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Automated 3D Ιnterstitial Lung Disease Εxtent Quantification: Performance Evaluation and Correlation to PFTs

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Abstract

In this study, the performance of a recently proposed computer-aided diagnosis (CAD) scheme in detection and 3D quantification of reticular and ground glass pattern extent in chest computed tomography of interstitial lung disease (ILD) patients is evaluated. CAD scheme performance was evaluated on a dataset of 37 volumetric chest scans, considering five representative axial anatomical levels per scan. CAD scheme reliability analysis was performed by estimating agreement (intraclass correlation coefficient, ICC) of automatically derived ILD pattern extent to semi-quantitative disease extent assessment in terms of 29-point rating scale provided by two expert radiologists. Receiver operating characteristic (ROC) analysis was employed to assess CAD scheme accuracy in ILD pattern detection in terms of area under ROC curve (A z ). Correlation of reticular and ground glass volumetric pattern extent to pulmonary function tests (PFTs) was also investigated. CAD scheme reliability was substantial for ILD extent (ICC = 0.809) and distinct reticular pattern extent (0.806) and moderate for distinct ground glass pattern extent (0.543), performing within inter-observer agreement. CAD scheme demonstrated high accuracy in detecting total ILD (A z  = 0.950 ± 0.018), while accuracy in detecting distinct reticular and ground glass patterns was 0.920 ± 0.023 and 0.883 ± 0.024, respectively. Moderate and statistically significant negative correlation was found between reticular volumetric pattern extent and diffusing capacity, forced expiratory volume in 1 s, forced vital capacity, and total lung capacity (R = −0.581, −0.513, −0.494, and −0.446, respectively), similar to correlations found between radiologists’ semi-quantitative ratings with PFTs. CAD-based quantification of disease extent is in agreement with radiologists’ semi-quantitative assessment and correlates to specific PFTs, suggesting a potential imaging biomarker for ILD staging and management.

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Acknowledgments

This work is supported in part by the Caratheodory Programme (C.591) of the University of Patras, Greece.

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Authors and Affiliations

  1. Department of Radiology, School of Medicine, University of Patras, 26504, Patras, Greece

    Alexandra Kazantzi & Christina Kalogeropoulou

  2. Department of Medical Physics, School of Medicine, University of Patras, 26504, Patras, Greece

    Lena Costaridou, Spyros Skiadopoulos, Panayiotis Korfiatis & Anna Karahaliou

  3. Division of Rheumatology, Department of Internal Medicine, School of Medicine, University of Patras, 26504, Patras, Greece

    Dimitris Daoussis & Andreas Andonopoulos

Authors
  1. Alexandra Kazantzi
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  2. Lena Costaridou
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  3. Spyros Skiadopoulos
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  4. Panayiotis Korfiatis
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  8. Christina Kalogeropoulou
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Correspondence to Christina Kalogeropoulou.

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Kazantzi, A., Costaridou, L., Skiadopoulos, S. et al. Automated 3D Ιnterstitial Lung Disease Εxtent Quantification: Performance Evaluation and Correlation to PFTs. J Digit Imaging 27, 380–391 (2014). https://doi.org/10.1007/s10278-013-9670-z

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  • DOI: https://doi.org/10.1007/s10278-013-9670-z

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