Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) Versus Normal Lung

Abstract

We evaluated the role of automated quantitative computed tomography (CT) scan interpretation algorithm in detecting interstitial lung disease (ILD) and/or emphysema in a sample of elderly subjects with mild lung disease. We hypothesized that the quantification and distributions of CT attenuation values on lung CT, over a subset of Hounsfield units (HUs) range (-1000 HU, 0 HU), can differentiate early or mild disease from normal lung. We compared the results of quantitative spiral rapid end-exhalation (functional residual capacity, FRC) and end-inhalation (total lung capacity, TLC) CT scan analyses of 52 subjects with radiographic evidence of mild fibrotic lung disease to the results of 17 normal subjects. Several CT value distributions were explored, including (1) that from the peripheral lung taken at TLC (with peels at 15 or 65 mm), (2) the ratio of (1) to that from the core of lung, and (3) the ratio of (2) to its FRC counterpart. We developed a fused-lasso logistic regression model that can automatically identify sub-intervals of -1000 HU and 0 HU over which a CT value distribution provides optimal discrimination between abnormal and normal scans. The fused-lasso logistic regression model based on (2) with 15-mm peel identified the relative frequency of CT values of over -1000 HU and -900 and those over -450 HU and -200 HU as a means of discriminating abnormal versus normal lung, resulting in a zero out-sample false-positive rate, and 15% false-negative rate of that was lowered to 12% by pooling information. We demonstrated the potential usefulness of this novel quantitative imaging analysis method in discriminating ILD and/or emphysema from normal lungs.