Background: Mechanistic modelling of normal tissue toxicities is unfolding as an alternative to the phenomenological normal tissue complication probability models. The latter, currently used in the clinics, rely exclusively on limited patient data and neglect spatial dose distribution information. Among the various approaches, agent-based models are appealing as they provide the means to include patient-specific parameters and simulate long-term effects in complex systems. However, Monte Carlo tools remain the state-of-the-art for modelling radiation transport and provide measurements of the delivered dose with unmatched precision.
Methods: In this work, we develop and characterize a coupled 3D agent-based - Monte Carlo model that mechanistically simulates the onset of the radiation-induced lung fibrosis in an alveolar segment. To the best of our knowledge, this is the first such model.
Results: Our model replicates extracellular matrix patterns, radiation-induced lung fibrosis severity indexes and functional subunits survivals that show qualitative agreement with experimental studies and are consistent with our past results. Moreover, in accordance with experimental results, higher functional subunits survival and lower radiation-induced lung fibrosis severity indexes are achieved when a 5-fractions treatment is simulated. Finally, the model shows increased sensitivity to more uniform protons dose distributions with respect to more heterogeneous ones from photon irradiation.
Conclusions: This study lays thus the groundwork for further investigating the effects of different radiotherapeutic treatments on the onset of radiation-induced lung fibrosis via mechanistic modelling.
Lung cancer leads to a significant number of deaths each year. Radiotherapy is known to be effective in treating lung cancer. However, it can also damage healthy tissue and this limits the dose that can be delivered to the cancer. To estimate the risk of harming healthy tissues in the lung with radiotherapy, mathematical models can be used. We propose a computer-based model to overcome some of the limitations of existing approaches currently used in the clinic. The model incorporates spatial information about the radiation dose and replicates findings observed in mice and humans on lung scarring caused by radiation. With further testing, our model may allow clinicians to better minimize harm to healthy tissues in patients with lung cancer.
© 2024. The Author(s).