contour plots, and 3D global gamma maps (criteria ranging between 2%1 mm and 5%2 mm, with a 10% dose threshold).Main results. For all gamma criteria tested, the 3D gamma pass rates were all above 90% for 3-field and 88% for the SRS plan. For the C-shape spine plan, we benchmarked our 2% 2 mm result against previously published work using film analysis (93.4%). For 2%2 mm, 99.4% (Institution A data), and 89.7% (Institution B data) were obtained based on VistaAce software analysis, 83.7% (Institution A data), and 82.9% (Institution B data) based on MATLAB.Significance. The benchmark data demonstrate that when two institutions follow the same rigorous procedures gamma passing rates up to 99%, for 2%2 mm criteria can be achieved for substantively different treatment plans. The use of different software and calibration techniques may have contributed to the variation in the 3D gamma results. By sharing the data across institutions, we observe the gamma passing rate is more consistent within each pipeline, indicating the need for standardized analysis methods.

Turning the attention to time-resolved EPID-images: treatment error classification with transformer multiple instance learning.

Objective.The aim of this work was to develop a novel artificial intelligence-assistedin vivodosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy.Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI)in vivoEPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence onγ-maps during dynamic treatments. To address this limitation, simulated TRγ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TRγ-maps.Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively.Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.

Stopping power and range estimations in proton therapy based on prompt gamma timing: motion models and automated parameter optimization.

Objective.Particle therapy treatments are currently limited by uncertainties of the delivered dose. Verification techniques like Prompt-Gamma-Timing-based Stopping Power Estimation (PGT-SPE) may allow for reduction of safety margins in treatment planning.Approach.From Prompt-Gamma-Timing measurements, we reconstruct the spatiotemporal distribution of prompt gamma emissions, which is linked to the average motion of the primary particles. The stopping power is determined by fitting a model of the average particle motion. Here, we compare a previously published implementation of the particle motion model with an alternative formulation and present two formulations to automatically select the hyperparameters of our procedure. The performance was assessed using Monte-Carlo simulations of proton beams (60 MeV-219 MeV) impinging on a homogeneous PMMA phantom.Main results.The range was successfully determined within a standard deviation of 3 mm for proton beam energies from 70 MeV to 219 MeV. Stopping power estimates showed errors below 5% for beam energies above 160 MeV. At lower energies, the estimation performance degraded to unsatisfactory levels due to the short range of the protons. The new motion model improved the estimation performance by up to 5% for beam energies from 100 MeV to 150 MeV with mean errors ranging from 6% to 18%. The automated hyperparameter optimization matched the average error of previously reported manual selections, while significantly reducing the outliers.Significance.The data-driven hyperparameter optimization allowed for a reproducible and fast evaluation of our method. The updated motion model and evaluation at new beam energies bring us closer to applying PGT-SPE in more complex scenarios. Direct comparison of stopping power estimates between treatment planning and measurements during irradiation would offer a more direct verification than other secondary-particle-based techniques.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy-a proof-of-principle study.

Objective.High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom.Approach.Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of thein vivodosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification.Main results.The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time.Significance.Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

To degas or not to degas? The effectiveness of pharmaceuticals in reducing the impact of bowel volume variations during liver SBRT treatment.

The indications for liver stereotactic body radiation therapy (SBRT) continue to expand in the management of liver cancer due to the improved rates of local control with acceptable normal tissue toxicity. Changes in internal anatomy, such as the bowel, may negatively impact the precision of treatment delivery of SBRT liver treatment by influencing daily image matching. Institutions have developed various approaches to promoting bowel volume consistency. One such strategy is the administration of pharmaceuticals. The administration of pharmaceuticals, such as Simethicone, has been adopted by the Princess Alexandra Hospital Radiation Oncology Department (ROPAIR) as a method to promote consistency in the amount of bowel gas observed in liver cancer patients. This case series examines a group of patients treated at ROPAIR with liver SBRT to determine whether current practices effectively reduce the impact of bowel volume variations for liver cancer patients. Initial observations from this hypothesis generating research suggest potential improved consistency of the small bowel's anatomical position for liver SBRT patients who were administered Simethicone (Bowel bag dice similarity coefficient - Simethicone group = 0.79-0.92, Standard group = 0.24-0.93). However, it appeared that this strategy alone may not be entirely effective achieving consistency in the amount of bowel gas present throughout the duration of treatment. Further investigation into the refinement of liver SBRT pre-treatment preparation is therefore recommended.

