Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors

Abstract Purpose To correlate radiation dose to the risk of severe radiologically-evident radiation-induced lung injury (RRLI) using voxel-by-voxel analysis of the follow-up computed tomography (CT) of patients treated for lung cancer with hypofractionated helical Tomotherapy. Methods and materials The follow-up CT scans from 32 lung cancer patients treated with various regimens (5, 8, and 25 fractions) were registered to pre-treatment CT using deformable image registration (DIR). The change in density was calculated for each voxel within the combined lungs minus the planning target volume (PTV). Parameters of a Probit formula were derived by fitting the occurrences of changes of density in voxels greater than 0.361 g cm −3 to the radiation dose. The model’s predictive capability was assessed using the area under receiver operating characteristic curve (AUC), the Kolmogorov-Smirnov test for goodness-of-fit, and the permutation test (P test ). Results The best-fit parameters for prediction of RRLI 6 months post RT were D 50 of 73.0 (95% CI 59.2.4–85.3.7) Gy, and m of 0.41 (0.39–0.46) for hypofractionated (5 and 8 fractions) and D 50 of 96.8 (76.9–123.9) Gy, and m of 0.36 (0.34–0.39) for 25 fractions RT. According to the goodness-of-fit test the null hypothesis of modeled and observed occurrence of RRLI coming from the same distribution could not be rejected. The AUC was 0.581 (0.575–0.583) for fractionated and 0.579 (0.577–0.581) for hypofractionated patients. The predictive models had AUC>upper 95% band of the P test . Conclusions The correlation of voxel-by-voxel density increase with dose can be used as a support tool for differential diagnosis of tumor from benign changes in the follow-up of lung IMRT patients.