Literature DB >> 23194641

Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?

Francesca Ng1, Robert Kozarski, Balaji Ganeshan, Vicky Goh.   

Abstract

OBJECTIVE: To determine if there is a difference between contrast enhanced CT texture features from the largest cross-sectional area versus the whole tumor, and its effect on clinical outcome prediction.
METHODS: Entropy (E) and uniformity (U) were derived for different filter values (1.0-2.5: fine to coarse textures) for the largest primary tumor cross-sectional area and the whole tumor of the staging contrast enhanced CT in 55 patients with primary colorectal cancer. Parameters were compared using non-parametric Wilcoxon test. Kaplan-Meier analysis was performed to determine the relationship between CT texture and 5-year overall survival.
RESULTS: E was higher and U lower for the whole tumor indicating greater heterogeneity at all filter levels (1.0-2.5): median (range) for E and U for whole tumor versus largest cross-sectional area of 7.89 (7.43-8.31) versus 7.62 (6.94-8.08) and 0.005 (0.004-0.01) versus 0.006 (0.005-0.01) for filter 1.0; 7.88 (7.22-8.48) versus 7.54 (6.86-8.1) and 0.005 (0.003-0.01) versus 0.007 (0.004-0.01) for filter 1.5; 7.88 (7.17-8.54) versus 7.48 (5.84-8.25) and 0.005 (0.003-0.01) versus 0.007 (0.004-0.02) for filter 2.0; and 7.83 (7.03-8.57) versus 7.42 (5.19-8.26) and 0.005 (0.003-0.01) versus 0.006 (0.004-0.03) for filter 2.5 respectively (p ≤ 0.001). Kaplan-Meier analysis demonstrated better separation of E and U for whole tumor analysis for 5-year overall survival.
CONCLUSION: Whole tumor analysis appears more representative of tumor heterogeneity.
Copyright © 2012. Published by Elsevier Ireland Ltd.

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Year:  2012        PMID: 23194641     DOI: 10.1016/j.ejrad.2012.10.023

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  123 in total

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6.  Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis.

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9.  Diagnostic accuracy of MRI texture analysis for grading gliomas.

Authors:  Austin Ditmer; Bin Zhang; Taimur Shujaat; Andrew Pavlina; Nicholas Luibrand; Mary Gaskill-Shipley; Achala Vagal
Journal:  J Neurooncol       Date:  2018-08-25       Impact factor: 4.130

10.  CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts.

Authors:  Su Joa Ahn; Jung Hoon Kim; Sang Min Lee; Sang Joon Park; Joon Koo Han
Journal:  Eur Radiol       Date:  2018-11-19       Impact factor: 5.315

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