Modeling the impact of out-of-phase ventilation on normal lung tissue response to radiation dose.

PURPOSE: To create a dose-response model that predicts lung ventilation change following radiation therapy, and examine the effects of out-of-phase ventilation.
METHODS: The dose-response model was built using 27 human subjects who underwent radiation therapy (RT) from an IRB-approved trial. For each four-dimensional computed tomography, two ventilation maps were created by calculating the N-phase local expansion ratio (LERN ) using most or all breathing phases and the 2-phase LER (LER2 ) using only the end inspiration and end expiration breathing phases. A polynomial regression model was created using the LERN ventilation maps pre-RT and post-RT and dose distributions for each subject, and crossvalidated with a leave-one-out method. Further validation of the model was performed using 15 additional human subjects using common statistical operating characteristics and gamma pass rates.
RESULTS: For voxels receiving 20 Gy or greater, there was a significant increase from 52% to 59% (P = 0.03) in the gamma pass rates of the LERN model predicted post-RT Jacobian maps to the actual post-RT Jacobian maps, relative to the LER2 model. Additionally, accuracy significantly increased (P = 0.03) from 68% to 75% using the LERN model, relative to the LER2 model.
CONCLUSIONS: The LERN model was significantly more accurate than the LER2 model at predicting post-RT ventilation maps. More accurate post-RT ventilation maps will aid in producing a higher quality functional avoidance treatment plan, allowing for potentially better normal tissue sparing.