S2023
Physics - Dose prediction/calculation, optimisation and applications for photon and electron planning
ESTRO 2026
( α / β = 2–3 Gy for serial OARs) using a validated in- house Python tool. Cumulative D0.03 cc were compared across strategies, with registration accuracy evaluated both visually and quantitatively using target registration error and local deformation Jacobians. Results: RIR and the bias-based mapping approach yielded closely matching cumulative D0.03 cc values ( Δ <3%) for rigid structures such as the brainstem and optic apparatus, while the bias method allowed direct visualization of total physical dose within Monaco and allowed lower OAR doses than other strategies, which were optımızatıon based on EQD2 allowance doses. For lung and pelvis cases, DIR provided improved anatomical conformity and more realistic dose transfer, reducing local D0.03 cc deviations by approximately 5–10% compared with RIR. Structure- based adaptation enhanced contour alignment but introduced up to 7% voxel-dose uncertainty near tissue boundaries. Conclusion: The integration of Monaco’s registration tools with an external EQD2-based accumulation workflow enables accurate, radiobiologically guided dose mapping for reRT. The bias-based RIR method supports intuitive physical dose visualization within the TPS, while DIR improves anatomical and dosimetric accuracy for deformable sites. This combined framework enhances cumulative dose assessment and supports safer, individualized re-irradiation planning. Keywords: ReRT, Dose Mapping, Image Registration Digital Poster 5062 Dosimetric feasibility of upfront "Brachy-like" SBRT with MRgRT followed by 40Gy/20F chemoradiation for cervical cancer: UPFRONTCx study Sawanya Suwandee 1 , Paritt Wongtrakool 1 , Tissana Prasartseree 1,2 , Eun Young Han 3 , Belinda M Lee 3 , Satawatchara Suwanprateep 1 , Natcha Nuchsirikulaphong 1 , Sajjaporn Pojai 1 , Napatsorn Thumyongkit 1,4 , Tanwiwat Jaikuna 1,4 , Pitchayut Nakkrasae 1,4 , Wiwatchai Sittiwong 1,4 , Christopher L Spicer 5 , Jinzhong Yang 5 , Zhiqian Henry Yu 5 , Ann H Klopp 6 , Yusung Kim 7 , Pittaya Dankulchai 1,4 1 Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 2 Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3 Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. 4 Siriraj Brachytherapy Center (SiBTC), Siriraj Center of Excellence (SiCOE), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. 5 Department of
simulation was performed and given the estimated higher doses in the abdominal region, one fraction was omitted for the isocenters that intersect the abdomen, namely the chest-abdomen isocenter and pelvis isocenter. The final estimated dose across all measured sites was 102.7±4.6% of the prescription dose. Conclusion: VMAT TSQI is feasible but extremely challenging due to difficult optimization and alignment of large annular volumes. Further investigations into robustness and methods to efficiently handle adaptations to anatomical changes throughout the treatment course are needed. Keywords: Total Subcutaneous Irradiation Clinical Implementation of Image Registration Strategies for Dose Mapping in Re-Irradiation Settings Using Monaco TPS Solutions Cemile Ceylan 1,2 , Artunç Osman Türe 1 , Alper Halil Özkan 1 1 Radiation Oncology, Istanbul oncology Hospıtal, Istanbul, Turkey. 2 Faculty of Health Sciences, Yeditepe University, Istanbul, Turkey Purpose/Objective: Re-irradiation (reRT) has become an increasingly relevant treatment option for patients with Digital Poster 5057 locoregional recurrence in previously irradiated sites. Anatomical changes between treatment courses and the absence of radiobiology-aware tools in most treatment planning systems (TPS) often limit reliable dose accumulation. This study aimed to implement and evaluate multiple image registration strategies in the Monaco TPS to obtain biologically consistent cumulative dose maps for reRT planning, supported by an in-house Python-based EQD2 conversion and
analysis workflow. Material/Methods:
Eight reRT cases treated between 2018 and 2024 were retrospectively analyzed. The cohort consisted of eight reRT cases retrospectively selected, including two lung, three brain (two gliomas and one metastasis following whole-brain RT and SRS), two head and neck, and one pelvic nodal case after prior rectal and pelvic lymph node irradiation. CT datasets from both RT courses were registered using four approaches: (1) rigid image registration (RIR), (2) deformable image registration (DIR), (3) structure-based adaptation via Monaco’s Adapt Anatomy option, and (4) a bias-based RIR strategy in which the reRT CT served as a phantom for mapping the total cumulative physical dose from the first treatment directly onto the second CT. All prior dose distributions were converted voxel-wise to EQD2
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