Radiotherapy Quality Assurance Research Articles

Enhancing Radiation Therapy Plan Quality in a Multi-Site Randomized Clinical Trial with a Benchmark Credentialing Exercise: The VA STARPORT Experience

<h3>Purpose/Objective(s)</h3> VA STARPORT is a phase 2/3 trial comparing systemic therapy +/- PET-directed, metastasis directed radiation therapy (RT) for oligo recurrent prostate cancer enrolling at 16 sites. RT options include SBRT to metastases only or elective nodal RT with a simultaneous integrated boost (ENRT+SIB). We hypothesized that benchmark credentialing exercise results that were shared across study sites would enhance protocol understanding, increase compliance, and identify opportunities to improve the study protocol. <h3>Materials/Methods</h3> A hypothetical benchmark patient scenario was created including prior definitive prostate RT with recurrences in a right obturator node, right external iliac node, and right iliac bone. OARs, target structures, and prior RT dosimetry were created on an open-source CT dataset. The benchmark exercise included: Phase 1: Each site described their preferred protocol treatment approach (SBRT or ENRT+SIB) and RT details (dose, fractions, PTV margins, and method to address prior RT). Phase 2: Each site performed 2 standardized treatment planning exercises on the dataset (SBRT vs. ENRT+SIB). Compliance worksheets summarized and assessed dose metrics, target coverage, and conformality. Plan DICOM files, worksheets, and IMRT QA were submitted for central RT quality assurance (RTQA). Phase 3: SBRT and ENRT + SIB plans were scored by the study RTQA team using a numerical plan scoring system. Scorecards and lessons learned were discussed with local investigators and further communicated to all study sites. <h3>Results</h3> Site survey results (n=16) revealed 8 preferred ENRT+SIB. Only 4 ENRT+SIB prescriptions were per-protocol, and there was PTV margin non-compliance in an intended SBRT plan. Benchmark lessons learned are summarized in the Table. <h3>Conclusion</h3> A practical and useful benchmark exercise successfully assessed protocol compliance, revealed key lessons, and identified ways to improve the protocol's RT section and RTQA process. Teaching points and learned planning strategies were shared across study sites and have a potential to improve metastasis-directed RT planning for both trial and non-trial patients at each facility.

Improving Radiation Quality Through the Implementation of Prospective Peer Review

<h3>Purpose/Objective(s)</h3> Quality assurance (QA) is critical to the successful delivery of radiation therapy (RT) for cancer patients and also impacts outcomes. The present study reports the longitudinal results of a prospective peer review evaluation system (PES) implemented at a major academic health system as an integral part of RT QA. <h3>Materials/Methods</h3> All patient cases treated with RT by one of 32 physicians at 14 treatment locations within our radiation oncology department undergo prospective peer review in daily rounds. Each case is assigned a grade (A, B, C) based on the following criteria: clinical suitability for RT, prescription, contours, and treatment plan, where applicable. "A" cases require no changes, "B's" require minor modification, and "C's" require major modification with re-review prior to treatment planning. The present study is a longitudinal analysis of grade trends over time by hospital, plan type, and physician. The Wilcoxon-signed-ranked test was used to compare grades during the baseline PES period (2013-2016) to the follow-up period (2017-2021). A p-value below 0.05 is considered significant. <h3>Results</h3> A total of 20,069 cases for 14,300 patients were reviewed from September 2013 to November 2021. Of the total cases, 15,659 (78%) were curative and were included in the present analysis; 5,868 from the baseline period and 9,791 in the follow-up period. The fraction of A cases decreased from 74.8% (baseline) to 64.5% (follow-up), while the fraction of B's increased from 19.4% (baseline) to 35.4% (follow-up), and the fraction of C's decreased from 5.8% (baseline) to 0.1% (follow-up). The main hospital site contributed 54.8% of all cases and 48.1% of all B grades. IMRT plans accounted for 55.4% of all plans and 68.2% of all B scores, while 3D-conformal plans accounted for 24.4% of cases and 15.4% of all B scores. When normalized by total department A/B/C scores, there were no statistically significant changes in percent A/B/C scores over the time period studied (p-value> 0.05). However, for the 13 physicians with cases throughout the study period, there was a statistically significant increase in the fraction of B scores (p value = 0.01) and decrease in the fraction of C scores (p value < 0.01) between the two time periods when individual physician grades were normalized to their individual caseload. <h3>Conclusion</h3> PES improves the identification of quality-related factors that can impact downstream radiation treatment plan quality. The diminishing fraction of C grades over time indicates amelioration of ‘major failures,' which were addressed in the startup years of PES. The proportional increase in B's, at the cost of fewer putative A's, suggests increased scrutiny of cases, as well as findings-based improvements in contouring and planning, over time. Peer review rounds upstream of treatment planning provides valuable RT QA and should be considered by other departments to enhance the quality and consistency of RT.

