Quantitative computed tomography imaging of interstitial lung diseases

Abstract

Purpose: High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process.

Materials and methods: Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome.

Results: Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance.

Conclusions: It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.

Figure 1

2D projection of the 3D MDS for the pairwise CVM dissimilarity of the 976 color-coded VOIs used to train CALIPER.

Figure 2

Representative glyph provides summary of distribution for 5 characteristic CT patterns (color-coded) in lung parenchyma. The first letter (R/L) denotes the right and left lung, the second letter (U/M/L) denotes, respectively, the upper, middle, and lower lung zones defined on the basis of the automatic detection of carina. The radius of the glyph is proportional to the lung volume. The white outer circle denotes the predicted total lung capacity of the subject.

Figure 3

The montage of glyphs for CT scans of 372 LTRC subjects demonstrates the spectrum of parenchymal abnormalities. Subjects in the LTRC shown here represent a variety of ILD, COPD, and mixed parenchymal and/or airway diseases including UIP, nonspecific interstitial pneumonitis, hypersensitivity pneumonitis, emphysema, bronchiolitis, and combined pulmonary fibrosis and emphysema. COPD subjects generally have more blue (representing low-attenuation/emphysema regions), whereas ILD or mixed parenchymal diseases demonstrate more yellow, orange, and red (GG, RI, and HC, respectively). There are few, if any, truly “normal” subjects in the LTRC database, and therefore only a minority of glyphs exhibit predominantly green throughout all regions.

Figure 4

Location tagging of the summary glyph (top) to the underlying 3D scan data (bottom). In this example, clicking on the emphysema (blue)—left upper (LU)—region of the glyph reveals the triplanar view of the most characteristic emphysema region in the left upper lobe. The first letter (R/L) indicates the right and left lung, the second letter (U/M/L) denotes, respectively, the upper, middle, and lower lung zones.

Figure 5

Patho-spatio-temporal glyph illustration for a subject with progressing fibrotic ILD. The decreasing lung volume (radius of the glyph) with respect to the outer white circle (predicted total lung capacity), loss of normal parenchyma, and increasing proportion of HC and RI densities in the volume is apparent. Volume loss in the right lung is relatively greater than in the left. The first letter (R/L) indicates the right and left lung, the second letter (U/M/L) denotes, respectively, the upper, middle, and lower lung zones.

Figure 6

Graph revealing that the mean pairwise CVM dissimilarity distances between the pattern-specific exemplars are significantly higher than the mean dissimilarity distances within the VOIs belonging to the individual cluster. The high interexemplar and low intracluster values signify high cluster validity. Quantitative efficacy of the clustering was established by computing the similarity statistic (R) by the ANOSIM method. The combined R for the 5 clusters was found to be 0.962±0.017, with P<0.005. E indicates emphysema; N, normal.