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Items: 1 to 20 of 27

1.

Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

Gastounioti A, Conant EF, Kontos D.

Breast Cancer Res. 2016 Sep 20;18(1):91. doi: 10.1186/s13058-016-0755-8. Review.

2.

Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development.

Tan M, Zheng B, Leader JK, Gur D.

IEEE Trans Med Imaging. 2016 Jul;35(7):1719-28. doi: 10.1109/TMI.2016.2527619.

PMID:
26886970
3.

Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort.

Brentnall AR, Harkness EF, Astley SM, Donnelly LS, Stavrinos P, Sampson S, Fox L, Sergeant JC, Harvie MN, Wilson M, Beetles U, Gadde S, Lim Y, Jain A, Bundred S, Barr N, Reece V, Howell A, Cuzick J, Evans DG.

Breast Cancer Res. 2015 Dec 1;17(1):147. doi: 10.1186/s13058-015-0653-5.

4.
5.

Gene signature model for breast cancer risk prediction for women with sclerosing adenosis.

Degnim AC, Nassar A, Stallings-Mann M, Keith Anderson S, Oberg AL, Vierkant RA, Frank RD, Wang C, Winham SJ, Frost MH, Hartmann LC, Visscher DW, Radisky DC.

Breast Cancer Res Treat. 2015 Aug;152(3):687-94. doi: 10.1007/s10549-015-3513-1.

6.

Using multiscale texture and density features for near-term breast cancer risk analysis.

Sun W, Tseng TL, Qian W, Zhang J, Saltzstein EC, Zheng B, Lure F, Yu H, Zhou S.

Med Phys. 2015 Jun;42(6):2853-62. doi: 10.1118/1.4919772.

7.

A new approach to develop computer-aided detection schemes of digital mammograms.

Tan M, Qian W, Pu J, Liu H, Zheng B.

Phys Med Biol. 2015 Jun 7;60(11):4413-27. doi: 10.1088/0031-9155/60/11/4413.

8.

Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

Tan M, Pu J, Cheng S, Liu H, Zheng B.

Ann Biomed Eng. 2015 Oct;43(10):2416-28. doi: 10.1007/s10439-015-1316-5.

9.

Twenty-five years of breast cancer risk models and their applications.

Gail MH.

J Natl Cancer Inst. 2015 Feb 26;107(5). pii: djv042. doi: 10.1093/jnci/djv042. No abstract available.

10.

JNCI and cancer prevention.

Dunn BK, Ghosh S, Kramer BS.

J Natl Cancer Inst. 2015 Feb 24;107(3). pii: djv021. doi: 10.1093/jnci/djv021.

11.

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.

12.

Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I.

Warwick J, Birke H, Stone J, Warren RM, Pinney E, Brentnall AR, Duffy SW, Howell A, Cuzick J.

Breast Cancer Res. 2014 Oct 8;16(5):451. doi: 10.1186/s13058-014-0451-5.

13.

Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population.

Lee CP, Irwanto A, Salim A, Yuan JM, Liu J, Koh WP, Hartman M.

Breast Cancer Res. 2014 Jun 18;16(3):R64. doi: 10.1186/bcr3678.

14.

Association between computed tissue density asymmetry in bilateral mammograms and near-term breast cancer risk.

Zheng B, Tan M, Ramalingam P, Gur D.

Breast J. 2014 May-Jun;20(3):249-57. doi: 10.1111/tbj.12255.

15.

Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.

Tan M, Zheng B, Ramalingam P, Gur D.

Acad Radiol. 2013 Dec;20(12):1542-50. doi: 10.1016/j.acra.2013.08.020.

16.

Using SNP genotypes to improve the discrimination of a simple breast cancer risk prediction model.

Dite GS, Mahmoodi M, Bickerstaffe A, Hammet F, Macinnis RJ, Tsimiklis H, Dowty JG, Apicella C, Phillips KA, Giles GG, Southey MC, Hopper JL.

Breast Cancer Res Treat. 2013 Jun;139(3):887-96. doi: 10.1007/s10549-013-2610-2.

17.

Benign breast disease, mammographic breast density, and the risk of breast cancer.

Tice JA, O'Meara ES, Weaver DL, Vachon C, Ballard-Barbash R, Kerlikowske K.

J Natl Cancer Inst. 2013 Jul 17;105(14):1043-9. doi: 10.1093/jnci/djt124.

18.

Belief in numbers: When and why women disbelieve tailored breast cancer risk statistics.

Scherer LD, Ubel PA, McClure J, Greene SM, Alford SH, Holtzman L, Exe N, Fagerlin A.

Patient Educ Couns. 2013 Aug;92(2):253-9. doi: 10.1016/j.pec.2013.03.016.

19.

Incremental impact of breast cancer SNP panel on risk classification in a screening population of white and African American women.

McCarthy AM, Armstrong K, Handorf E, Boghossian L, Jones M, Chen J, Demeter MB, McGuire E, Conant EF, Domchek SM.

Breast Cancer Res Treat. 2013 Apr;138(3):889-98. doi: 10.1007/s10549-013-2471-8.

20.

A focus group study on breast cancer risk presentation: one format does not fit all.

Dorval M, Bouchard K, Chiquette J, Glendon G, Maugard CM, Dubuisson W, Panchal S, Simard J.

Eur J Hum Genet. 2013 Jul;21(7):719-24. doi: 10.1038/ejhg.2012.248.

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