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Similar articles for PubMed (Select 24579153)

1.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

Mahrooghy M, Ashraf AB, Daye D, Mies C, Feldman M, Rosen M, Kontos D.

Med Image Comput Comput Assist Interv. 2013;16(Pt 2):295-302.

PMID:
24579153
2.

Pharmacokinetic Tumor Heterogeneity as a Prognostic Biomarker for Classifying Breast Cancer Recurrence Risk.

Mahrooghy M, Ashraf A, Days D, Mcdonald E, Rosen M, Mies C, Feldman M, Kontos D.

IEEE Trans Biomed Eng. 2015 Jan 23. [Epub ahead of print]

PMID:
25622311
3.

Multilevel analysis of spatiotemporal association features for differentiation of tumor enhancement patterns in breast DCE-MRI.

Lee SH, Kim JH, Cho N, Park JS, Yang Z, Jung YS, Moon WK.

Med Phys. 2010 Aug;37(8):3940-56.

PMID:
20879557
4.

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

Agner SC, Soman S, Libfeld E, McDonald M, Thomas K, Englander S, Rosen MA, Chin D, Nosher J, Madabhushi A.

J Digit Imaging. 2011 Jun;24(3):446-63. doi: 10.1007/s10278-010-9298-1.

5.

Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.

Chen W, Giger ML, Bick U, Newstead GM.

Med Phys. 2006 Aug;33(8):2878-87.

PMID:
16964864
6.

Characterization of spatiotemporal changes for the classification of dynamic contrast-enhanced magnetic-resonance breast lesions.

Milenković J, Hertl K, Košir A, Zibert J, Tasič JF.

Artif Intell Med. 2013 Jun;58(2):101-14. doi: 10.1016/j.artmed.2013.03.002. Epub 2013 Mar 30.

PMID:
23548472
7.

Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.

Agliozzo S, De Luca M, Bracco C, Vignati A, Giannini V, Martincich L, Carbonaro LA, Bert A, Sardanelli F, Regge D.

Med Phys. 2012 Apr;39(4):1704-15. doi: 10.1118/1.3691178.

PMID:
22482596
8.

A multichannel Markov random field framework for tumor segmentation with an application to classification of gene expression-based breast cancer recurrence risk.

Ashraf AB, Gavenonis SC, Daye D, Mies C, Rosen MA, Kontos D.

IEEE Trans Med Imaging. 2013 Apr;32(4):637-48. doi: 10.1109/TMI.2012.2219589. Epub 2012 Sep 19.

9.

Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.

Ashraf AB, Daye D, Gavenonis S, Mies C, Feldman M, Rosen M, Kontos D.

Radiology. 2014 Aug;272(2):374-84. doi: 10.1148/radiol.14131375. Epub 2014 Apr 4.

PMID:
24702725
10.

STEP: spatiotemporal enhancement pattern for MR-based breast tumor diagnosis.

Zheng Y, Englander S, Baloch S, Zacharaki EI, Fan Y, Schnall MD, Shen D.

Med Phys. 2009 Jul;36(7):3192-204.

11.

Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.

Chen W, Giger ML, Lan L, Bick U.

Med Phys. 2004 May;31(5):1076-82.

PMID:
15191295
12.

Feature selection in computer-aided breast cancer diagnosis via dynamic contrast-enhanced magnetic resonance images.

Rakoczy M, McGaughey D, Korenberg MJ, Levman J, Martel AL.

J Digit Imaging. 2013 Apr;26(2):198-208. doi: 10.1007/s10278-012-9506-2.

13.

Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images.

Yabuuchi H, Matsuo Y, Okafuji T, Kamitani T, Soeda H, Setoguchi T, Sakai S, Hatakenaka M, Kubo M, Sadanaga N, Yamamoto H, Honda H.

J Magn Reson Imaging. 2008 Nov;28(5):1157-65. doi: 10.1002/jmri.21570.

PMID:
18972357
14.

Computer aided analysis of breast MRI enhancement kinetics using mean shift clustering and multifeature iterative region of interest selection.

Stoutjesdijk MJ, Zijp M, Boetes C, Karssemeijer N, Barentsz JO, Huisman H.

J Magn Reson Imaging. 2012 Nov;36(5):1104-12. doi: 10.1002/jmri.23746. Epub 2012 Jul 11.

PMID:
22786883
15.

New spatiotemporal features for improved discrimination of benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging of the breast.

Gal Y, Mehnert A, Bradley A, Kennedy D, Crozier S.

J Comput Assist Tomogr. 2011 Sep-Oct;35(5):645-52. doi: 10.1097/RCT.0b013e318224234f.

PMID:
21926864
16.

Differentiation of breast cancer from fibroadenoma with dual-echo dynamic contrast-enhanced MRI.

Wang S, Delproposto Z, Wang H, Ding X, Ji C, Wang B, Xu M.

PLoS One. 2013 Jul 2;8(7):e67731. doi: 10.1371/journal.pone.0067731. Print 2013.

17.

Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching.

Kim M, Wu G, Shen D.

Med Phys. 2012 Jan;39(1):353-66. doi: 10.1118/1.3665705.

18.

Dynamic contrast-enhanced magnetic resonance imaging of tumors: preclinical validation of parametric images.

Egeland TA, Simonsen TG, Gaustad JV, Gulliksrud K, Ellingsen C, Rofstad EK.

Radiat Res. 2009 Sep;172(3):339-47. doi: 10.1667/RR1787.1.

PMID:
19708783
19.

Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed.

Cui Y, Tan Y, Zhao B, Liberman L, Parbhu R, Kaplan J, Theodoulou M, Hudis C, Schwartz LH.

Med Phys. 2009 Oct;36(10):4359-69.

20.

Texture analysis in assessment and prediction of chemotherapy response in breast cancer.

Ahmed A, Gibbs P, Pickles M, Turnbull L.

J Magn Reson Imaging. 2013 Jul;38(1):89-101. doi: 10.1002/jmri.23971. Epub 2012 Dec 13.

PMID:
23238914
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