The integral quality monitor (IQM) system compares the signal measured with a large volume chamber mounted to the linear accelerator's head to the signal calculated using the patient DICOM RT plan for patient-specific quality assurance (PSQA). A method was developed to reconstruct the dose in patients using the signal measured by IQM chamber and patient planning CT images. A software tool named IQMDose3D was implemented to automate this procedure and integrated into the IQM-based PSQA workflow. IQMDose3D enables the physicists to evaluate PSQA by focusing on the clinical perspective by comparing the delivered plan to the approved clinical plan in terms of the clinical goals, dose-volume histogram (DVH) in addition to the three-dimensional (3D) gamma map and gamma pass rate.

A software system named AutoMRISimQA was developed to monitor the daily performance of a wide-bore 3T scanner(MRI) which was designed and dedicated to radiotherapy simulation. The system can monitor the performance of the MRI simulator not only by using image quality indices such as signal-to-noise ratio (SNR), uniformity, ghosting and contrast but also performing a quick check of geometry accuracy as well as the external lasers quantitatively. It was implemented into the daily clinically workflow in 2013 and has been used for more than 10 years. It was also seamlessly integrated with QAtrack, allowing continuous monitoring of the consistency of the MRI simulator's performance.

Magnetic resonance imaging (MRI) is increasingly being integrated into the radiation oncology workflow, due to its improved soft tissue contrast without additional exposure to ionising radiation. A review of MRI utilisation according to evidence based departmental guidelines was performed. Guideline utilisation rates were calculated to be 50% (true utilisation rate was 46%) of all new cancer patients treated with adjuvant or curative intent, excluding simple skin and breast cancer patients. Guideline utilisation rates were highest in the lower gastrointestinal and gynaecological subsites, with the lowest being in the upper gastrointestinal and thorax subsites. Head and neck (38% vs 45%) and CNS (46% vs 67%) cancers had the largest discrepancy between true and guideline utilisation rates due to unnamed reasons and non-contemporaneous diagnostic imaging respectively. This report outlines approximate MRI utilisation rates in a tertiary radiation oncology service and may help guide planning for future departments contemplating installation of an MRI simulator.

Abstract The sensitivity of the ArcCHECK 3D dosimeter in detecting VMAT delivery errors has been investigated. Dose and leaf positional errors of different magnitudes were introduced to whole arc and individual control points (CPs) of a simple open arc VMAT plan. The error introduced and error free plans were delivered and measured using the ArcCHECK device. The measured doses were compared against the treatment planning system calculated doses using gamma criteria with 2 percent 2mm and 3 percent 3mm tolerance levels. ArcCHECK effectively detected the dose errors resulting from MLC leaf positioning errors in limited CPs and Whole arc. For errors introduced to MU, ArcCHECK effectively detected the MU delivery errors in whole arc but not the MU errors introduced to CPs in integrated dose comparison. Abstract . The sensitivity of the ArcCHECK 3D dosimeter in detecting VMAT delivery errors has been investigated. Dose and leaf positional errors of different magnitudes were introduced to whole arc and individual control points (CPs) of a simple open arc VMAT plan. The error introduced and error free plans were delivered and measured using the ArcCHECK device. The measured doses were compared against the treatment planning system calculated doses using gamma ( γ ) criteria with 2%/2mm and 3%/3mm tolerance levels. ArcCHECK effectively detected the dose errors resulting from MLC leaf positioning errors in limited CPs and Whole arc. For errors introduced to MU, ArcCHECK effectively detected the MU delivery errors in whole arc but not the MU errors introduced to CPs in integrated dose comparison.

Purpose Dose deposition measurements for parallel MRI-linacs have previously only shown comparisons between 0 T and a single available magnetic field. The Australian MRI-Linac consists of a magnet coupled with a dual energy linear accelerator and a 120 leaf Multi-Leaf Collimator with the radiation beam parallel to the magnetic field. Two different magnets, with field strengths of 1 and 1.5 T, were used during prototyping. This work aims to characterize the impact of the magnetic field at 1 and 1.5 T on dose deposition, possible by comparing dosimetry measured at both magnetic field strengths to measurements without the magnetic field. Methods Dose deposition measurements focused on a comparison of beam quality (TPR20/10), PDD, profiles at various depths, surface doses, and field size output factors. Measurements were acquired at 0, 1, and 1.5 T. Beam quality was measured using an ion chamber in solid water at isocenter with appropriate TPR20/10 buildup. PDDs and profiles were acquired via EBT3 film placed in solid water either parallel or perpendicular to the radiation beam. Films at surface were used to determine surface dose. Output factors were measured in solid water using an ion chamber at isocenter with 10 cm solid water buildup. Results Beam quality was within +/- 0.5% of the 0 T value for the 1 and 1.5 T magnetic field strengths. PDDs and profiles showed agreement for the three magnetic field strengths at depths beyond 20 mm. Deposited dose increased at shallower depths due to electron focusing. Output factors showed agreement within 1%. Conclusion Dose deposition at depth for a parallel MRI-linac was not significantly impacted by either a 1 or 1.5 T magnetic field. PDDs and profiles at shallow depths and surface dose measurements showed significant differences between 0, 1, and 1.5 T due to electron focusing.

