AI Article Synopsis
- This study aims to assess a new image marker that utilizes computed tomography (CT) density histograms to classify different lung tissue types and compares it with traditional markers.
- The research involved analyzing density histograms from 220 subjects across various conditions (normal, emphysema, fibrotic), applying multiple methods including a new histogram's functional shape (HFS) alongside conventional measures.
- Results revealed that combining all methods in multinomial logistic regression yielded the best classification accuracy (92%), while the HFS method alone also outperformed conventional methods, showing promise for improving lung density analysis.
Article Abstract
Objectives: 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.
Materials And Methods: Density histograms from 220 different subjects (normal = 71; emphysema = 73; fibrotic = 76) were used to compare the conventionally applied emphysema index (EI), 15 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 R (NR) effect size were estimated. NR was used to set up a ranking list of the different methods.
Results: MLR indicates the highest classification power (OCA of 92%; sens 0.95; spec 0.89; NR 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; NR 0.80). Conventional methods achieved lower classification potential on their own: EI (OCA 69%; sens 0.95; spec 0.26; NR 0.52); PV (OCA 69%; sens 0.90; spec 0.37; NR 0.57); MV (OCA 65%; sens 0.71; spec 0.58; NR 0.61); V (OCA 66%; sens 0.72; spec 0.53; NR 0.66); S (OCA 65%; sens 0.88; spec 0.26; NR 0.55); and K (OCA 63%; sens 0.90; spec 0.16; NR 0.48).
Conclusion: 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.
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| http://dx.doi.org/10.1016/j.ejrad.2018.01.018 | DOI Listing |
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