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Related Experiment Video

Updated: Feb 13, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
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Quantitative computed tomography applied to interstitial lung diseases.

Martin Obert1, Marian Kampschulte1, Rebekka Limburg1

  • 1Department of Radiology, University Hospital Giessen, Justus-Liebig-University Giessen, Klinikstrasse 33, 35392 Giessen, Germany Members of The German Center for Lung Research (DZL e. V.).

European Journal of Radiology
|March 3, 2018
PubMed
Summary
This summary is machine-generated.

A new histogram functional shape (HFS) method significantly improves classification of lung parenchyma groups from CT density histograms. Combining HFS with conventional markers offers the highest accuracy in diagnosing conditions like emphysema and fibrosis.

Keywords:
CT histogram analysisImage markerMultinomial logistic regressionQuantitative image analysisRadiomics

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Area of Science:

  • Radiology and Medical Imaging
  • Quantitative Analysis of Medical Data
  • Pulmonary Medicine

Background:

  • Computed tomography (CT) density histograms are crucial for analyzing lung parenchyma.
  • Accurate classification of lung conditions like emphysema and fibrosis is vital for patient management.
  • Existing markers for lung density analysis have limitations in classification accuracy.

Purpose of the Study:

  • To evaluate a novel image marker, the histogram functional shape (HFS) method, for classifying lung parenchyma groups using CT density histograms.
  • To compare the performance of the HFS method against conventional markers such as emphysema index (EI), 15th percentile value (PV), mean value (MV), variance (V), skewness (S), and kurtosis (K).

Main Methods:

  • Density histograms from 220 subjects (71 normal, 73 emphysema, 76 fibrotic) were analyzed.
  • The HFS method was compared with EI, PV, MV, V, S, and K.
  • Multinomial logistic regression (MLR) was used to predict lung parenchyma group membership.
  • Classification power was assessed using overall correct assigned subjects (OCA), sensitivity (sens), specificity (spec), and Nagelkerke's pseudo R-squared (NR2).

Main Results:

  • The combination of all histogram analysis methods in MLR achieved the highest classification power (OCA 92%; NR2 0.95).
  • The HFS method, when applied individually, demonstrated superior classification performance (OCA 86%; NR2 0.80) compared to conventional markers.
  • Conventional methods showed lower individual classification potential: EI (OCA 69%; NR2 0.52), PV (OCA 69%; NR2 0.57), MV (OCA 65%; NR2 0.61), V (OCA 66%; NR2 0.66), S (OCA 65%; NR2 0.55), and K (OCA 63%; NR2 0.48).

Conclusions:

  • The HFS method is an effective tool for extracting information from lung density histograms, extending its utility beyond bone density analysis.
  • The HFS method shows promise as a valuable tool for health-related information extraction from diverse histogram data.
  • The findings suggest that HFS analysis can significantly enhance the diagnostic capabilities in pulmonary imaging.