A Multi-Layer Auto-Segmentation Quality Assurance and Correction Pipeline for MR-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> Fast and accurate auto-segmentation on daily image is essential for MR-guided adaptive radiotherapy (MRgART). However, the state-of-the-art deep learning auto-segmentation (DLAS) still has limited success particularly for complex structures such as those in the abdomen. Inaccurate contours from DLAS need to be inspected and edited and this manual process can be labor intensive, time consuming and subject to human errors. This work aims to develop a multi-layer pipeline that includes an automatic contour quality assurance (ACQA) test, an automatic contour refinement (ACR) process, and an interactive semi-automatic segmentation (ISS) tool to quickly identify and correct for inaccurate auto-segmented contours, thus replacing the manual process in MRgART. <h3>Materials/Methods</h3> The proposed pipeline includes the ACQA, ACR and ISS modules that are developed to detect and correct for inaccurate auto-segmented contours in the following steps: (1) identifying contours/image slices of acceptable or with minor or major errors from the input auto-segmented contours using ACQA, (2) correcting the identified inaccurate contours/slices using ACR, (3) repeating steps (1) and (2) on the contours obtained from the previous ACR until the contours/slices become acceptable or the performance of ACR becomes saturated, and (4) correcting the un-correctable contours from ACR using ISS tool. The steps 1-3 are designed to be fully automated. Details on ACQA, ACR and ISS are being investigated in separate studies. Briefly, the ACQA module uses either machine learning or DL classification algorithms based on organ and image properties to validate contour quality. The ACR module utilizes multiple correction methods, including an active contour model (ACM), U-Net and Dense UNet DL models to sequentially refine the inaccurate contours with minor and/or major errors. The ISS tool uses the accurate contours on an adjacent slice as prior knowledge informant to guide a 2D UNet DL model to predict contours on a subsequent slice. We have demonstrated the performance of the multi-layer pipeline on complex abdominal organs on various MRIs. <h3>Results</h3> The ACQA models achieved AUC/accuracy of 0.95/0.89 for pancreas and 0.92/0.88 for stomach contours. The ACM model improved 54%/49% of bowels/pancreas contours from major into minor errors and the UNet/Dense UNet ACR model improved 43%/33% of the minor bowel/stomach contours to be acceptable. The ISS tool achieved DSC of 0.92, 0.91, 0.91, 0.87, and 0.87 for colon, small bowel, stomach, pancreas, and duodenum. The expected execution time of the pipeline on an auto-segmented MRI was within 5 minutes. <h3>Conclusion</h3> This proposed multi-layer pipeline can complement deep learning auto-segmentation and can improve the segmentation efficiency and accuracy. As part of our on-going efforts, the pipeline is being implemented as a post DLAS process, substantially speeding up daily recontouring in MRgART.

