Poster — Thur Eve — 46: Image‐Based Scoring of Radiation Injury in Lung: Analysis of Sources of Uncertainties

Abstract

We are studying the robustness and uncertainties of an automated method for quantifying radiotherapy‐induced lung injury from CT images and delineate its relationship with radiation dose at different post‐radiation (post‐RT) time points. Methods: Using multi‐resolution affine optimization technique, post‐RT diagnostic CT images were registered to planning CT images. Following the registration and patient tissue‐based CT calibration, a change in physical density at each voxel position of the planning CT was evaluated and voxels which density change is considered pathological were segmented as injury. Retrospective dose calculations using anisotropic analytical algorithm (AAA) and Monte‐Carlo (MC) were performed. The segmented injury was spatially correlated to the dose distributions to deduce a patient‐specific dose‐response relationship for radiation‐induced injury. Results: We found the probability of injury as a function of dose and post‐treatment time was patient‐specific. Due to the inaccuracy of the affine registration, the injury segmentation was manually corrected for the misalignment of normal tissue features, which gave rise to a case‐dependent uncertainty of up to 10%. Inter‐patient variability in CT calibration contributed 4% or less to the uncertainty on the probability. Finally, dose calculation from MC simulation occasionally yielded a significantly modified complication probability compared to AAA model suggesting that dose calculation accuracy is important for the investigation on dose‐response of lung injury. Conclusion: The presented method provided a quantitative approach for dose‐response analysis in normal lung tissues if the accuracy in image registration and dose calculation can be assured and will provide better options to complication‐driven treatment planning.