Purpose: To accurately model the MLC in RayStation v3.0.0.251 and achieve acceptable clinical results. Methods: MLC parameters including transmission, leaf tip width and tongue and groove effect were investigated using previously published tests. Transmission and leaf‐tip geometry were assessed with a 2cm × 10cm MLC defined strip test on CAX and +/− 10 cm off axis with 16× 20 jaws, and a step‐wedge (Cadman, et al) irradiating 10 adjacent 1cm × 10cm MLC segments with step and shoot IMRT, while increasing the MU per segment by a fixed value. Conformal MLC shapes included the C‐shape and Squiggle (TG‐53), cross (Xiao, et al) and slanted rectangle (Phillips, et al). RayStation dose calculations were compared to EBT3 film and PTW Octavius detector 729 ion chamber array measurements. Analysis software included FilmQA™ and PTW Verisoft v5.1. All tests were performed on a Varian Trilogy.Additionally, six patient plans were created and IMRT QA was performed as clinical cases. Both split and non‐split field IMRT QA was tested. Patient disease sites included pelvis, prostate, head and neck, and lung. A passing criteria of 2%,2mm was applied. Results: After numerous iterations, the following was determined for both 6MV and 18MV: 0.017 tranmission, 0.05 cm tongue and groove, and 0.200 cm leaf tip width.IMRT analysis for a total 66 fields resulted in a 98.5% (97.2% split, 100% split) overall passing with the 2%,2mm criteria. Conclusion: The MLC model in RayStation v3.0.0.251 is clinically acceptable for patient treatment.
Objective: To develop an instrument for quantifying innovation and assess the diffusion of innovation in radiation oncology (RO) in the United States. Methods: Primary data were collected for using total population convenience sampling. Innovation Score and Innovation Utilization Score were determined using 20 indicators. 240 medical physicists (MPs) practicing in RO in the United States completed a custom Internet-based survey. Results: Centers with no academic affiliation are trailing behind in innovation in total (MD = 1.65, 95% C I[0.38,2.917], p = 0.011, d = 0.351), in patient treatment (MD = 0.39, 95% CI [0.021,0.76], p = 0.038, d = 0.282), and workflow innovation (MD = 7.09, 95% CI [0.78,13.39], p = 0.028, d = 0.330). Centers with no academic affiliation are trailing behind in innovation utilization in total (MD = 0.46, 95% CI [0.05,0.86], p = 0.028, d = 0.188). Rural center are trailing behind in patient positioning in innovation (MD = 0.31, 95% CI [0.011,0.612], p = 0.042, d = 0.293) and innovation utilization (MD = 16.22, 95% CI [0.73,31.72], p = 0.04, d = 0.608). Rural centers are trailing behind in innovative treatments (MD = 0.62, 95% CI [0.23,1.00], p = 0.002, d = 0.457). Motivation (rs = 0.224, p = 0.002) and appreciation (rs = 0.215, p = 0.003) were statistically significant personal factors influencing innovation utilization. Conclusions: There is a wide range of innovation across RO centers in the United States. RO centers in the United States are not practicing as innovative as reasonably achievable. Advances in knowledge: This work quantified how innovative RO in the United States is and results provide guidance on how to improve it in the future.
Purpose: To evaluate the use of a web based error tracking system. Methods: A web based error tracking system called the PIT (Process Improvement Tracking Tool) was used by all staff members to enter errors found at all parts of the treatment process in four satellite facilities. An error is defined as an action that did not fully comply with policy and procedure. Entries were made in real time by all staff members and require that the user enters the date of the incident, the patient medical record number, the physical location of the site, the process during which the error was found and categorize the incident type. Results: Number of errors is approximately proportional to the number of patient treatments. Errors: VBMC 45%, UROC 25%, FROC 13%, PHC 17%. Treatments: VBMC 38%, UROC 32%, FROC 16%, PHC 14%. Staff members making the most mistakes are therapists (36%), dosimetrists (23%) and physicists (15%). Processes where errors were found: physics weekly 25%, therapist weekly 18%, physics second check 15%, physics peer review 6%, therapists check 5%, treatment 5% and various other steps 26%. Conclusion: Proportionality of the number of patients treated to the number of errors implies homogeneity across four satellites. Differences between staff member error percentages need to be investigated more thoroughly, but possible explanations are workload and staffing levels. Step in the process where errors are found implies the need for improved processes upstream. Specifically, 5% errors discovered during treatment is considered very high and a specific attempt to reduce it will be undertaken. The vast majority of the errors discovered during treatment were found before the treatment was delivered. Only four were discovered after delivery and none involved significant dose errors. Further work is required to identify the exact procedures within the department that will achieve this goal.
Purpose: To identify changes in target coverage when clinical practice advances from the GGPB algorithm to the electron Monte Carlo algorithm in Eclipse. Method and Materials: Sixteen patients were planned with electron Monte Carlo. Prescriptions were set to cover percentage of target volume, as clinically acceptable by the physicians, on a case by case basis. Dose volume histogram coverages were noted for PTV and CTV, where applicable. MUs were recorded. The same plan was run with GGPB. Prescription point and line was set to achieve similar dose volume histograms to the electron Monte Carlo plans. Dose volume histogram coverages were noted for PTV and CTV, where applicable. MUs were recorded. The MU derived form the GGPB model were used to calculate an “inverse electron Monte Carlo” plan, namely a plan using the electron Monte Carlo algorithm but calculated with fixed MUs, as they were calculated from the GGPB. Dose volume histogram coverages were noted for PTV and CTV, where applicable. MUs were recorded. Results: The inverse electron Monte Carlo plans give a representative image of clinical practice before the electron Monte Carlo clinical implementation. The overall target coverage was significantly less than previously thought while using the GGPB. This trend increases with the increase in angle of incidence and is more prominent for angles > 15degrees. Further study is necessary in order to evaluate the effects of inhomogeneity. Conclusion: The use of electron Monte Carlo leads to increasingly better tumor coverage. The MUs used are also increasing in the range of 4%–13%, in order to achieve the true desired coverage.
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