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TU-C-12A-10: BEST IN PHYSICS (IMAGING) - Correlating 4DCT-Ventilation with Clinical Pulmonary Function Test Data.

PURPOSE
4DCT-ventilation is an emerging form of lung function imaging calculated from 4DCT data. Because 4DCTs are acquired as part of routine care, the lung function information comes at no extra monetary or dosimetric cost to the patient. Studies are needed that compare 4DCT-ventilation with clinical data. Pulmonary function tests (PFT) provide an established method of evaluating lung function. Our study performed a clinical validation by comparing 4DCT-ventilation with PFT data.


METHODS
63 patients with pre-treatment PFTs and 4DCT data were used for the study. Standard PFT metrics used to diagnose tumor-based obstructive lung disease were recorded for each patient, including forced expiratory volume in 1 second (FEV1), and the ratio between FEV1 and the forced vital capacity (FEV1/FVC). A PFT value of 70% was used to delineate normal versus abnormal lung function for modeling. 4DCT data sets were input into a density-change based model to compute 4DCT-ventilation. The 4DCT-ventilation maps were reduced to single metrics intended to reflect the degree of ventilation obstruction and heterogeneity. The coefficient of variation (CoV) defined as the ratio of standard deviation over mean ventilation and the ventilation-V20 (volume of lung <20% ventilation) were computed. Ventilation metrics were compared to PFT data using correlation coefficients and logistic regression modeling methods.


RESULTS
The 4DCT-ventilation metrics correlated well with PFT data. Ventilation-V20 had correlation values of 0.75 and 0.71 with FEV1 and FEV1/FVC respectively. A probit model as a function of the ventilationderived CoV was fit and able to significantly (p<0.01) predict for normal versus abnormal PFT-based lung function.


CONCLUSIONS
The current study validates 4DCT-ventilation with a clinically established end-point. Our data demonstrate good agreement between PFTs and ventilation data, indicating that 4DCT-ventilation can reliably demonstrate oncologic-based obstructive lung disease. We present important results that support integration of 4DCT-based pre-treatment lung function assessment into clinical practice. This work was partially supported by the NIH Director's new Innovator award (TG, EC, RC) and NIH research scientist development award (RC).