Literature DB >> 12815683

Textural analysis of contrast-enhanced MR images of the breast.

Peter Gibbs1, Lindsay W Turnbull.   

Abstract

Texture analysis was applied to high-resolution, contrast-enhanced (CE) images of the breast to provide a method of lesion discrimination. Significant differences were seen between benign and malignant lesions for a number of textural features, including entropy and sum entropy. Using logistic regression analysis (LRA), a diagnostic accuracy of A(z) = 0.80 +/- 0.07 was obtained with a model requiring only three parameters. By initially dividing the patient data into training and test datasets, reasonable model robustness was also established. On combining features obtained using textural analysis with lesion size, time to maximum enhancement, and patient age, a diagnostic accuracy of A(z) = 0.92 +/- 0.05 was demonstrated. Copyright 2003 Wiley-Liss, Inc.

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Year:  2003        PMID: 12815683     DOI: 10.1002/mrm.10496

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  88 in total

1.  Germinal center texture entropy as possible indicator of humoral immune response: immunophysiology viewpoint.

Authors:  Igor Pantic; Senka Pantic
Journal:  Mol Imaging Biol       Date:  2012-10       Impact factor: 3.488

Review 2.  Texture analysis: a review of neurologic MR imaging applications.

Authors:  A Kassner; R E Thornhill
Journal:  AJNR Am J Neuroradiol       Date:  2010-04-15       Impact factor: 3.825

3.  Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis.

Authors:  A Karahaliou; K Vassiou; N S Arikidis; S Skiadopoulos; T Kanavou; L Costaridou
Journal:  Br J Radiol       Date:  2010-04       Impact factor: 3.039

4.  Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

Authors:  Shannon C Agner; Salil Soman; Edward Libfeld; Margie McDonald; Kathleen Thomas; Sarah Englander; Mark A Rosen; Deanna Chin; John Nosher; Anant Madabhushi
Journal:  J Digit Imaging       Date:  2011-06       Impact factor: 4.056

5.  Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions.

Authors:  Neha Bhooshan; Maryellen Giger; Milica Medved; Hui Li; Abbie Wood; Yading Yuan; Li Lan; Angelica Marquez; Greg Karczmar; Gillian Newstead
Journal:  J Magn Reson Imaging       Date:  2013-09-10       Impact factor: 4.813

6.  Computer-aided diagnosis of breast DCE-MRI images using bilateral asymmetry of contrast enhancement between two breasts.

Authors:  Qian Yang; Lihua Li; Juan Zhang; Guoliang Shao; Chengjie Zhang; Bin Zheng
Journal:  J Digit Imaging       Date:  2014-02       Impact factor: 4.056

7.  Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging.

Authors:  Tianwen Xie; Qiufeng Zhao; Caixia Fu; Qianming Bai; Xiaoyan Zhou; Lihua Li; Robert Grimm; Li Liu; Yajia Gu; Weijun Peng
Journal:  Eur Radiol       Date:  2018-11-06       Impact factor: 5.315

8.  Radiomics: a new application from established techniques.

Authors:  Vishwa Parekh; Michael A Jacobs
Journal:  Expert Rev Precis Med Drug Dev       Date:  2016-03-31

9.  Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.

Authors:  Neha Bhooshan; Maryellen L Giger; Sanaz A Jansen; Hui Li; Li Lan; Gillian M Newstead
Journal:  Radiology       Date:  2010-02-01       Impact factor: 11.105

10.  Non-Hodgkin lymphoma response evaluation with MRI texture classification.

Authors:  Lara C V Harrison; Tiina Luukkaala; Hannu Pertovaara; Tuomas O Saarinen; Tomi T Heinonen; Ritva Järvenpää; Seppo Soimakallio; Pirkko-Liisa I Kellokumpu-Lehtinen; Hannu J Eskola; Prasun Dastidar
Journal:  J Exp Clin Cancer Res       Date:  2009-06-22
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