Quantitative computed tomography applied to interstitial lung diseases

• Published in: European journal of radiology Vol. 100; pp. 99 - 107
• Main Authors: Obert, Martin, Kampschulte, Marian, Limburg, Rebekka, Barańczuk, Stefan, Krombach, Gabriele A.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.03.2018
• Subjects: CT histogram analysis; Image marker; Multinomial logistic regression; Quantitative image analysis; Radiomics; Image marker; Multinomial logistic regression; Quantitative image analysis; CT histogram analysis; Radiomics
• ISSN: 0720-048X; 1872-7727; 1872-7727
• DOI: 10.1016/j.ejrad.2018.01.018

[Display omitted] •CT density curves contain more disease knowledge that was not extracted so far.•Mathematical tools can help to decode invisible disease information from histograms.•Combinations of different image markers outperform individual methods.•On its own, the HFS concept achieves the highest correct disease classification. To evaluate a new image marker that retrieves information from computed tomography (CT) density histograms, with respect to classification properties between different lung parenchyma groups. Furthermore, to conduct a comparison of the new image marker with conventional markers. Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15th percentile value (PV), mean value (MV), variance (V), skewness (S), kurtosis (K), with a new histogram’s functional shape (HFS) method. Multinomial logistic regression (MLR) analyses was performed to calculate predictions of different lung parenchyma group membership using the individual methods, as well as combinations thereof, as covariates. Overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke’s pseudo R2 (NR2) effect size were estimated. NR2 was used to set up a ranking list of the different methods. MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR2 0.95) when all histogram analyses methods were applied together in the MLR. Highest classification power among individually applied methods was found using the HFS concept (OCA 86%; sens 0.93; spec 0.79; NR2 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR2 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR2 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR2 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR2 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR2 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR2 0.48). The HFS method, which was so far applied to a CT bone density curve analysis, is also a remarkable information extraction tool for lung density histograms. Presumably, being a principle mathematical approach, the HFS method can extract valuable health related information also from histograms from complete different areas.