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Proceedings Paper

Quantitative assessment of emphysema from whole lung CT scans: comparison with visual grading
Author(s): Brad M. Keller; Anthony P. Reeves; Tatiyana V. Apanosovich; Jianwei Wang; David F. Yankelevitz; Claudia I. Henschke
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Paper Abstract

Emphysema is a disease of the lungs that destroys the alveolar air sacs and induces long-term respiratory dysfunction. CT scans allow for imaging of the anatomical basis of emphysema and for visual assessment by radiologists of the extent present in the lungs. Several measures have been introduced for the quantification of the extent of disease directly from CT data in order to add to the qualitative assessments made by radiologists. In this paper we compare emphysema index, mean lung density, histogram percentiles, and the fractal dimension to visual grade in order to evaluate the predictability of radiologist visual scoring of emphysema from low-dose CT scans through quantitative scores, in order to determine which measures can be useful as surrogates for visual assessment. All measures were computed over nine divisions of the lung field (whole lung, individual lungs, and upper/middle/lower thirds of each lung) for each of 148 low-dose, whole lung scans. In addition, a visual grade of each section was also given by an expert radiologist. One-way ANOVA and multinomial logistic regression were used to determine the ability of the measures to predict visual grade from quantitative score. We found that all measures were able to distinguish between normal and severe grades (p<0.01), and between mild/moderate and all other grades (p<0.05). However, no measure was able to distinguish between mild and moderate cases. Approximately 65% prediction accuracy was achieved from using quantitative score to predict visual grade, with 73% if mild and moderate cases are considered as a single class.

Paper Details

Date Published: 28 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726008 (28 February 2009); doi: 10.1117/12.812254
Show Author Affiliations
Brad M. Keller, Cornell Univ. (United States)
Anthony P. Reeves, Cornell Univ. (United States)
Tatiyana V. Apanosovich, Thomas Jefferson Univ. (United States)
Jianwei Wang, Weill Medical College, Cornell Univ. (United States)
David F. Yankelevitz, Weill Medical College, Cornell Univ. (United States)
Claudia I. Henschke, Weill Medical College, Cornell Univ. (United States)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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