S2608
Radiobiology - Radiobiological modelling
ESTRO 2026
Conclusion: This computational evaluation demonstrates how the PDAC-on-chip model can serve as a relevant radiobiological model for studying the influence of the ECM on tumour radiotherapy and chemotherapy treatments. Coupling these computational tools with the on-chip platform expands the possible assessments, and mechanistic interpretations of experiments aimed at evaluating PDAC radio and chemoresistance. Acknowledgements: This work was funded by 5xmille funds for healthcare research (Ministry of Health) References: 1) Pandol et al. Clinical Gastroenterology and Hepatology, 7 (2009) 2)Possenti et al. Radiotherapy and Oncology 182, S57–S58 (2023) 3) Possenti et al. Computers in Biology and Medicine, Volume 173 (2024), 108334 4)Ghaffarizadeh et al. PLoS Comput. Biol. 14(2): e1005991 (2018) 5) Randy et al. Gigabyte(2024) 6)Wenzl et al. Phys Med Biol. (2011); 56(11):3251-68 Keywords: desmoplasia, hypoxia, computational modelling
Digital Poster 5058
Digital Twins in Radiation Oncology: dynamic adaptation of dose and dose fractionation based on mathematical modeling of patient-specific responses Mohammad U Zahid 1 , Robert Warren Floyd 1 , Mai Mandor Badawy 1 , Amy Catherine Moreno 1 , David I Rosenthal 1 , Steven J Frank 2 , Anna Lee 1 , Kristy K Brock 3 , Jimmy J Caudell 4 , Heiko Enderling 1 1 Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA. 2 GU Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA. 3 Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, USA. 4 Radiation Oncology, H. Lee Moffitt Cancer Center, Tampa, USA Purpose/Objective: To develop a digital twin platform to simulate virtual clinical trials of mid-treatment patient-specific radiation dose and dose fractionation adaptation. Material/Methods: We developed an ordinary differential equation model of oropharyngeal cancer growth and radiation response dynamics. The model parameters were calibrated and validated on retrospective data of patients treated with 2Gy once daily radiation (QD) or 1.2Gy twice daily hyperfractionation (HFx). We developed an adaptive Bayesian framework that updates treatment response predictions for individual patients on a weekly basis as new radiographic response measurements become available. Results: Model prediction for locoregional control and disease-free survival reach accuracy for clinical actionability after 3 weeks or radiation. Simulations of dose escalation and dose de-escalation based on early treatment response dynamics, as well as simulations of adaptive radiation fractionation indicate improved locoregional control with lower radiation dose for the majority of patients. Conclusion: Dynamic adaptation of radiation dose and dose-fractionation based on individual treatment response characteristics offer a novel angle for personalized radiotherapy. Digital twin solutions for radiation oncology are technically feasible and promising. Prospective clinical trials will need to validate these findings before adaptation into individualized clinical decision making. References: Zahid, M. U. et al. Forecasting Individual Patient Response to Radiotherapy in Head and Neck Cancer with a Dynamic Carrying Capacity Model. Int. J. Radiat. Oncol. Biol. Phys. (2021)Zahid, M. U. et al. Proliferation Saturation Index to Characterize Response to
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