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  5. Automated Biological Dose Conversion with Predictive OAR Tolerances

Automated Biological Dose Conversion with Predictive OAR Tolerances

B Morris1*, S Petro2, V Coffman3, K Lai4, L Rigsby5, M Leyva6, A Gandhi7, S Hedrick8, (1) Provision Center for Proton Therapy, Knoxville, TN, (2) Provision Center for Proton Therapy, Knoxville, TN, (3) Provision Center for Proton Therapy, ,,(4) Provision Center for Proton Therapy, Knoxville, TN, AF, (5) Provision Center for Proton Therapy, Knoxville, TN, (6) Provision Center for Proton Therapy, Knoxville, TN, (7) Provision Center for Proton Therapy, Knoxville, TN, (8) Provision Center for Proton Therapy, Knoxville, TN

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose: Due to the push towards hypofractionation and the prevalence of retreatment in radiation oncology, biological dose conversion has become necessary and more frequent. Performing these conversions needs to be accurate and efficient to facilitate safe treatment and prevent delays. We developed a tool to perform automated biological dose conversion for planning and intercomparison of dose schemes.


Methods: A spreadsheet was designed that automatically calculates Biologically Equivalent Dose (BED) and Equivalent Dose in 2 Gy Fractions (EQD2) for multiple dose schemes and phases. The user can use these calculations to compare dose schemes and/or convert dose distributions to the desired regimen. The tool also estimates the adjusted dose tolerances with a given fractionation scheme that correspond to the conventional fractionation tolerances. The allows the planner to create hypo- or hyper-fractionation objectives that will result in conventional tolerances being met after the final plan dose is converted to EQD2. If prior dose has been delivered, the user is provided an estimate of the remaining dose tolerance under the specified fractionation scheme.


Results: On average, the time required for biological dose conversions was reduced by 38%. No mistakes were found upon review of the calculations generated by the spreadsheet, compared with one mistake found in the manually calculated dose conversions.


Conclusion: The biological dose conversion tool is an efficient and reliable method for calculating a wide range of doses, and it was implemented as the standard dose conversion modality for our clinic. The tool decreased the average time required to derive converted doses and empowered the dosimetry team to confidently plan with accurate optimization objectives across various dose regimens. In the future, we will build a GUI interface which will also allow the user to convert dose distributions to BED or EQD2 on a voxel-by-voxel basis.

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