Predicting end-of-treatment ventilation response to radiotherapy using early treatment ventilation for patients with lung cancer treated with photons and protons.

Abstract

Functional lung avoidance radiotherapy spares high functioning regions of the lung to reduce toxicity risk but disregards functional changes during treatment. To further investigate functional change in the normal lung, we employ voxel-based techniques such as ventilation maps throughout treatment. We hypothesize that ventilation change during the beginning of treatment (BOT) predicts for ventilation change between planning and the end of treatment (EOT). For 71 lung cancer patients, 48 treated with photon radiotherapy and 23 treated with proton radiotherapy, 4-dimensional CT (4DCT)-based ventilation maps were generated using stress-based finite-element methods at planning, BOT, and EOT. Voxel-wise ventilation change at BOT and EOT was calculated. Patients were stratified into 6 groups according to modality (combined and separate) and increased or decreased ventilation at BOT. For each group, ventilation change was binned by planned dose and the median was computed at BOT and EOT across patients. EOT ventilation was correlated with planning ventilation, BOT ventilation, and clinical factors through univariate analysis. A linear regression model was developed to identify predictors of EOT ventilation. Model features included ventilation at planning, ventilation at BOT, and lung volume. Model accuracy was assessed through R². Of the patients with increased ventilation at BOT, 74% (79% of photon patients and 75% of proton patients) were stratified identically at EOT. Of the patients with decreased ventilation at BOT, 83% (86% of photon patients and 82% of proton patients) were stratified identically at EOT. Univariate analysis indicated that only planning ventilation, BOT ventilation, and lung volume was correlated with EOT ventilation. The linear regression model achieved a R² of 0.89. Ventilation change at BOT can predict ventilation change at EOT, demonstrating great potential for using ventilation as an imaging biomarker. Further work is needed to correlate ventilation change with patient-reported outcomes and radiation-induced toxicities such as pneumonitis.