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Radiobiologically equivalent deformable dose mapping for re-irradiation planning: Implementation, robustness, and
Juan A García-Alvarez1, Eric Paulson1, Kristofer Kainz1
1Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI 53226, United States.
Background:
Re-irradiation in radiotherapy presents complexities that require dedicated tools to generate optimal re-treatment plans. This study presents a robust workflow that considers fractionation size, anatomical variations between treatments, and cumulative bias doses to improve the re-irradiation planning process.
Methods:
The workflow was automated in MIM® Software and the Elekta© Monaco® treatment planning system. Prior treatment doses are deformably mapped, converted to equivalent dose in 2 Gy fractions (EQD2), and accumulated onto the re-treatment planning CT. Two MIM extensions were developed to estimate voxel-wise dose mapping uncertainties and to convert the cumulative EQD2 into a physical dose distribution equivalent to the re-treatment fractionation size. This dose distribution is used in Monaco as bias to optimize the re-irradiation plan. The workflow was retrospectively tested with data from 14 patients, and the outcomes were compared to the manually optimized plans (MOPs) clinically utilized.
Results:
Bias-dose guided plans (BDGPs) demonstrated a median reduction of the critical organ at risk (OAR) cumulative EQD2 metrics of 240 cGy (range: 1909 cGy, -187 cGy, p = 0.002). BDGPs allowed higher target coverage in cases where the MOP approach implied dose de-escalation of the target. The dose mapping uncertainties resulted in OAR cumulative EQD2 metrics increments ranging from 10 cGy to 730 cGy.
Conclusions:
We introduced a re-irradiation planning workflow using commercially available software that accounts for anatomic and fraction size variations and improves planning efficiency. Employing voxel-level bias dose guidance demonstrated OAR-sparing benefits while maximizing prescription dose coverage to targets. The workflow's robustness tools aid informed clinical decision-making.
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