Onboard magnetic resonance imaging (MRI) guided radiotherapy is now clinically available in nine centers in the world. This technology has facilitated the clinical implementation of online adaptive radiotherapy (OART), or the ability to alter the daily treatment plan based on tumor and anatomical changes in real-time while the patient is on the treatment table. However, due to the time sensitive nature of OART, implementation in a large and busy clinic has many potential obstacles as well as patient-related safety considerations. In this work, we have described the implementation of this new process of care in the Department of Radiation Oncology at the University of California, Los Angeles (UCLA). We describe the rationale, the initial challenges such as treatment time considerations, technical issues during the process of re-contouring, re-optimization, quality assurance, as well as our current solutions to overcome these challenges. In addition, we describe the implementation of a coverage system with a physician of the day as well as online planners (physicists or dosimetrists) to oversee each OART treatment with patient-specific ‘hand-off’ directives from the patient’s treating physician. The purpose of this effort is to streamline the process without compromising treatment quality and patient safety. As more MRI-guided radiotherapy programs come online, we hope that our experience can facilitate successful adoption of OART in a way that maximally benefits the patient.

A B S T R A C T PurposeThe Cancer and Leukemia Group B (CALGB) C9343 trial found that adjuvant radiation therapy (RT) provided minimal benefits for older women with breast cancer. Although treatment guidelines were changed to indicate that some women could forego RT, the impact of the C9343 results on clinical practice is unclear. Patients and MethodsWe used the Surveillance, Epidemiology, and End Results (SEER) -Medicare data set to assess the use of adjuvant RT in a sample of women Ն 70 years old diagnosed with stage I breast cancer from 2001 to 2007 who fulfilled the C9343 inclusion criteria. We used log-binomial regression to estimate the relation between publication of C9343 and use of RT in the full sample and across strata of patient and health system characteristics. ResultsOf the 12,925 Medicare beneficiaries in our sample (mean age, 77.7 years), 76.5% received RT. Approximately 79% of women received RT before study publication compared with 75% after (adjusted relative risk of receiving RT postpublication v prepublication: 0.97; 95% CI, 0.95 to 0.98). Although use of RT was lower after the trial within all strata of age and life expectancy, the magnitude of this decrease did not differ significantly by strata. For instance, among patients with life expectancy less than 5 years, RT use decreased by 3.7%, from 44.4% prepublication to 40.7% postpublication. Among patients with life expectancy Ն 10 years, RT use decreased by 3.0%, from 92.0% to 89.0%. ConclusionThe C9343 trial had minimal impact on the use of RT among older women in the Medicare population, even among the oldest women and those with shorter life expectancies.

Sources of support: This work was funded by the American Society for Radiation Oncology. Task Force Members' Disclosure Statements: All task force members' disclosure statements were shared with other task force members throughout the guideline's development. Those disclosures are published within this report. Where potential conflicts were detected, remedial measures to address them were taken.

Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy.A retrospective study was performed using CT images and 0.35T MR images of 12 patients with liver (n=8) and non-liver abdominal (n=4) cancer. CT images were deformably registered to the corresponding MR images to generate deformed CT (dCT) images for treatment planning. Both cGAN and cycleGAN were trained using MR and dCT transverse slices. Four-fold cross-validation testing was conducted to generate sCT images for all patients. The HU prediction accuracy was evaluated by voxel-wise similarity metric between each dCT and sCT image for all 12 patients. dCT-based and sCT-based dose distributions were compared using gamma and dose-volume histogram (DVH) metric analysis for 8 liver patients. sCTcycleGAN achieved the average mean absolute error (MAE) of 94.1 HU, while sCTcGAN achieved 89.8 HU. In both models, the average gamma passing rates within all volumes of interest were higher than 95% using a 2%, 2 mm criterion, and 99% using a 3%, 3 mm criterion. The average differences in the mean dose and DVH metrics were within ±0.6% for the planning target volume and within ±0.15% for evaluated organs in both models.Results demonstrated that abdominal sCT images generated by both cGAN and cycleGAN achieved accurate dose calculation for 8 liver radiotherapy plans. sCTcGAN images had smaller average MAE and achieved better dose calculation accuracy than sCTcyleGAN images. More abdominal patients will be enrolled in the future to further evaluate two models. Keywords: Generative adversarial network, Synthetic CT, MR-guided radiotherapy planning workflows for pelvic or abdominal cancer radiotherapy (Villeirs et al 2005, Lim et al 2011, Heerkens et al 2017, Mittauer et al 2018. Since there is no direct relationship between MR intensity values and electron densities, the standard MR-guided radiotherapy workflow still requires the acquisition of a CT image for dose calculation. However, registration between CT and MR images for transferring target delineations introduces systematic uncertainties that propagate throughout the treatment (Edmund and Nyholm 2017). Acquiring an additional CT image also increases unwanted radiation exposure, clinical workload, and financial cost (Karlsson et al 2009). MR-only radiotherapy can avoid these downsides.A few methods have been proposed to generate synthetic CT (sCT) images from MR images. These methods include atlas-based methods, voxel-based methods, and hybrid methods (Edmund and Nyholm 2017). In atlas-based methods (Sjölund et al 2015, Dowling et al 2015, the target MR image was first deformably registered to atlas-MR images to acquire deformation vector fields. The acquired vector fi...