A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy.

Objective. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with the risk of multiple anatomical and/or mechanical errors occurring simultaneously during treatment. The purpose of this study was to evaluate the capability of CNN-based error identification in this more complex scenario.Approach. For 40 lung cancer patients, clinically realistic ranges of combinations of various treatment errors within treatment plans and/or computed tomography (CT) images were simulated. Modified CT images and treatment plans were used to predict 2580 3D dose distributions, which were compared to dose distributions without errors using various gamma analysis criteria and relative dose difference as dose comparison methods. A 3D CNN capable of multilabel classification was trained to identify treatment errors at two classification levels, using dose comparison volumes as input: Level 1 (main error type, e.g. anatomical change, mechanical error) and Level 2 (error subtype, e.g. tumor regression, patient rotation). For training the CNNs, a transfer learning approach was employed. An ensemble model was also evaluated, which consisted of three separate CNNs each taking a region of interest of the dose comparison volume as input. Model performance was evaluated by calculating sample F1-scores for training and validation sets.Main results. The model had high F1-scores for Level 1 classification, but performance for Level 2 was lower, and overfitting became more apparent. Using relative dose difference instead of gamma volumes as input improved performance for Level 2 classification, whereas using an ensemble model additionally reduced overfitting. The models obtained F1-scores of 0.86 and 0.62 on an independent test set for Level 1 and Level 2, respectively.Significance. This study shows that it is possible to identify multiple errors occurring simultaneously in 3D dose verification data.

Evaluation of a planar diode matrix for SRS patient-specific QA in comparison with GAFchromic films.

PURPOSE: We validate the routine use of a two-dimensional (2D) diode matrix for patient specific pre-treatment verification for Cyberknife (CK) stereotactic radiosurgery and to compare it with film dosimetry. MATERIALS AND METHOD: A total of 46 patients were selected according to the most frequent diseases treated at our institution with the CK system, that is, brain metastases, meningiomas, spine metastases, and prostate tumors. All cases were evaluated with GAFChromic EBT-3 films and SRS MapCHECK for Fixed cone, IRIS, and MLC collimators of the CK. RESULTS: The highest mean passing rate was observed for the SRS MapCHECK system compared to films. In order to assess if the two techniques provide statistically different results, a Wilcoxon Signed-Rank non-parametric test was performed (p < 0.05) and we found gamma values significantly lower for EBT-3 films with respect to the SRS MapCHECK. We noticed a moderately significant association between the two techniques using Spearman's rank correlation coefficient (rs > 0.4). We also performed the Bland-Altman statistical method: less than 5% of the differences resulted outside the range (mean ± 1.96 × SD), so the two methods can be considered interchangeable within the combined inaccuracy. CONCLUSIONS: The use of SRS MapCHECK for CK patient specific quality assurance (QA) is feasible for a variety of clinical districts and could be reliably used as a replacement for radiochromic films.

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% Dmax ) 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.

Treatment verification in high dose rate brachytherapy using a realistic 3D printed head phantom and an imaging panel.