Contouring Variations and the Role of Deep Learning-Based Auto-Contouring in Breast Cancer Radiation Therapy: A Multi-Institutional Planning Study

<h3>Purpose/Objective(s)</h3> To quantify variations in target and organ-at-risk (OAR) contouring and to investigate whether intervention with deep learning-based auto-contour could improve the interobserver variations. <h3>Materials/Methods</h3> Two breast cancer cases (one with breast conservation surgery and the other with mastectomy with immediate implant-based reconstruction) were distributed with clinical information to multiple institutions that responded to participate in KROG 21-01 (1^st phase). Six months after the 1^st phase data was collected, auto-contour sets were generated for two cases using the deep-learning model that had previously been developed and verified, and the participants were asked to edit and submit the finalized auto-contours (2^nd phase). Consensus contour sets (i.e., ground truth) were generated and approved by members of KROG. Study endpoints were Dice similarity coefficient (DSC), 95% Hausdorff distance (HD), and added path length (APL). <h3>Results</h3> Thirty-one physicians from 31 institutions participated in this study. Statistically significant improvements (<i>p</i> <.05) in DSC, HD, or APL were observed in most target volumes (CTV_Ax_level II and III, CTV_IMN, and CTV_breast) and some OARs (heart, contralateral_breast, Lung_L and R, A_LAD, and thyroid) in the 2^nd phase compared to the 1^st phase. All OARs had a high level of agreement with less variability, except for A_LAD. Despite the high level of agreement in PTV (average interquartile range of DSC and HD; 0.08 and 7.90 mm, respectively), the lymph node CTVs had a considerable variance (the highest being CTV_Ax_level III). For each structure, areas with substantial contouring deviations were identified and shown using heatmaps. <h3>Conclusion</h3> Our findings demonstrated the potential of deep-learning based auto-contours in radiation therapy quality assurance programs for reducing the interobserver variations in multi-center studies.

Improving Online Adaptive Radiotherapy Quality Assurance with Streamlined Clinical Workflow through In-House Development

<h3>Purpose/Objective(s)</h3> Online adaptive radiotherapy has been clinically feasible through decades of research and development and is now available in the clinical routine with dedicated platforms using CBCT-LINAC or MR-LINAC. The quality assurance (QA) of online plans has become a bottleneck in the clinical workflow as it often requires manual data export/import through third party software, lengthy wait for the secondary dose calculation, and tedious work on report generation and checking, etc. The QA bottleneck not only impedes clinical workflow but also risks plan and treatment quality due to the time pressure with the patient waiting on the treatment table. We have developed and implemented in-house solutions to address such a bottleneck. <h3>Materials/Methods</h3> We developed dedicated in-house infrastructure to facilitate online adaptive plan QA and clinical workflow: 1) RT-PACK with SQLite database, which receives and organizes DICOM-RT data from any DICOM sender (e.g., clinical TPS); 2) a general purpose dose calculation server powered by the in-house GPU-based Monte Carlo particle transport engine with electromagnetic field modelling; 3) a report generator that automatically generates reports for secondary dose and plan quality check; 4) a web-portal that allows clinicians to review and sign the report anywhere with a network and VPN connection, even from a mobile phone. The reports and related documents will be automatically sent to the Record & Verify system after obtaining both physician and physicist signatures. An auto-commissioner was also developed to facilitate beam modeling based on water-tank measurements, which serves the purpose of fully independent verification. The dose calculation was performed on the whole patient volume with grid resolution of 2 mm x 2 mm x 2 mm. The 3D dose volume, treatment fields, point dose, Gamma Index with global 3%, 2mm criteria, three orthogonal dose profiles, dose volume histograms, and dosimetric constraints are metrics to compare the plan retrieved from TPS to the secondary dose verification and reported for clinicians' review. The whole in-house infrastructure went through extensive unit tests, dosimetric accuracy verifications, and alpha and beta tests before adopted for clinical use. <h3>Results</h3> The secondary dose and plan check infrastructure went live for CBCT-LINAC and MR-LINAC in our clinic and has since performed secondary check for every plan generated (846 plans and 622 plans, respectively, in less than 6 months.) The average processing time, from DICOM receiving to report generation, was 62 seconds for CBCT-LINAC and 130 seconds for MR-LINAC, which was 3-5 minutes shorter compared to using commercial software. More than 95% of CBCT-LINAC (MR-LINAC) plans had Gamma passing rates >99% (>95%). <h3>Conclusion</h3> The in-house developed software, through strict verification and validation, has been integrated into the clinical system. It has greatly improved online adaptive quality assurance with efficient and streamlined clinical workflow.

