Abstract Purpose: Radiation pneumonitis occurs in approximately 10-30\% of lung cancer patients treated with radiation therapy, posing a significant dose-limiting factor. The assessment of regional ventilation changes from functional ventilation data can provide essential information regarding treatment response. However, this task can be challenging since ventilation maps contain noisy measurements and artifacts.Methods: We introduce a framework that estimates physiological changes from a set of longitudinal ventilation scans. Our method identifies changes as more plausible if they follow a monotonic trend while attributing smaller confidence in regions where large fluctuations are observed. Our algorithm outputs the estimated volumes of significant function increase and decline. The proposed framework was calibrated and validated using synthetic datasets. We also applied our model to a dataset comprising 11 lung cancer patients for whom multiple 4DCT scans were obtained during the course of radiotherapy treatment. CT-derived ventilation maps were generated and used as input to the proposed framework. In order to create a control dataset where no functional changes were expected, we also shuffled the time points for the 11 patients in every possible way that discarded as much temporal information as possible resulting in 128 functional map sequences.Results: In the patient dataset, 3/11 patients were identified with significant functional decline and 4/11 with functional increase that was associated with tumor regression. Finally, in the control dataset the frequency of occurrence of significant changes was 1.6\% (4/256) compared to 32\% (7/22) for the original patient dataset.Conclusion: We have developed a framework for analyzing functional ventilation changes from longitudinal data. The results of the lung cancer patient dataset indicate that significant functional increase and decline can occur during the course of radiotherapy treatment. More generally, the developed framework can be used to assess ventilation changes with the potential of guiding adaptive treatment strategies.
Tzitzimpasis et al. (Tue,) studied this question.