PURPOSE: Even though High Dose Rate (HDR) brachytherapy has good treatment outcomes in different treatment sites, treatment verification is far from widely implemented because of a lack of easily available solutions. Previously it has been shown that an imaging panel (IP) near the patient can be used to determine treatment parameters such as the dwell time and source positions in a single material pelvic phantom. In this study we will use a heterogeneous head phantom to test this IP approach, and simulate common treatment errors to assess the sensitivity and specificity of the error-detecting capabilities of the IP. METHODS AND MATERIALS: A heterogeneous head-phantom consisting of soft tissue and bone equivalent materials was 3D-printed to simulate a base of tongue treatment. An High Dose Rate treatment plan with 3 different catheters was used to simulate a treatment delivery, using dwell times ranging from 0.3 s to 4 s and inter-dwell distances of 2 mm. The IP was used to measure dwell times, positions and detect simulated errors. Measured dwell times and positions were used to calculate the delivered dose. RESULTS: Dwell times could be determined within 0.1 s. Source positions were measured with submillimeter accuracy in the plane of the IP, and average distance accuracy of 1.7 mm in three dimensions. All simulated treatment errors (catheter swap, catheter shift, afterloader errors) were detected. Dose calculations show slightly different distributions with the measured dwell positions and dwell times (gamma pass rate for 1 mm/1% of 96.5%). CONCLUSIONS: Using an IP, it was possible to verify the treatment in a realistic heterogeneous phantom and detect certain treatment errors.

In-vivo range verification analysis with in-beam PET data for patients treated with proton therapy at CNAO.

Morphological changes that may arise through a treatment course are probably one of the most significant sources of range uncertainty in proton therapy. Non-invasive in-vivo treatment monitoring is useful to increase treatment quality. The INSIDE in-beam Positron Emission Tomography (PET) scanner performs in-vivo range monitoring in proton and carbon therapy treatments at the National Center of Oncological Hadrontherapy (CNAO). It is currently in a clinical trial (ID: NCT03662373) and has acquired in-beam PET data during the treatment of various patients. In this work we analyze the in-beam PET (IB-PET) data of eight patients treated with proton therapy at CNAO. The goal of the analysis is twofold. First, we assess the level of experimental fluctuations in inter-fractional range differences (sensitivity) of the INSIDE PET system by studying patients without morphological changes. Second, we use the obtained results to see whether we can observe anomalously large range variations in patients where morphological changes have occurred. The sensitivity of the INSIDE IB-PET scanner was quantified as the standard deviation of the range difference distributions observed for six patients that did not show morphological changes. Inter-fractional range variations with respect to a reference distribution were estimated using the Most-Likely-Shift (MLS) method. To establish the efficacy of this method, we made a comparison with the Beam's Eye View (BEV) method. For patients showing no morphological changes in the control CT the average range variation standard deviation was found to be 2.5 mm with the MLS method and 2.3 mm with the BEV method. On the other hand, for patients where some small anatomical changes occurred, we found larger standard deviation values. In these patients we evaluated where anomalous range differences were found and compared them with the CT. We found that the identified regions were mostly in agreement with the morphological changes seen in the CT scan.

What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Background and purpose: Deep learning (DL) provides high sensitivity for detecting and identifying errors in pre-treatment radiotherapy quality assurance (QA). This work's objective was to systematically evaluate the impact of different dose comparison and image preprocessing methods on DL model performance for error identification in pre-treatment QA. Materials and methods: For 53 volumetric modulated arc therapy (VMAT) and 69 stereotactic body radiotherapy (SBRT) treatment plans of lung cancer patients, mechanical errors were simulated (MLC leaf positions, monitor unit scaling, collimator rotation). Two classification levels were assessed: error type (Level 1) and error magnitude (Level 2). Portal dose images with and without errors were compared using standard (gamma analysis), simple (absolute/relative dose difference, ratio) and alternative (distance-to-agreement, structural similarity index, gradient) dose comparison methods. For preprocessing, different normalization methods (min/max and mean/standard deviation) and image resolutions (32 × 32, 64 × 64 and 128 × 128) were evaluated. All possible combinations of classification level, dose comparison, normalization method and image size resulted in 144 input datasets for DL networks for error identification. Results: Average accuracy was highest for simple dose comparison methods (Level 1: 97.7%, Level 2: 78.1%) while alternative methods scored lowest (Level 1: 91.6%, Level 2: 71.2%). Mean/stdev normalization particularly improved Level 2 classification. Higher image resolution improved error identification, although for SBRT lower image resolution was also sufficient. Conclusions: The choice of dose comparison method has the largest impact on error identification for pre-treatment QA using DL, compared to image preprocessing. Model performance can improve by using simple dose comparison methods, mean/stdev normalization and high image resolution.

