Abstract

Purpose:Ventilation change caused by radiation therapy (RT) can be predicted using four‐dimensional computed tomography (4DCT) and image registration. This study tested the dependency of predicted post‐RT ventilation on effort correction and pre‐RT lung function.Methods:Pre‐RT and 3 month post‐RT 4DCT images were obtained for 13 patients. The 4DCT images were used to create ventilation maps using a deformable image registration based Jacobian expansion calculation. The post‐RT ventilation maps were predicted in four different ways using the dose delivered, pre‐RT ventilation, and effort correction. The pre‐RT ventilation and effort correction were toggled to determine dependency. The four different predicted ventilation maps were compared to the post‐RT ventilation map calculated from image registration to establish the best prediction method. Gamma pass rates were used to compare the different maps with the criteria of 2mm distance‐to‐agreement and 6% ventilation difference. Paired t‐tests of gamma pass rates were used to determine significant differences between the maps. Additional gamma pass rates were calculated using only voxels receiving over 20 Gy.Results:The predicted post‐RT ventilation maps were in agreement with the actual post‐RT maps in the following percentage of voxels averaged over all subjects: 71% with pre‐RT ventilation and effort correction, 69% with no pre‐RT ventilation and effort correction, 60% with pre‐RT ventilation and no effort correction, and 58% with no pre‐RT ventilation and no effort correction. When analyzing only voxels receiving over 20 Gy, the gamma pass rates were respectively 74%, 69%, 65%, and 55%. The prediction including both pre‐ RT ventilation and effort correction was the only prediction with significant improvement over using no prediction (p<0.02).Conclusion:Post‐RT ventilation is best predicted using both pre‐RT ventilation and effort correction. This is the only prediction that provided a significant improvement on agreement.Research support from NIH grants CA166119 and CA166703, a gift from Roger Koch, and a Pilot Grant from University of Iowa Carver College of Medicine

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