| Voxel-wised EQD2 calculation with Python programming language in the setting of Re-irradiation. |
| Paper ID : 1194-IGA |
| Authors |
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Sahar Heidary *1, cemile ceylan2, mohammad hasani3 1Yeditepe University 2Yeditepe University, Istanbul Oncology Hospital 3Octopus energy |
| Abstract |
| This study aims to develop a Python-based methodology for voxel-wise calculation of Equivalent Dose in 2 Gy fractions (EQD2) to support re-irradiation (ReRT) planning. The main objectives are to transform physical dose data from the initial course of radiotherapy (course 1) into EQD2 and to calculate the remaining safe dose limits for subsequent courses (course 2 or 3) to optimize treatment while ensuring patient safety. Patient dose-volume histogram (DVH) data from treatment planning systems (TPS) were imported into Python to calculate EQD2 voxel-by-voxel. The initial physical dose D was converted using the equation (eq. A1). EQD2=D⋅((d+α\/β)/(2+α\/β)) d is the dose per fraction, and α/β is the tissue-specific sensitivity ratio. An optional adjustment was applied by using (eq. A2) to account for potential tissue recovery between treatments: EQD2_{α/β,original(with recovery)} = (1 - R)⋅EQD2_{α/β,original} Where R represents the assumed recovery proportion. To determine the remaining dose capacity, the calculated EQD2 from course one was subtracted from cumulative dose constraints set by clinical guidelines (e.g., QUANTEC, RTOG) as per the equation (eq. A3). EQD2_{dose remaining} = EQD2_{ cumulative} - EQD2_{original(with recovery)} The remaining safe EQD2 dose was then translated back into a deliverable physical dose for subsequent courses using the equation (eq. A4). D_n=n/2├ ( √({(α/β)^2+ 4/n⋅EQD2_{{dose remaining}} ⋅(2 +α/β)} )-α/β┤) where Dn is the dose per fraction, and n is the number of planned fractions. The Python script successfully performed voxel-wise EQD2 calculations, accurately converting physical doses and calculating remaining safe dose limits for subsequent courses. This method ensures the evaluation of re-irradiation feasibility based on radiobiological parameters and existing dose constraints. The tool provided clear dose-response graphs and comparative visualizations for clinical review, improving treatment planning precision. The developed Python-based program effectively solves EQD2 calculations in re-irradiation planning. |
| Keywords |
| Re-irradiation, Python, EQD2. |
| Status: Accepted |
