Quantitative computed tomography imaging of interstitial lung diseases

Brian J. Bartholmai, Sushravya Raghunath, Ronald A. Karwoski, Teng Moua, Srinivasan Rajagopalan, Fabien Maldonado, Paul A. Decker, Richard A. Robb

Research output: Contribution to journalArticle

58 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)298-307
Number of pages10
JournalJournal of thoracic imaging
Volume28
Issue number5
DOIs
StatePublished - Sep 1 2013

Keywords

  • computed tomography
  • computer-aided detection
  • imaging biomarkers
  • interstitial lung disease
  • quantitative analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Pulmonary and Respiratory Medicine

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    Bartholmai, B. J., Raghunath, S., Karwoski, R. A., Moua, T., Rajagopalan, S., Maldonado, F., Decker, P. A., & Robb, R. A. (2013). Quantitative computed tomography imaging of interstitial lung diseases. Journal of thoracic imaging, 28(5), 298-307. https://doi.org/10.1097/RTI.0b013e3182a21969