Novel Logistic Regression Model of Chest CT Attenuation Coefficient Distributions for the Automated Detection of Abnormal (Emphysema or ILD) versus Normal Lung
Published Date:Jan 08 2016
Source:Acad Radiol. 23(3):304-314.
Aged, 80 And Over
False Negative Reactions
False Positive Reactions
Functional Residual Capacity
Lung Diseases, Interstitial
Multi-detector Volumetric CT Scanning
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Total Lung Capacity
Pubmed Central ID:PMC4744594
Funding:R01 HL089897/HL/NHLBI NIH HHS/United States
T420H008491/PHS HHS/United States
R01 HL112986/HL/NHLBI NIH HHS/United States
P30 ES005605/ES/NIEHS NIH HHS/United States
K23 HL114640/HL/NHLBI NIH HHS/United States
U01 HL089897/HL/NHLBI NIH HHS/United States
01HL112986/HL/NHLBI NIH HHS/United States
U01 HL114494/HL/NHLBI NIH HHS/United States
T42 OH008491/OH/NIOSH CDC HHS/United States
P30 ES05605/ES/NIEHS NIH HHS/United States
Description:Rationale and Objectives
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.ypothesized that the quantification and distributions of CT attenuation values on lung CT, over a subset of Hounsfield Units (HU) range [−1000 HU, 0 HU], can differentiate early or mild disease from normal lung.
Materials and Methods
We compared results of quantitative spiral rapid end-exhalation (functional residual capacity; FRC) and end-inhalation (total lung capacity; TLC) CT scan analyses in 52 subjects with radiographic evidence of mild fibrotic lung disease to 17 normal subjects. Several CT value distributions were explored, including (i) that from the peripheral lung taken at TLC (with peels at 15 or 65mm), (ii) the ratio of (i) to that from the core of lung, and (iii) the ratio of (ii) to its FRC counterpart. We developed a fused-lasso logistic regression model that can automatically identify sub-intervals of [−1000 HU, 0 HU] over which a CT value distribution provides optimal discrimination between abnormal and normal scans.
The fused-lasso logistic regression model based on (ii) with 15 mm peel identified the relative frequency of CT values over [−1000, −900] and that over [−450,−200] HU as a means of discriminating abnormal versus normal, 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.
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