Weekly changes in ventilation response for photon and proton lung cancer patients during radiotherapy

Conformal dose distributions in proton radiotherapy promise to reduce normal tissue toxicity such as radiation-induced pneumonitis, but this has not been fully realized in clinical trials. To further investigate dose and toxicity, we employ voxel-based normal tissue evaluation techniques such as ventilation maps throughout treatment. We hypothesize that ventilation change after 1 week of treatment (WK1) predicts for ventilation change at the end of treatment (EOT).

For 48 photon and 23 proton lung cancer patients, 4DCT-based ventilation maps were generated using stress-based methods at planning, WK1, and EOT. Voxel-wise ventilation change from planning to WK1 and EOT was calculated and binned by planned dose, and median ventilation change at WK1 and EOT was calculated across all patients in each dose bin. Patients were stratified into 6 groups based on modality and increased, decreased, or stable ventilation at WK1. Mann-Whitney U tests were performed to determine if median ventilation change at WK1 and EOT in each dose bin was significantly different from zero. Univariate analysis was performed to correlate ventilation change at EOT with change at WK1 and other clinical factors. A linear regression model was developed to predict ventilation at EOT using a variety of input features including ventilation at planning, ventilation at WK1, tumor response information, and tumor location. Accuracy of the model was assessed through R².

For patients that decreased in ventilation at WK1, 90% of photon patients and 92% of proton patients were stratified similarly at EOT. Patients that were stratified as increased ventilation at WK1 were stratified similarly (72% for photon, 80% for proton) at EOT. These patients were more likely to develop Grade 2+ pneumonitis though the difference was not significant when computing a Fisher's exact test. Univariate analysis indicated that only ventilation change at WK1 was correlated with ventilation change at EOT. The linear regression model achieved R² of 0.65.

Ventilation changes at EOT can be predicted using ventilation information from planning and WK1. Patients that increased in ventilation at WK1 were more likely to develop pneumonitis. Further work is needed to characterize the relationship between ventilation change with pneumonitis development.