In Vivo Verification of Treatment Source Dwell Times in Brachytherapy of Postoperative Endometrial Carcinoma: A Feasibility Study.

(1) Background: In brachytherapy, there are still many manual procedures that can cause adverse events which can be detected with in vivo dosimetry systems. Plastic scintillator dosimeters (PSD) have interesting properties to achieve this objective such as real-time reading, linearity, repeatability, and small size to fit inside brachytherapy catheters. The purpose of this study was to evaluate the performance of a PSD in postoperative endometrial brachytherapy in terms of source dwell time accuracy. (2) Methods: Measurements were carried out in a PMMA phantom to characterise the PSD. Patient measurements in 121 dwell positions were analysed to obtain the differences between planned and measured dwell times. (3) Results: The repeatability test showed a relative standard deviation below 1% for the measured dwell times. The relative standard deviation of the PSD sensitivity with accumulated absorbed dose was lower than 1.2%. The equipment operated linearly in total counts with respect to absorbed dose and also in count rate versus absorbed dose rate. The mean (standard deviation) of the absolute differences between planned and measured dwell times in patient treatments was 0.0 (0.2) seconds. (4) Conclusions: The PSD system is useful as a quality assurance tool for brachytherapy treatments.

An audit of Grade III or more skin reactions in consecutively assessed patients at a modern radiation oncology center.

Purpose: Radiation dermatitis is most common and debilitating side effects of radiotherapy leading to treatment interruption, thereby compromising the local control, and effecting quality of life. With the invent of modern imaging and recent advances in megavoltage radiotherapy, radiation-related side effects have reduced. In this audit, we report the risk factors associated with Grade III dermatitis in modern centers. Materials and Methods: We analyzed 172 patients treated with volume modulated arc therapy (VMAT) and static field intensity-modulated radiotherapy (SFIMRT) at our center. All head and neck, breast, gynecological, GU malignancies, and sarcoma patients treated with a dose of >45 Gy from April 2018 to December 2019 were included in the study. On couch, treatment verification was done with cone-beam computer tomography (CBCT). Slice-by-slice verification of planning target volume (PTV) with CBCT was done in the first three fractions and weekly thereafter. Skin evaluation was done using CTCAE v. 5. Statistical analysis was done using SPSS v. 22. Results: Of the 172 patients treated with VMAT and SFIMRT, 15 patients (8.7%) had Grade III dermatitis. Grade III dermatitis was mostly seen in breast cancer followed by head-and-neck patients. More reactions were observed in patients with advanced stage disease. Treatment verification is important at the later course of treatment, especially in head-and-neck cases where the treatment volume is large and PTV may extend outside skin. Contributing factors of radiation dermatitis at modern radiotherapy center are gene mutation, use of concurrent chemoradiotherapy, and bolus. Conclusion: We hereby conclude that PTV mismatch in weekly treatment verification, genetic mutations, concurrent chemo-radiotherapy, use of thermoplastic mask, and bolus are the contributing factors for Grade III dermatitis in modern radiotherapy centers.

MaxiCalc: A tool for online dosimetric evaluation of source-tracking based treatment verification in HDR brachytherapy.

PURPOSE: Source tracking is becoming a more widely used approach in HDR brachytherapy treatment verification. While it provides a sensitive method to detect deviations from the treatment plan during delivery, it does not show the clinical significance of any detected changes. By incorporating a tool that calculates volumetric doses and DVH indices from measurements, source tracking systems can be expanded to assess dosimetric significance of any deviations from the plan. METHODS: The source tracking dose calculation tool, MaxiCalc, was developed in MATLAB. Validation was performed by comparing doses and DVH indices calculated in MaxiCalc to those calculated by the clinical TPS, for several test plans and 10 clinical plans. Clinical implementation was demonstrated by calculating volumetric doses from a clinical source tracking event. RESULTS: MaxiCalc showed excellent agreement with the clinical TPS for point and volumetric doses (mean difference < 0.01% and 0.1% respectively). MaxiCalc calculates dosimetrically equivalent plans to the TPS with agreement < 0.3% for all DVH indices except PTV V200%. Small differences seen for the clinical source tracking event were consistent with the known tracking uncertainties enabling them to be quantified for clinical decision making. Calculations are fast, enabling real-time use. CONCLUSIONS: MaxiCalc is an independent tool that calculates doses and DVH indices from dwells measured with any clinical HDR brachytherapy source tracking system. This extends the capabilities of source tracking systems from determining discrepancies in positions or times during delivery to assessing the dosimetric impact of any detected deviations, allowing for more comprehensive treatment verification and evaluation.

