Physics Aspects of Quality Control in Radiotherapy, Edited by W.P.M. Mayles, R. Lake, A. McKanzie, E.M. Macaulay, H.M. Morgan, T.J. Jordan and S.K. Powley, The Institute of Physics and Engineering in Medicine, York, 1999, 286 pp. ISBN: 0-904181-91-X

http://www.thegreenjournal.com/article/S0167814022041500/pdf

Plan quality assessment in clinical practice: results of the 2020 ESTRO survey on plan complexity and robustness.

Stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT) have settled down in the center of modern palliative and recently curative intent treatments in the last two decades. Being special, technology-driven, direct knowledge and experience based clinical procedures, both SRS and SBRT require high precision, accuracy and reproducibility to be safely and effectively delivered, which delineates the importance of quality assurance (QA) procedures from head to toe. In this review, we focused on summary of the comprehensive QA program covering clinical, technical and patient-specific treatment aspects, which need to be individualized per department based on several current recommendation guidelines. Review Article Saglam et al.; AIR, 21(7): 22-33, 2020; Article no.AIR.57833 23

/pdf/quality-assurance-in-stereotactic-radiosurgery-and-3vysqhyqnf.pdf

Quality Assurance in Stereotactic Radiosurgery and Stereotactic Body Radiotherapy

The ACPSEM radiation oncology medical physics workforce modelling project task group was formed to acquire a snapshot of practices in Australia and New Zealand and to develop an activity-based workforce model. To achieve this, two surveys were carried out, capturing the work practices of 98 radiation oncology departments and 182 college members. The member survey provided a snapshot of the current workforce: their demographics, work conditions, professional recognition, and future plans. The facility survey provided an Australian and New Zealand contextualisation of the volume-based activities defined in the International Atomic Energy Agency activity-based radiation oncology staffing model at a granular level. An ACPSEM ROMP workforce model was developed to be a modelling tool applicable at both the facility and sector levels.

Report of the ACPSEM radiation oncology medical physics workforce modelling project task group

Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT. 

The Future of MR-Guided Radiation Therapy.

Background: The IAEA recommends a quality assurance program in radiotherapy to ensure safe and effective treatments. In this study, radiotherapy departments were surveyed on their current practice including the extent and depth of quality assurance activities.Methods: Radiotherapy departments were voluntarily surveyed in three stages, firstly, in basic facility information, secondly, in quality assurance activities and treatment techniques, and thirdly, in a snapshot of quality assurance, departmental and treatment activities.Results: The IAEA received completed surveys from 381 radiotherapy departments throughout the world with 100 radiotherapy departments completing all three surveys. Dominant patterns were found in linac-based radiotherapy with access to treatment planning systems for 3D-CRT and 3D imaging. Staffing levels for major staff groups were on average in the range recommended by the IAEA. The modal patient workload per EBRT unit was as expected in the range of 21-30 patients per day, however significant instances of high workload (more than 50 patients per day per treatment unit) were reported. Staffing levels were found to correlate with amount of treatment equipment and patient workload. In a self-assessment of quality assurance performance, most radiotherapy departments reported that they would perform at least 60% of the quality assurance activities itemized in the second survey, with particular strength in equipment quality control. In a snapshot survey of quality assurance performance, again equipment quality control practice was well developed, particularly for the treatment equipment.Conclusions: The IAEA surveys provide a snapshot of current radiotherapy practice including quality assurance activities.

An IAEA survey of radiotherapy practice including quality assurance extent and depth

<h3>Purpose/Objective(s)</h3> Delta radiomics which refers to longitudinal changes of radiomic features over time has shown the potential to predict treatment response. However, its role in predicting normal lung toxicity has not been studied extensively. This study evaluates the potential for CBCT based delta radiomics in predicting radiotherapy induced lung parenchymal changes during thoracic radiotherapy. <h3>Materials/Methods</h3> Sixty-two patients were selected who underwent thoracic radiation with volume-modulated-arc-therapy. CBCT images from treatment weeks 1, 3 and 6 (CBCTfirst, CBCTmid, CBCTend) were included. An experienced radiologist reviewed the serial lung changes and geographic location. 19 patients had radiological confirmed early pneumonitis. The normal lung was delineated into regions receiving dose between 5-10 Gy(V5-10), 10-20 Gy(V10-20), above 20 Gy (V20) on the planning CT and structures were transferred to CBCT following rigid registration using the treatment planning system. Each CBCT was processed with resampling (2mm3 isotropic), feature extraction (103 Pyradiomic features) and normalization (z-score). Optimal delta features were selected following a forward sequential selection method. Dataset was resampled with synthetic minority oversampling technique. Support vector machine classifier was used to build a machine-learning-based model to pick up incidence of early pneumonitis. The models used were built using 80:20 training/testing data split and performance was evaluated by the area-under-the-receiver-operating-characteristic-curve (AUC). <h3>Results</h3> The median target dose received by the patients during thoracic radiation was 54 Gy. The delta features selected for model training following the sequential feature selection technique were SurfaceArea, Contrast, RootMeanSquared, MeanAbsoluteDeviation, SmallAreaHighGrayLevelEmphasis (GLZSM). At CBCTmid, support vector machine classifier resulted in the best performance in predicting early pneumonitis in the region V5-10, AUC being 0.89 (95% CI 0.89-0.903) and 0.76 (0.74-0.78) in the training and testing datasets, respectively. This was followed by V20 region in the CBCTend with AUC of 0.904(0.89-0.92) and 0.71(0.69-0.74) in the training and testing datasets, respectively. <h3>Conclusion</h3> This study supports a potential role of delta radiomics to predict incidence of early pneumonitis changes with reasonable accuracy in patients undergoing thoracic radiation. Future studies with more patients and external validation are needed to confirm these results and for clinical implementation. In future, prediction models for clinical pneumonitis using CBCT based delta radiomics will be explored. 

Can CBCT-Based Delta Radiomics Predict Normal Lung Toxicity during Thoracic Radiation?

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An IAEA survey of radiotherapy practice including quality assurance extent and depth

Can CBCT-Based Delta Radiomics Predict Normal Lung Toxicity during Thoracic Radiation?