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

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

Final Analysis of Diagnostic Endoscopic Resection Followed by Selective Chemoradiotherapy for Stage I Esophageal Cancer: JCOG0508

Final Analysis of Diagnostic Endoscopic Resection Followed by Selective Chemoradiotherapy for Stage I Esophageal Cancer: JCOG0508

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.

P097: The status quo of involved-field radiotherapy – Quality analysis of radiotherapy in the GHSG HD 16

Question: The HD 16 trial of the German Hodgkin Study Group (GHSG) investigated the use of consolidative radiotherapy (RT) after 2 cycles of ABVD chemotherapy for early-stage Hodgkin lymphoma (HL) and could demonstrate the superiority of a combined modality approach [1]. The present work aims at analyzing the quality of involved-field RT used in this study. Methods/Material: For quality assessment, 100 randomly chosen plans from the HD 16 trial were requested and analyzed. RT was assessed by experts of the radiation therapy panel of the GHSG using initial (staging) imaging, RT plans and the recommendation forms by the reference radiation oncology. Evaluation was graded as „correct“, „minor deviation“ or „major deviation“. Statistical analyses were performed using SPSS (version 27/28, IBM, NY, USA). Results: Radiation doses were adequate with a median of 20 Gy (19.8 Gy-21.6 Gy) in normofractionation. In the majority of radiation series, supradiaphragmatic target volumes were treated (91 %), with the regions most commonly irradiated being supraclavicular left/right (73 % each), infraclavicular left/right (73 % each) and cervical left/right (44 % and 45 %, respectively). Acute treatment toxicities were mild to moderate with only 3 cases of grade 3–4 toxicities (1 nausea/vomiting, 1 dysphagia, 1 mucositis). According to the RT panel, 84 % of cases were evaluated as „correct“, 5 % as „minor deviations“ and 11 % as „major deviations“. Major deviations were caused by insufficient dose coverage of involved regions in most cases (10/11), predominantly in the upper mediastinum (5/10 cases). Previous GHSG studies in early-stage HL (HD 10 [2] and HD 13 [3]) showed lower rates of RT series according to protocol (38.8 % and 52 %, respectively). A χ2-test was used to compare the number of correct RT series in the different GHSG study generations (HD 13 vs. HD 16). No expected cell frequencies were below 5. Results reveal a significant improvement in favor of HD 16 (χ2(1)=33.8, p<0.001 Φ=-0.247). Similar outcomes were found for the association of GHSG study generation and the absence of major deviations (χ2(1)=27.4, p<0.001 Φ=- 0.222). Conclusion: Since its introduction in HD10, involved-field RT has evolved considerably. However, continuous efforts in RT quality assurance and training are needed in the modern era. Literature 1.JCO. November 2019; 37(31):2835-2845. 2.IJROBP. August 2008; 71 (5):1419-1424. 3.SUON. February 2017;193(2):109-115.

Fast Isocenter Determination Using 3D Polymer Gel Dosimetry with Kilovoltage Cone-Beam CT Reading and the PolyGeVero-CT Software Package for Linac Quality Assurance in Radiotherapy.