The Australian MRI-Linac consists of a fixed horizontal photon beam combined with a MRI. Commissioning required PDD and profiles measured in a horizontal set-up using a combination of water tank measurements and gafchromic film. To validate the methodology, measurements were performed comparing PDD and profiles measured with the gantry angle set to 0 and 90 degrees on a conventional linac. Results showed agreement to within 2.0% for PDD measured using both film and the water tank at gantry 90 degrees relative to PDD acquired using gantry 0 degrees. Profiles acquired using a water tank at both gantry 0 and 90 degrees showed agreement in FWHM to within 1 mm. The agreement for both PDD and profiles measured at gantry 90 degrees relative to gantry 0 degrees curves indicates that the methodology described can be used to acquire the necessary beam data for horizontal beam lines and in particular, commissioning the Australian MRI-linac.

Conclusion:This retrospective study shows that the SUVmax50 pre-therapeutic signal correlates with the posttherapeutic recurrences in the majority of patients.Pretherapeutic PET/CT or planning PET/CT is a useful tool to guide the future dose escalation studies.

3D Gamma index is one of the metrics which have been widely used for clinical routine patient specific quality assurance for IMRT, Tomotherapy and VMAT. The algorithms for calculating the 3D Gamma index using global and local methods implemented in two software tools: PTW- VeriSoft® as a part of OCTIVIUS 4D dosimeter systems and 3DVHTM from Sun Nuclear were assessed. The Gamma index calculated by the two systems was compared with manual calculated for one data set. The Gamma pass rate calculated by the two systems was compared using 3%/3mm, 2%/2mm, 3%/2mm and 2%/3mm for two additional data sets. The Gamma indexes calculated by the two systems were accurate, but Gamma pass rates calculated by the two software tools for same data set with the same dose threshold were different due to the different interpolation of raw dose data by the two systems and different implementation of Gamma index calculation and other modules in the two software tools. The mean difference was -1.3%±3.38 (1SD) with a maximum difference of 11.7%.

With the advent of high-field MRI scanners specially designed for modern radiotherapy planning and simulation (also known as MRI simulator), more and more oncology centres are introducing MRI into their radiotherapy workflow. A dedicated wide-bore 3T MRI simulator was integrated into our clinical workflow for various treatment sites including head and neck, pelvis, breast, cervix and lung. Patients are scanned first on a 4D CT simulator and then on the MRI simulator in the same setup position as treatment. CT and MRI images are transferred to the treatment planning system for registration. The target volume is then contoured on MRI and transferred to CT images for dose calculation and planning. In order to test the accuracy and consistency of this procedure a quality assurance (QA) program was developed using a suitable MR and CT compatible phantom. Results demonstrate the MRI-integrated workflow implemented in our centre was consistent and reproducible. It is recommended that this QA be performed quarterly or after MRI simulator major repair or maintenance.

Delivery quality assurance (DQA) has been performed for each Tomotherapy patient either using ArcCHECK or MatriXX Evolution in our clinic since 2012. ArcCHECK is a quasi-3D dosimeter whereas MatriXX is a 2D detector. A review of DQA results was performed for all patients in the last three years, a total of 221 DQA plans. These DQA plans came from 215 patients with a variety of treatment sites including head-neck, pelvis, and chest wall. The acceptable Gamma pass rate in our clinic is over 95% using 3mm and 3% of maximum planned dose with 10% dose threshold. The mean value and standard deviation of Gamma pass rates were 98.2% ± 1.98(1SD) for MatriXX and 98.5%±1.88 (1SD) for ArcCHECK. A paired t-test was also performed for the groups of patients whose DQA was performed with both the ArcCHECK and MatriXX. No statistical dependence was found in terms of the Gamma pass rate for ArcCHECK and MatriXX. The considered 3D and 2D dosimeters have achieved similar results in performing routine patient-specific DQA for patients treated on a TomoTherapy unit.

Poster: 2014 CSM / R-0059 / Consistency and accuracy of fusing MRI to CT images acquired on a MRI simulator and a CT simulator: A phantom study by: A. Xing , G. Liney, L. Holloway, S. Arumugam, P. Vial, E. Juresic, R. Rai, S. Vinod, M. Jameson, G. Goozee; SYDNEY/AU

Purpose:The data receive server (DRS) in the Tomotherapy Unit records planned and actually delivered plan parameters for each treatment into a log file. The purpose of this study was to develop a software tool using the log file for verification of the patient plan delivered during treatment.Methods:A software tool, TomoPQA, was developed using the Python programming language. The software was implemented using object‐oriented methodology and modular design. The program has three built‐in modules: Read‐in module for loading the log file, analysis module for analyzing the log file and reporting module for producing a PDF report.Results:The developed software tool can be used to monitor and check the following plan parameters during patient treatment: (1) planned and actual field width; (2) planned and actual treatment time; (3) planned and actual couch speed; (4) planned and actual gantry speed; (4) planned and actual setup position. The software shows the difference between these parameters as a graphic plot against each treatment fraction in a PDF report for easy review or these values can be exported to an excel file. The program can process a 100M byte log file and produce a report in the order of one minute and can run on Windows, Linux or Mac platforms as a standalone program. A server version of this program can also be implemented for full automation of the log file processing, generation of the PDF report and also has the potential for an automated email if given values are out of tolerance.Conclusion:A QA software tool has been developed for in‐vivo quality assurance of treatment delivered on a Tomotherapy Unit. This tool provides an independent verification of the difference between actual delivered and planned parameters during treatment.

