Parameters Affecting Pre-Treatment Dosimetry Verification

To assure the accuracy and safety of radiation delivery, it is highly recommended to perform pretreatment verification for complex treatment methods such as intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) to detect any potential errors in the treatment planning process and machine deliverability. It is expected that a qualified medical physicist is aware of the underlying scientific principles of imaging and therapeutic processes to perform or supervise technical aspects of pretreatment procedures to ensure safe and effective delivery of the treatment. For this purpose, several guidelines have been published to help direct medical physicists to evaluate the accuracy of treatment planning system (TPS) in the calculation of radiation dose, and dosimetry equipment to avoid possible errors. This will require a clear understanding of abilities as well as the limitations of each TPS, the dosimetry equipment at hand, and the gamma index to perform a comprehensive pre-treatment verification

Flattened photon beams, an obsolete feature in modern linear accelerators

Background: With the advent of Intensity Modulated Radiotherapy (IMRT) and recently, Volumetric Modulated Arc Therapy (VMAT), treatment planning using Flattening Filter Free (FFF) beams can meet all of the energy requirements in radiation therapy clinics. Manufacturers of linear accelerators no longer need to install a flattening filter (FF) in gantry head. This study aims to provide evidence of the superiority of FFF to FF through both dosimetric measurements and clinical treatment plans. Materials and Methods: A 50x50x50cm3 water phantom was created in the RayStation treatment planning system (TPS) for dosimetry comparisons. Flat beam profiles were generated using FFF beam through an optimization process for 10x10 to 30x30cm2 field sizes. Next, a comparison of treatment plans was made using 21 Head and Neck and 14 Lung/Mediastinum treatment sites using 6MV and 6MV-FFF beams. Results: Using FFF beams, profiles with flatness and symmetry identical to or better than those of the flattened beams were produced. At the very edge of the optimized plans for FFF beams, horns had the highest gamma index deviation <1.5% of the normalized dose. For clinical plans evaluated, most of the mean doses to organs_atrisk (OAR) volumes receiving 5% to 30% of the prescription dose were reduced with FFF beams. Conclusion: These results indicate the feasibility of delivering flat beams with FFF quality and producing treatment plans with equal or higher qualities in PTV coverage while achieving better sparing of OAR which will allow escalation of target dose if desired. Plus, removing FF will simplify the gantry head and reduces quality assurance and machine maintenance efforts.Comment: 6 pages, 10 Figures, 2 Tables. International Journal of Radiation Research, October 202

Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

Objectives: Distinguishing between radiation necrosis(RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. Methods: 86 patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification(RFC), logistic regression, and support vector classification(SVC) were trained and subsequently validated using test set. The classification performance was measured by area under the curve(AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: The best performance was achieved using RFC with a Gradient filter (AUC=0.910, std=0.047), (accuracy 0.8, std=0.071), (sensitivity=0.796 std=0.055), (specificity =0.922, std=0.059). For SVC the best result obtains using wavelet_HHH with a high AUC of 0.890 with std=0.89, accuracy of 0.777 with std=0.062, sensitivity=0.701, std=0.084, and specificity=0.85 with std=0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882 & std=0.051, accuracy of 0.753 & std=0.08, sensitivity=0.717 & std=0.208, and specificity=0.816 with std=0.123. Conclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.Comment: 10 pages, 4 Figures, 2 Tables. American Journal of Clinical Oncology, August 202

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table