This work presents an approach to the fast determination of a medical accelerator irradiation isocenter as a quality assurance (QA) procedure in radiotherapy. The isocenter determination tool is the tissue equivalent high-resolution 3D polymer gel dosimeter (PABIGnx) in a dedicated container combined with kilovoltage imaging systems and the polyGeVero-CT software package (v. 1.2, GeVero Co., Poland). Two accelerators were employed: Halcyon and TrueBeam (Varian, USA), both equipped with cone beam computed tomography (CBCT) and iterative reconstruction CBCT (iCBCT) algorithms. The scope of this work includes: (i) the examination of factors influencing image quality (reconstruction algorithms and modes), radiation field parameters (dose and multi-leaf collimator (MLC) gaps), fiducial markers, signal averaging for reconstruction algorithms and the scanning time interval between consecutive scans, (ii) the examination of factors influencing the isocenter determination, image processing (signal averaging, background subtraction, image filtering) and (iii) an isocenter determination report using a 2D and 3D approach. An optimized protocol and isocenter determination conditions were found. The time and effort required to determine the isocenter are discussed.

A Review of Computational Phantoms for Quality Assurance in Radiology and Radiotherapy in the Deep-Learning Era

The exciting advancement related to the “modeling of digital human” in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation- transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

Feasibility study of perovskite film dosimeter for radiotherapy quality assurance

In this study, we developed a film-type perovskite dosimeter that can be used as a radiotherapy quality assurance (QA) dosimeter while replacing the existing semiconductor dosimeter. Dosimeter performance was evaluated by reproducibility, linearity, dose rate dependence, and percentage depth dose (PDD). In terms of reproducibility, the relative standard deviation (RSD) at 6 MV and 15 MV energy were 1.058% and 1.003%, respectively. As a result of linearity, a coefficient of determination of 0.9999 was presented at 6 MV and 15 MV energies. In terms of dose rate dependence evaluation, the RSD was 1.242% and 0.628% at 6 MV and 15 MV energies, respectively. As for PDD measurement, the result of matching the Dmax point was obtained. Our analysis confirms that the perovskite film dosimeter made of CsPbBr3 was suitable for use as a QA dosimeter in radiotherapy.

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

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. 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. 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. 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.

The Effect of Pattern and Infill Percentage in 3D Printer for Phantom Radiation Applications

3D printing technology was capable of fabricating phantoms to enhance quality assurance in radiation therapy. The ideal phantom has properties equivalent to the real tissue. However, 3D Printing has the limits to mimicking the attenuation properties of various tissues because during 3D printing there can be only one type of material. The purpose of this study was to evaluate the effect of infill percentage and infill patterns of 3D printing technology to simulate various types of tissue. This study used 25 samples measuring 5 × 5 × 1 cm3 from PETG material. The 20 samples were printed using variations infill percentages from 5 - 100% and the infill pattern in lines. The five samples were then printed with the infill percentage constant at 50% and used the infill pattern triangles, grid, gyroid, octet, and concentric. We used Computed Tomography (CT) to determine the Hounsfield Unit (HU) value for each sample and evaluated the suitability of each sample for phantom applications in radiation therapy and radiology. However, none of the samples was able to simulate compact bone. As a result, we found that PETG material could simulate the properties of soft tissue, fat, lung, kidney, liver, pancreas, and spongy bone. Thus, the study had shown promising potential for the fabrication of the anthropomorphic phantom of radiation therapy.

The fast calibration model for dosimetry with an electronic portal imaging device.