Large amounts of data are collected on radiotherapy (RT) patients and treatments. This resource could provide clinical evidence to help future decisions. A collaborative project has begun between Australian RT centres and MAASTRO to evaluate the feasibility and impact of datamining of routine clinical RT data and the use of rapid learning tools in routine practice. The initial pilot was for non-small cell lung cancer (NSCLC) patients in the Liverpool Hospital Cancer Centre.

SharePlan is a treatment planning system developed by Raysearch Laboratories AB to enable creation of a linear accelerator intensity modulated radiotherapy (IMRT) plan as a backup for a Tomotherapy plan. A 6MV Elekta Synergy Linear accelerator photon beam was modelled in SharePlan. The beam model was validated using Matrix Evolution, a 2D ion chamber array, for two head-neck and three prostate plans using 3%/3mm Gamma criteria. For 39 IMRT beams, the minimum and maximum Gamma pass rates are 95.4% and 98.7%. SharePlan is able to generate backup IMRT plans which are deliverable on a traditional linear accelerator and accurate in terms of clinical criteria. During use of SharePlan, however, an out-of-memory error frequently occurred and SharePlan was forced to be closed. This error occurred occasionally at any of these steps: loading the Tomotherapy plan into SharePlan, generating the IMRT plan, selecting the optimal plan, approving the plan and setting up a QA plan. The out-of-memory error was caused by memory leakage in one or more of the C/C++ functions implemented in SharePlan fluence engine, dose engine or optimizer, as acknowledged by the manufacturer. Because of the interruption caused by out-of-memory errors, SharePlan has not been implemented in our clinic although accuracy has been verified. A new software program is now being provided to our centre to replace SharePlan.

BACKGROUND AND PURPOSE:A rapid learning approach has been proposed to extract and apply knowledge from routine care data rather than solely relying on clinical trial evidence. To validate this in practice we deployed a previously developed decision support system (DSS) in a typical, busy clinic for non-small cell lung cancer (NSCLC) patients. MATERIAL AND METHODS:Gender, age, performance status, lung function, lymph node status, tumor volume and survival were extracted without review from clinical data sources for lung cancer patients. With these data the DSS was tested to predict overall survival. RESULTS:3919 lung cancer patients were identified with 159 eligible for inclusion, due to ineligible histology or stage, non-radical dose, missing tumor volume or survival. The DSS successfully identified a good prognosis group and a medium/poor prognosis group (2 year OS 69% vs. 27/30%, p<0.001). Stage was less discriminatory (2 year OS 47% for stage I-II vs. 36% for stage IIIA-IIIB, p=0.12) with most good prognosis patients having higher stage disease. The DSS predicted a large absolute overall survival benefit (∼40%) for a radical dose compared to a non-radical dose in patients with a good prognosis, while no survival benefit of radical radiotherapy was predicted for patients with a poor prognosis. CONCLUSIONS:A rapid learning environment is possible with the quality of clinical data sufficient to validate a DSS. It uses patient and tumor features to identify prognostic groups in whom therapy can be individualized based on predicted outcomes. Especially the survival benefit of a radical versus non-radical dose predicted by the DSS for various prognostic groups has clinical relevance, but needs to be prospectively validated.

Using an EPID for patient specific IMRT QA is an efficient way to verify patient plans prior to the treatment. Our centres' current EPID dosimetry method is based on a water equivalent depth approach, where EPID images of each IMRT field are converted to dose images and compared to Treatment planning system calculated dose images using a commercial software tool. Two physicists across two sites perform the analysis for an average of 12 new IMRT patients per week. To speed up this process, an in-house program called AutoEPIDIMRTQA was developed. The program automatically performs the following tasks sequentially: reading and converting raw EPID images acquired on either a Siemens Oncor or Elekta Synergy linear accelerator, registering them with planar dose images calculated by Pinnacle (Philips) or CMS XiO (Elekta) treatment planning systems, analyzing the profiles for registered images and calculating the Gamma map. Finally an IMRT QA report is automatically generated. AutoEPIDIMRTQA was validated against commercial software. The analysis time for a typical 9-beam IMRT head-neck patient decreases from 30 minutes to 4 minutes. The total QA time was reduced by 40% using AutoEPIDIMRTQA. Thus we have demonstrated a significant reduction in the time burden for physics staff performing IMRT QA.