Faculty Collaboration Database

Radiobiologically equivalent deformable dose mapping for re-irradiation planning: Implementation, robustness, and dosimetric benefits. Radiother Oncol 2025 Apr;205:110741

Date

01/25/2025

Pubmed ID

39855600

DOI

10.1016/j.radonc.2025.110741

Scopus ID

2-s2.0-85216209940 (requires institutional sign-in at Scopus site)

Abstract

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.

Author List

García-Alvarez JA, Paulson E, Kainz K, Puckett L, Shukla ME, Zhu F, Gore E, Tai A

Authors

Juan A. Garcia Alvarez PhD Medical Physicist Assistant II in the Radiation Oncology department at Medical College of Wisconsin
Elizabeth M. Gore MD Professor in the Radiation Oncology department at Medical College of Wisconsin
Kristofer Kainz PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
Eric Paulson PhD Chief, Professor in the Radiation Oncology department at Medical College of Wisconsin
Lindsay L. Puckett MD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
Monica E. Shukla MD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin
An Tai PhD Associate Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Humans
Organs at Risk
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Re-Irradiation
Retrospective Studies
Software
Workflow