The aim of this study was to develop an algorithm that corrects the image of an electronic portal imaging device (EPID) of a linear accelerator so that it can be used for dosimetric purposes, such as in vivo dosimetry or quality assurance for photon radiotherapy. For that purpose, the impact of the field size, phantom thickness, and the varying spectral photon distribution within the irradiation field on the EPID image was investigated. The EPID measurements were verified using reference measurements with ionization chambers. Therefore, absolute dose measurements with an ionization chamber and relative dose measurements with a detector array were performed. An EPID calibration and correction algorithm was developed to convert the EPID image to a dose distribution. The algorithm was validated by irradiating inhomogeneous phantoms using square fields as well as irregular IMRT fields. It was possible to correct the influence of the field size, phantom thickness on the EPID signal as well as the homogenization of the image profile by several correction factors within 0.6%. A gamma index analysis (3%, 3mm) of IMRT fields showed a pass rate of above 99%, when comparing to the planning system. The developed algorithm enables an online dose measurement with the EPID during the radiation treatment. The algorithm is characterized by a robust, non-iterative, and thus real-time capable procedure with little measuring effort and does not depend on system-specific parameters. The EPID image is corrected by multiplying three independent correction factors. Therefore, it can easily be extent by further correction factors for other influencing variables, so it can be transferred to other linear accelerators and EPID configurations.

P22 Postoperative and pathological outcomes of CROSS and FLOT as neo-adjuvant therapy for oesophageal and junctional adenocarcinoma: an international cohort study from the Oesophagogastric Anastomosis Audit (OGAA)

Abstract Introduction Ongoing debate exists around optimum approach to locally advanced esophageal adenocarcinoma (EAC), with proponents for perioperative chemotherapy, such as FLOT, or multimodal therapy, in particular the CROSS regimen. This study aimed to compare the postoperative and pathological outcomes between CROSS and FLOT in esophageal adenocarcinoma (EAC) patients from an international, multicenter cohort. Methods Patients undergoing CROSS (n=350) and FLOT (n=368), followed by curative esophagectomy for EAC were identified from the Oesophagogastric Anastomosis Audit (OGAA). Results The 90-day mortality was higher after CROSS than FLOT (5% vs 1%, p=0.005), even on adjusted analyses (odds ratio (OR): 3.97, CI95%: 1.34–13.67). Postoperative mortality in CROSS were related to higher pulmonary (74% vs 60%) and cardiac complications (42% vs 20%) compared to FLOT. CROSS was associated with higher pCR rates (18% vs 10%, p=0.004) and margin-negative resections (93% vs 76%, p&amp;lt;0.001) compared with FLOT. On adjusted analyses, CROSS was associated with higher pCR rates (OR: 2.05, CI95%: 1.26–3.34) and margin-negative resections (OR: 4.55, CI95%: 2.70–7.69) compared to FLOT. Conclusion This study provides real-world data CROSS was associated with higher 90-day mortality than FLOT, related to cardio-pulmonary complications with CROSS. These warrant a further review into causes and mechanisms in selected patients, and at minimum suggest the need for strict radiation therapy quality assurance. Research into impact of higher pCR rates and R0 resections with CROSS compared to FLOT on long term survival is needed.

Variability of Target Volumes and Organs at Risk Delineation in Breast Cancer Radiation Therapy: Quality Assurance Results of the Pretrial Benchmark Case for the POTENTIAL Trial

PurposeTo estimate the variations in clinical target volumes (CTVs) and organs at risk delineation within the quality assurance (QA) program of the POTENTIAL trial, which is a multicenter, randomized phase 3 trial evaluating postmastectomy radiation therapy (RT), with or without internal mammary nodal irradiation, for patients with high-risk breast cancer. Methods and MaterialsThe simulating computed tomography scan data set of a benchmark case was sent to the participating centers, and the delineation of CTVs and organs at risk was required to be completed by the investigators following protocol guidelines. All submitted contours were reviewed and compared with the reference contours created by the QA team, using quantitative geometric analysis regarding volume and the Jaccard Index (JCI), Dice similarity coefficient, Geographic Miss Index, Discordance Index, and mean distance to agreement. In addition to the whole-volume analysis of all structures, the combination contour of the supraclavicular fossa and level III and II axilla (CTVsc + axIII + axII) was further analyzed on a slice-by-slice basis. ResultsThe contours from 26 centers were reviewed and variations were observed between submission and reference. The variations of the CTV of the chest wall, contralateral breast, and heart were small, for which the mean JCI values were 0.62, 0.68, and 0.87, respectively. However, the mean JCI values of the CTV of the internal mammary nodal region, ipsilateral brachial plexus, left anterior descending coronary artery, and right coronary artery were 0.38, 0.21, 0.29, and 0.18, respectively, suggesting marked variations. In addition, marked under- and overoutlining variations were identified on 4 slices of CTVsc + axIII + axII on slice-by-slice analysis. ConclusionsThere were residual contouring variations despite a detailed protocol being provided, confirming the importance of pretrial QA in RT and highlighting the need for education and consideration of a real-time central review of the target delineation before the trial participants begin RT.

