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Clinical Comparison of Treatment Plans and Fallback Plans for Machine Downtime

W Cruz

W Cruz1,3*, P Papanikolaou1 , P Mavroidis2 , S Stathakis3 , (1) University of Texas Health Science Center at San Antonio, San Antonio, TX, (2) University of North Carolina, Chapel Hill, NC, (3) Cancer Therapy and Research Center, San Antonio, TX


SU-E-T-173 (Sunday, July 12, 2015) 3:00 PM - 6:00 PM Room: Exhibit Hall

Purpose: The purpose of this study was to determine the clinical effectiveness and dosimetric quality of fallback planning in relation to machine downtime.

Methods: Plans for a Varian Novalis TX were mimicked, and fallback plans using an Elekta VersaHD machine were generated using a dual arc template. Plans for thirty (n=30) patients of various treatment sites optimized and calculated using RayStation treatment planning system. For each plan, a fall back plan was created and compared to the original plan. A dosimetric evaluation was conducted using the homogeneity index, conformity index, as well as DVH analysis to determine the quality of the fallback plan on a different treatment machine. Fallback plans were optimized for 60 iterations using the imported dose constraints from the original plan DVH to give fallback plans enough opportunity to achieve the dose objectives.

Results: The average conformity index and homogeneity index for the NovalisTX plans were 0.76 and 10.3, respectively, while fallback plan values were 0.73 and 11.4. (Homogeneity =1 and conformity=0 for ideal plan) The values to various organs at risk were lower in the fallback plans as compared to the imported plans across most organs at risk. Isodose difference comparisons between plans were also compared and the average dose difference across all plans was 0.12%.

Conclusion: The clinical impact of fallback planning is an important aspect to effective treatment of patients. With the complexity of LINACS increasing every year, an option to continue treating during machine downtime remains an essential tool in streamlined treatment execution. Fallback planning allows the clinic to continue to run efficiently should a treatment machine become offline due to maintenance or repair without degrading the quality of the plan all while reducing strain on members of the radiation oncology team.

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