Literature DB >> 26745258

Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.

Jinzhong Yang1, Lifei Zhang2, Xenia J Fave2, David V Fried2, Francesco C Stingo3, Chaan S Ng4, Laurence E Court2.   

Abstract

PURPOSE: To assess the uncertainty of quantitative imaging features extracted from contrast-enhanced computed tomography (CT) scans of lung cancer patients in terms of the dependency on the time after contrast injection and the feature reproducibility between scans.
METHODS: Eight patients underwent contrast-enhanced CT scans of lung tumors on two sessions 2-7 days apart. Each session included 6 CT scans of the same anatomy taken every 15s, starting 50s after contrast injection. Image features based on intensity histogram, co-occurrence matrix, neighborhood gray-tone difference matrix, run-length matrix, and geometric shape were extracted from the tumor for each scan. Spearman's correlation was used to examine the dependency of features on the time after contrast injection, with values over 0.50 considered time-dependent. Concordance correlation coefficients were calculated to examine the reproducibility of each feature between times of scans after contrast injection and between scanning sessions, with values greater than 0.90 considered reproducible.
RESULTS: The features were found to have little dependency on the time between the contrast injection and the CT scan. Most features were reproducible between times of scans after contrast injection and between scanning sessions. Some features were more reproducible when they were extracted from a CT scan performed at a longer time after contrast injection.
CONCLUSION: The quantitative imaging features tested here are mostly reproducible and show little dependency on the time after contrast injection.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 26745258      PMCID: PMC4755872          DOI: 10.1016/j.compmedimag.2015.12.001

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


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