A Bayesian framework for the detection of physiological pulmonary ventilation changes

Abstract

Objective.The assessment of regional ventilation changes from functional ventilation data can provide essential information regarding treatment response in lung cancer radiotherapy (RT). However, this task can be challenging since ventilation maps contain noisy measurements and artifacts.Approach.We introduce a Bayesian framework that identifies physiological changes from a set of longitudinal ventilation scans. The proposed framework was calibrated and validated using synthetic datasets. Its ability to correctly classify regions of true change was evaluated using the sensitivity, precision and Dice Similarity coefficient. We also applied our model to a dataset comprising 11 lung cancer patients for whom multiple 4DCT scans were obtained during the course of RT 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.Main results.In the synthetic validation dataset, the average (standard deviation) of the sensitivity, precision and Dice Similarity coefficient was 0.72 (0.26), 0.95(0.05) and 0.78 (0.22) respectively. 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.Significance.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 RT treatment. More generally, the developed framework can be used to assess ventilation changes with the potential of guiding adaptive treatment strategies.