Task Group 101 of the AAPM has prepared this report for medical physicists, clinicians, and therapists in order to outline the best practice guidelines for the external-beam radiation therapy technique referred to as stereotactic body radiation therapy (SBRT). The task group report includes a review of the literature to identify reported clinical findings and expected outcomes for this treatment modality. Information is provided for establishing a SBRT program, including protocols, equipment, resources, and QA procedures. Additionally, suggestions for developing consistent documentation for prescribing, reporting, and recording SBRT treatment delivery is provided.

The MCS allows for a quantitative assessment of plan complexity, on a fixed scale, that can be applied to all treatment sites and can provide more information related to dose delivery than simple beam parameters. This could prove useful throughout the entire treatment planning and QA process.

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Refinement of radiomic results and methodologies is required to ensure progression of the field. In this work, we establish a set of safeguards designed to improve and support current radiomic methodologies through detailed analysis of a radiomic signature. Methods: A radiomic model (MW2018) was fitted and externally validated using features extracted from previously reported lung and head and neck (H&N) cancer datasets using gross-tumour-volume contours, as well as from images with randomly permuted voxel index values; i.e. images without meaningful texture. To determine MW2018's added benefit, the prognostic accuracy of tumour volume alone was calculated as a baseline. Results: MW2018 had an external validation concordance index (c-index) of 0.64. However, a similar performance was achieved using features extracted from images with randomized signal intensities (c-index = 0.64 and 0.60 for H&N and lung, respectively). Tumour volume had a c-index = 0.64 and correlated strongly with three of the four model features. It was determined that the signature was a surrogate for tumour volume and that intensity and texture values were not pertinent for prognostication. Conclusion: Our experiments reveal vulnerabilities in radiomic signature development processes and suggest safeguards that can be used to refine methodologies, and ensure productive radiomic development using objective and independent features.

Kilovoltage cone-beam computerized tomography (kV-CBCT) systems integrated into the gantry of linear accelerators can be used to acquire high-resolution volumetric images of the patient in the treatment position. Using on-line software and hardware, patient position can be determined accurately with a high degree of precision and, subsequently, set-up parameters can be adjusted to deliver the intended treatment. While the patient dose due to a single volumetric imaging acquisition is small compared to the therapy dose, repeated and daily image guidance procedures can lead to substantial dose to normal tissue. The dosimetric properties of a clinical CBCT system have been studied on an Elekta linear accelerator (Synergy RP, XVI system) and additional measurements performed on a laboratory system with identical geometry. Dose measurements were performed with an ion chamber and MOSFET detectors at the center, periphery, and surface of 30 and 16-cm-diam cylindrical shaped water phantoms, as a function of x-ray energy and longitudinal field-of-view (FOV) settings of 5,10,15, and 26 cm. The measurements were performed for full 360 degrees CBCT acquisition as well as for half-rotation scans for 120 kVp beams using the 30-cm-diam phantom. The dose at the center and surface of the body phantom were determined to be 1.6 and 2.3 cGy for a typical imaging protocol, using full rotation scan, with a technique setting of 120 kVp and 660 mAs. The results of our measurements have been presented in terms of a dose conversion factor fCBCT, expressed in cGy/R. These factors depend on beam quality and phantom size as well as on scan geometry and can be utilized to estimate dose for any arbitrary mAs setting and reference exposure rate of the x-ray tube at standard distance. The results demonstrate the opportunity to manipulate the scanning parameters to reduce the dose to the patient by employing lower energy (kVp) beams, smaller FOV, or by using half-rotation scan.

Abstract. Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-pervoxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31). The novel dose prediction method with dose mimicking can generate deliverable treatment plans in 12-13 minutes without any user interaction. The method is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

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