A customizable anthropomorphic phantom for dosimetric verification of 3D-printed lung, tissue, and bone density materials.

PURPOSE: To design and manufacture a customized thoracic phantom slab utilizing the 3D printing process, also known as additive manufacturing, consisting of different tissue density materials. Here, we demonstrate the 3D-printed phantom's clinical feasibility for imaging and dosimetric verification of volumetric modulated arc radiotherapy (VMAT) plans for lung and spine stereotactic ablative body radiotherapy (SABR) through end-to-end dosimetric verification. METHODS: A customizable anthropomorphic phantom slab was designed using the CT dataset of a commercial phantom (adult female ATOM dosimetry phantom, CIRS Inc.). Material extrusion 3D printing was utilized to manufacture the phantom slab consisting of acrylonitrile butadiene styrene material for the lung and the associated lesion, polylactic acid (PLA) material for soft tissue and spinal cord, and both PLA and iron-reinforced PLA materials for bone. CT images were acquired for both the commercial phantom and 3D-printed phantom for HU comparison. VMAT plans were generated for spine and lung SABR scenarios and were delivered as per departmental SABR protocols using a Varian TrueBeam STx linear accelerator. End-to-end dosimetry was implemented with radiochromic films, analyzed with gamma criteria of 5% dose difference, and a distance-to-agreement of 1 mm, at a 10% low-dose threshold by comparing with calculated dose using the Acuros algorithm of the Eclipse treatment planning system (v15.6). RESULTS: 3D-printed phantom inserts were observed to produce HU ranging from -750 to 2100. The 3D-printed phantom slab was observed to achieve a similar range of HU from the commercial phantom including a mean HU of -760 for lung tissue, a mean HU of 50 for soft tissue, and a mean HU of 220 and 630 for low- and high-density bone, respectively. Film dosimetry results show 2D-gamma passing rates for lung SABR (internal and superior) and spine SABR (inferior and superior) over 98% and 90%, respectively. CONCLUSIONS: The end-to-end testing of VMAT plans for spine and lung SABR suggests the clinical feasibility of the 3D-printed phantom, consisting of different tissue density materials that emulate lung, soft tissue, and bone in kV imaging and megavoltage photon dosimetry. Further investigation of the proposed 3D printing techniques for manufacturability and reproducibility will enable the development of clinical 3D-printed phantoms in radiotherapy.

Validation of a secondary dose check tool against Monte Carlo and analytical clinical dose calculation algorithms in VMAT.

PURPOSE: Patient-specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time-consuming reducing considerably the time available for patient treatments. In this work, a new MC-based secondary dose check software (SciMoCa) is evaluated and benchmarked against well-established TPS (Monaco and Pinnacle3 ) by means of treatment plans and dose measurements. METHODS: Fifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%-2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans. RESULTS: TPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and -0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95 % clinical tolerance threshold. CONCLUSION: This work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC-based TPS for the first time. Results show that a MC-based secondary patient-specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per-plan basis QA that effectively complements traditional commissioning and calibration protocols.

Technical challenges of linac-based stereotactic ablative body radiotherapy: short review for non-radiation oncologists.

