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Purpose: The health care industry has many stakeholders who seek relationships within government to ensure certain interests are considered in the legislative process. This study characterized the federal political contributions of US radiation oncologists (ROs).Methods and Materials: Public finance data from the Federal Election Commission (FEC) containing self-identified ROs' contributions to a federal candidate or committee were obtained from 2003 to 2018. Contribution recipients were classified as a political action committee (PAC) or a presidential, Senate, or House candidate. Political parties of recipients were based on FEC designations. For PACs, political parties of recipients were based on spending patterns to political parties in each 2-year election cycle or, when unavailable, overall spending patterns of greater than 50% to 1 political party, as detailed by the Center for Responsive Politics. Data were analyzed temporally, and Spearman's rho was used to assess trends.Results: From 2003 to 2018, the FEC reported a total of 31,646,000 federal political contributions. Exactly 4617 federal political contributions were made from 1021 unique self-identified ROs, totaling $3,350,747. The number of ROs making contributions ranged from 56 in 2005 to 600 in 2016 (mean, 289 ± 191.4 ROs yearly), with a mean annual contribution of $209,422. Of all RO dollars, 61.5% went to Democrats, 37.5% to Republicans, and 1.0% to third-party candidates. Most RO dollars (81.0%) went to PACs rather than candidates, with the majority of those PAC dollars (50.2%) directed toward the American Society for Radiation Oncology Political Action Committee. There was a positive annual trend in number of donors by year (ρ = 0.83, P < .0001) as well as RO contributions to the House (ρ = 0.58, P < .02), Senate (ρ = 0.58, P < .02), Democrats (ρ = 0.81, P < .001), Republicans (ρ = 0.66, P < .006), third-parties (ρ = 0.87, P < .001), PACs (ρ = 0.85, P < .0001), and overall (ρ = 0.8, P < .001). There was not a significant trend in contributions to presidential candidates (ρ = 0.71, P = .06).Conclusions: ROs' federal political contributions have significantly increased over the last decade and a half. This growth overwhelmingly represents contributions to specialty-focused PACs and both Democratic and Republican congressional candidates. [ABSTRACT FROM AUTHOR]

Accurate target delineation/contouring is essential for radiation treatment planning and the clinical efficacy of radiation therapy. Clinical trial data has demonstrated the importance of quality contouring and treatment planning for optimal survival outcomes. As a result, improving the quality of target delineation is an important goal in the education of radiation oncology residents. However, there is limited quantitative data on the quality of residents' contours. Therefore, it would be beneficial to track performance and improvement in resident target delineation during residency. The purpose of this study was to determine if it was feasible to track the concordance of radiation oncology residents' contours with faculty physicians' contours. Residents were asked to contour target volumes (GTV, CTV, ITV, PTV, etc.) based on patient history, physical exam, clinical stage and fused diagnostic imaging and were allowed to use any available outside resources, including textbooks, review articles, consensus guidelines, and online atlases. Resident contours were saved as separate structures. Finalized, faculty physician-approved contours were also saved. Saved contour structures and data from October 2019 through June 2020 were reviewed for feasibility. In total, 209 structures had both resident and faculty versions saved within Eclipse and were available for analysis. The Boolean Operations Tool in Eclipse (Varian Medical Systems, Palo Alto, CA, USA) was used to create an intersection volume of the resident/faculty contours. Separately, the Boolean Operations Tool was used to create a union volume of the resident/faculty contours. Based on these two volumes, the Jaccard Concordance Index (JCI) was calculated by dividing the intersection volume (in cubic centimeters [cc]) by the union volume (cc). The JCI could be successfully calculated for 203 (97.1%) of the 209 structures. For 6 structures (2.9%), both the intersection and union volumes could not be calculated because the volumes were too small for Eclipse to determine. All 6 of these structures were small brain metastases. Tracking and comparing the concordance of resident contours and faculty physician contours is feasible using available tools in Eclipse. Additional data collection and assessment is necessary before this technique can be more widely utilized. [ABSTRACT FROM AUTHOR]