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Results: 1 to 20 of 32

1.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

Keller BM, Oustimov A, Wang Y, Chen J, Acciavatti RJ, Zheng Y, Ray S, Gee JC, Maidment AD, Kontos D.

J Med Imaging (Bellingham). 2015 Apr;2(2):024501. doi: 10.1117/1.JMI.2.2.024501. Epub 2015 Apr 3.

PMID:
26158105
2.

Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

Zheng Y, Keller BM, Ray S, Wang Y, Conant EF, Gee JC, Kontos D.

Med Phys. 2015 Jul;42(7):4149. doi: 10.1118/1.4921996.

PMID:
26133615
3.

Breast DCE-MRI Kinetic Heterogeneity Tumor Markers: Preliminary Associations With Neoadjuvant Chemotherapy Response.

Ashraf A, Gaonkar B, Mies C, DeMichele A, Rosen M, Davatzikos C, Kontos D.

Transl Oncol. 2015 Jun;8(3):154-62. doi: 10.1016/j.tranon.2015.03.005.

4.

Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers.

Wu S, Weinstein SP, DeLeo MJ 3rd, Conant EF, Chen J, Domchek SM, Kontos D.

Breast Cancer Res. 2015 May 19;17(1):67. doi: 10.1186/s13058-015-0577-0.

5.

Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography.

Keller BM, McCarthy AM, Chen J, Armstrong K, Conant EF, Domchek SM, Kontos D.

BMC Cancer. 2015 Mar 18;15:143. doi: 10.1186/s12885-015-1159-3.

6.

Breast MRI fibroglandular volume and parenchymal enhancement in BRCA1 and BRCA2 mutation carriers before and immediately after risk-reducing salpingo-oophorectomy.

DeLeo MJ 3rd, Domchek SM, Kontos D, Conant E, Chen J, Weinstein S.

AJR Am J Roentgenol. 2015 Mar;204(3):669-73. doi: 10.2214/AJR.13.12146.

PMID:
25714301
7.

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
8.

The use of the Gail model, body mass index and SNPs to predict breast cancer among women with abnormal (BI-RADS 4) mammograms.

McCarthy AM, Keller B, Kontos D, Boghossian L, McGuire E, Bristol M, Chen J, Domchek S, Armstrong K.

Breast Cancer Res. 2015 Jan 8;17:1. doi: 10.1186/s13058-014-0509-4.

9.

Women In Steady Exercise Research (WISER) Sister: study design and methods.

Schmitz KH, Williams NI, Kontos D, Kurzer MS, Schnall M, Domchek S, Stopfer J, Galantino ML, Hwang WT, Morales K, Wu S, DiGiovanni L, Salvatore D, Fenderson D, Good J, Sturgeon K, Grant L, Bryan CJ, Adelman J.

Contemp Clin Trials. 2015 Mar;41:17-30. doi: 10.1016/j.cct.2014.12.016. Epub 2015 Jan 3.

PMID:
25559914
10.

Screening outcomes following implementation of digital breast tomosynthesis in a general-population screening program.

McCarthy AM, Kontos D, Synnestvedt M, Tan KS, Heitjan DF, Schnall M, Conant EF.

J Natl Cancer Inst. 2014 Oct 13;106(11). pii: dju316. doi: 10.1093/jnci/dju316. Print 2014 Nov.

PMID:
25313245
11.

Breast density and parenchymal texture measures as potential risk factors for Estrogen-Receptor positive breast cancer.

Keller BM, Chen J, Conant EF, Kontos D.

Proc SPIE Int Soc Opt Eng. 2014 Mar 27;9035:90351D.

12.

Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.

Ou Y, Weinstein SP, Conant EF, Englander S, Da X, Gaonkar B, Hsieh MK, Rosen M, DeMichele A, Davatzikos C, Kontos D.

Magn Reson Med. 2015 Jun;73(6):2343-56. doi: 10.1002/mrm.25368. Epub 2014 Jul 15.

PMID:
25046843
13.

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
14.

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
15.

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.

Wu S, Weinstein SP, Conant EF, Kontos D.

Med Phys. 2013 Dec;40(12):122302. doi: 10.1118/1.4829496.

16.

Mammographic parenchymal patterns as an imaging marker of endogenous hormonal exposure: a preliminary study in a high-risk population.

Daye D, Keller B, Conant EF, Chen J, Schnall MD, Maidment AD, Kontos D.

Acad Radiol. 2013 May;20(5):635-46. doi: 10.1016/j.acra.2012.12.016.

17.

Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images.

Wu S, Weinstein SP, Conant EF, Schnall MD, Kontos D.

Med Phys. 2013 Apr;40(4):042301. doi: 10.1118/1.4793255.

18.

Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures.

Keller BM, Nathan DL, Gavenonis SC, Chen J, Conant EF, Kontos D.

Acad Radiol. 2013 May;20(5):560-8. doi: 10.1016/j.acra.2013.01.003. Epub 2013 Mar 5.

19.

Atlas-based probabilistic fibroglandular tissue segmentation in breast MRI.

Wu S, Weinstein S, Kontos D.

Med Image Comput Comput Assist Interv. 2012;15(Pt 2):437-45.

20.

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.

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