Disadvantaged Subgroups Within the Global Head and Neck Cancer Population: How Can We Optimize Care?

Within the global head and neck cancer population, there are subgroups of patients with poorer cancer outcomes independent from tumor characteristics. In this article, we review three such groups. The first group comprises patients with nasopharyngeal cancer in low- and middle-income countries where access to high-volume, well-resourced radiotherapy centers is limited. We discuss a recent study that is aiming to improve outcomes through the instigation of a comprehensive radiotherapy quality assurance program. The second group comprises patients with low socioeconomic status in a high-income country who experience substantial financial toxicity, defined as financial hardship for patients due to health care costs. We review causes and consequences of financial toxicity and discuss how it can be mitigated. The third group comprises older patients who may poorly tolerate and not benefit from intensive standard-of-care treatment. We discuss the role of geriatric assessment, particularly in relation to the use of chemotherapy. Through better recognition and understanding of disadvantaged groups within the global head and neck cancer population, we will be better placed to instigate the necessary changes to improve outcomes and quality of life for patients with head and neck cancer.

QUARTET: A SIOP Europe project for quality and excellence in radiotherapy and imaging for children and adolescents with cancer

The European Society for Paediatric Oncology (SIOPE) Radiation Oncology Working Group presents the QUARTET Project: a centralised quality assurance programme designed to standardise care and improve the quality of radiotherapy and imaging for international clinical trials recruiting children and adolescents with cancer throughout Europe.QUARTET combines the paediatric radiation oncology expertise of SIOPE with the infrastructure and experience of the European Organisation for Research and Treatment of Cancer to deliver radiotherapy quality assurance programmes for large, prospective, international clinical trials. QUARTET-affiliated trials include children and adolescents with brain tumours, neuroblastoma, sarcomas including rhabdomyosarcoma, and renal tumours including Wilms’ tumour.With nine prospective clinical trials and two retrospective studies within the active portfolio in March 2022, QUARTET will collect one of the largest repositories of paediatric radiotherapy and imaging data, support the clinical assessment of radiotherapy, and evaluate the role and benefit of radiotherapy quality assurance for this cohort of patients within the context of clinical trials.

Resistive electrode array (REA) for radiotherapy beam monitoring and quality assurance

We have developed a new type of detector array for monitoring of radiation beams in radiotherapy. The detector has parallel-plane architecture with multiple large-area uniform thin-film electrodes. At least one of the electrodes is resistive and has multiple signal readouts spread out along its perimeter. The integral dose deposited in the detector gives rise to multiple signals that depend on the distribution of radiation with respect to resistive electrode array (REA) geometry. The purpose of the present study was to experimentally determine basic detector response to MLC collimated x-ray fields. Two detector arrays have been characterized: circular and rectangular. The current and electrostatic potential distribution within the resistive electrode are governed by the Laplace and continuity equations with boundary conditions at the border with the readouts. Measurements for pencil beams showed that signal strength depends primarily on the distances between the location of the pencil beam and the readouts. Measurements for larger irregular MLC showed that signals as a function of time are quasi-linear with respect to MLC position and are proportional to the MLC area. Derivation of clinically relevant radiation beam parameters from REA signals, such as MLC position, MLC gap size and monitor unit per MLC segment relies on the detector response model with empirical model parameters. An approximate analytical detector response model was proposed and used to fit experiment data.