Stereotactic ablative radiotherapy (SABR) is a radiation technique delivering high doses of radiation in a small number of treatments, to extracranial targets. It is standard of care in patients with inoperable early stage non-small cell lung cancer, and it is increasingly used in patients with oligometastatic disease. The main advantage of SABR is a steep dose gradient, allowing delivery of high biologically effective doses to the target, while minimizing irradiation exposure of the neighboring normal tissues. This results in high rates of local control of the treated target and minimal toxicity risks, and minimal impact on the quality of life of the patients. However, it requires high precision, accuracy and reproducibility during the entire process, from simulation to treatment planning and treatment delivery. This article will focus on general principles of SABR treatment planning and delivery, with emphasis on the strategies to reduce errors related to immobilization, respiratory management and treatment verification.

4D in vivo dose verification for real-time tumor tracking treatments using EPID dosimetry.

The use of sophisticated techniques such as gating and tracking treatments requires additional quality assurance to mitigate increased patient risks. To address this need, we have developed and validated an in vivo method of dose delivery verification for real-time aperture tracking techniques, using an electronic portal imaging device (EPID)-based, on-treatment patient dose reconstruction and a dynamic anthropomorphic phantom. Using 4DCT scan of the phantom, ten individual treatment plans were created, 1 for each of the 10 separate phases of the respiratory cycle. The 10 MLC apertures were combined into a single dynamic intensity-modulated radiation therapy (IMRT) plan that tracked the tumor motion. The tumor motion and linac delivery were synchronized using an RPM system (Varian Medical Systems) in gating mode with a custom breathing trace. On-treatment EPID frames were captured using a data-acquisition computer with a dedicated frame-grabber. Our in-house EPID-based in vivo dose reconstruction model was modified to reconstruct the 4D accumulated dose distribution for a dynamic MLC (DMLC) tracking plan using the 10-phase 4DCT dataset. Dose estimation accuracy was assessed for the DMLC tracking plan and a single-phase (50% phase) static tumor plan, represented a static field test to verify baseline accuracy. The 3%/3 mm chi-comparison between the EPID-based dose reconstruction for the static tumor delivery and the TPS dose calculation for the static plan resulted in 100% pass rate for planning target volume (PTV) voxels while the mean percentage dose difference was 0.6%. Comparing the EPID-based dose reconstruction for the DMLC tracking to the TPS calculation for the static plan gave a 3%/3 mm chi pass rate of 99.3% for PTV voxels and a mean percentage dose difference of 1.1%. While further work is required to assess the accuracy of this approach in more clinically relevant situations, we have established clinical feasibility and baseline accuracy of using the transmission EPID-based, in vivo patient dose verification for MLC-tracking treatments.

Simple template-based optimization for pediatric total lymphoid irradiation (TLI) radiotherapy treatments.

Total lymphoid irradiation (TLI) is used in the management of pediatric allogeneic hematopoietic stem cell transplantation (HSCT. This work aims to simplify the treatment planning process for TLI via a proposed template using the volumetric modulated arc therapy (VMAT) technique. Fifteen pediatric patients were planned, prescribed to 8 Gy in 4 fractions. Cost functions included in the template were the ones for the planning target volume (PTV), and conformality cost function (CCF) for the rest of the patient's volume. Conformity index (CI), homogeneity index (HI), conformation number (CN), gradient index (GI), integral dose, and doses to the organs at risk achieved with the template were reported. Cost function influence over various indexes was studied by Wilcoxon signed ranks test. Same 15 patients were planned with 3-dimensional conventional radiotherapy (3D-CRT) technique for comparison. Mean CI and HI were 1.33 and 0.13, respectively, which indicates good dose conformation and homogeneity. Mean CN and GI values were 0.69 and 4.51, respectively. Mean PTV coverage was reached (V100% > 95%). No correlation between the CCF and indexes values was found (p > 0.05). Doses to organs at risk (OARs) were as low as possible without losing PTV coverage. VMAT plan showed higher levels of conformation and similar homogeneity as 3D-CRT plans. Doses to OARs were inferior with VMAT except for the right kidney. The proposed template simplifies the planning of TLI treatments, and it is able to create acceptable plans with little modification in order to reduce doses to certain organs like the kidneys or the heart. VMAT technique showed higher conformation and lower doses to OAR compared to 3D-CRT.