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Proceedings Paper

Computer-aided characterization of solitary pulmonary nodules (SPNs) using structural 3D, texture, and functional dynamic contrast features
Author(s): Yang Wang; Michael F. McNitt-Gray; Sumit Shah; Jonathan G. Goldin; Matthew S. Brown; Denise R. Aberle
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Paper Abstract

The purpose of this paper was to investigate the effects of integrating nodule 3D morphological features, texture features and functional dynamic contrast-enhanced features in differentiating between benign and malignant solitary pulmonary nodules (SPNs). In this study, 42 cases with solitary lung nodules were examined in this study. The dynamic CT helical scans were acquired image at five time intervals: prior to contrast injection (baseline) and then at 45, 90, 180, 300 seconds after administrating the contrast agent. The nodule boundaries were contoured by radiologists on all series. Using these boundaries, several types of nodule features were computed, including: 3D morphology and Shape Index of the nodule contrast intensity surface; Dynamic contrast related features; 3D texture features. AdaBoost was performed to select the best features. Logistic Regression Analysis (LRA) and AdaBoost were used to analyze the diagnostic accuracy of features in each feature category. The performance when integrating all feature types was also evaluated. For 42 patients, when using only six SI and 3D structural features, the accuracy of AdaBoost was 81.4%, with accuracies of AdaBoost using functional contrast related features (include 8 features) and texture features(include 18 features) were 65.1% and 69.1% respectively. After combining all types' features together, the overall accuracy was improved to over 88%. In conclusion: Combining 3D structural, textural and functional contrast features can provide a more comprehensive examination of the SPNs by coupling dynamic CT scan techniques with image processing to quantify multiple properties that relate to tumor geometry and tumor angiogenesis. This integration may assist radiologists in characterizing SPNs more accurately.

Paper Details

Date Published: 29 March 2007
PDF: 9 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651413 (29 March 2007); doi: 10.1117/12.713232
Show Author Affiliations
Yang Wang, Univ. of California, Los Angeles (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)
Sumit Shah, Univ. of California, Los Angeles (United States)
Jonathan G. Goldin, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)
Denise R. Aberle, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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