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Items: 1 to 20 of 113

1.

Mammographic density changes in surgical weight loss-an indication for personalized screening.

Partain N, Mokdad A, Puzziferri N, Porembka J, Seiler S, Christie A, Farr D, Rivers A, Marilyn Leitch A, Wooldridge R, Huth J, Rao R.

BMC Med Imaging. 2018 May 9;18(1):10. doi: 10.1186/s12880-017-0242-4.

2.

The origins of breast cancer associated with mammographic density: a testable biological hypothesis.

Boyd N, Berman H, Zhu J, Martin LJ, Yaffe MJ, Chavez S, Stanisz G, Hislop G, Chiarelli AM, Minkin S, Paterson AD.

Breast Cancer Res. 2018 Mar 7;20(1):17. doi: 10.1186/s13058-018-0941-y.

3.

A comparison of five methods of measuring mammographic density: a case-control study.

Astley SM, Harkness EF, Sergeant JC, Warwick J, Stavrinos P, Warren R, Wilson M, Beetles U, Gadde S, Lim Y, Jain A, Bundred S, Barr N, Reece V, Brentnall AR, Cuzick J, Howell T, Evans DG.

Breast Cancer Res. 2018 Feb 5;20(1):10. doi: 10.1186/s13058-018-0932-z.

4.

Combining quantitative and qualitative breast density measures to assess breast cancer risk.

Kerlikowske K, Ma L, Scott CG, Mahmoudzadeh AP, Jensen MR, Sprague BL, Henderson LM, Pankratz VS, Cummings SR, Miglioretti DL, Vachon CM, Shepherd JA.

Breast Cancer Res. 2017 Aug 22;19(1):97. doi: 10.1186/s13058-017-0887-5.

5.

Breast density scales: the metric matters.

Abdolell M, Tsuruda KM, Brown P, Caines JS, Iles SE.

Br J Radiol. 2017 Oct;90(1078):20170307. doi: 10.1259/bjr.20170307. Epub 2017 Sep 8.

PMID:
28707989
6.

Family history and risk of breast cancer: an analysis accounting for family structure.

Brewer HR, Jones ME, Schoemaker MJ, Ashworth A, Swerdlow AJ.

Breast Cancer Res Treat. 2017 Aug;165(1):193-200. doi: 10.1007/s10549-017-4325-2. Epub 2017 Jun 3.

7.

Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants.

Chan S, Chen JH, Li S, Chang R, Yeh DC, Chang RF, Yeh LR, Kwong J, Su MY.

BMC Cancer. 2017 Apr 17;17(1):274. doi: 10.1186/s12885-017-3270-0.

8.

Family History of Breast Cancer, Breast Density, and Breast Cancer Risk in a U.S. Breast Cancer Screening Population.

Ahern TP, Sprague BL, Bissell MCS, Miglioretti DL, Buist DSM, Braithwaite D, Kerlikowske K.

Cancer Epidemiol Biomarkers Prev. 2017 Jun;26(6):938-944. doi: 10.1158/1055-9965.EPI-16-0801. Epub 2017 Jan 17.

9.

Projecting Individualized Absolute Invasive Breast Cancer Risk in US Hispanic Women.

Banegas MP, John EM, Slattery ML, Gomez SL, Yu M, LaCroix AZ, Pee D, Chlebowski RT, Hines LM, Thompson CA, Gail MH.

J Natl Cancer Inst. 2016 Dec 20;109(2). doi: 10.1093/jnci/djw215. Print 2017 Feb.

10.

Quantitative assessment of background parenchymal enhancement in breast magnetic resonance images predicts the risk of breast cancer.

Hu X, Jiang L, Li Q, Gu Y.

Oncotarget. 2017 Feb 7;8(6):10620-10627. doi: 10.18632/oncotarget.13538.

11.

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling.

Taslim C, Weng DY, Brasky TM, Dumitrescu RG, Huang K, Kallakury BV, Krishnan S, Llanos AA, Marian C, McElroy J, Schneider SS, Spear SL, Troester MA, Freudenheim JL, Geyer S, Shields PG.

Oncotarget. 2016 Dec 27;7(52):86457-86468. doi: 10.18632/oncotarget.13241.

12.

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk.

Schonberg MA, Li VW, Eliassen AH, Davis RB, LaCroix AZ, McCarthy EP, Rosner BA, Chlebowski RT, Hankinson SE, Marcantonio ER, Ngo LH.

Breast Cancer Res Treat. 2016 Dec;160(3):547-562. doi: 10.1007/s10549-016-4020-8. Epub 2016 Oct 21.

13.

Constrained Maximum Likelihood Estimation for Model Calibration Using Summary-level Information from External Big Data Sources.

Chatterjee N, Chen YH, Maas P, Carroll RJ.

J Am Stat Assoc. 2016 Mar;111(513):107-117. Epub 2016 May 5.

14.

Bayesian joint ordinal and survival modeling for breast cancer risk assessment.

Armero C, Forné C, Rué M, Forte A, Perpiñán H, Gómez G, Baré M.

Stat Med. 2016 Dec 10;35(28):5267-5282. doi: 10.1002/sim.7065. Epub 2016 Aug 14.

15.

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study.

Winkel RR, von Euler-Chelpin M, Nielsen M, Petersen K, Lillholm M, Nielsen MB, Lynge E, Uldall WY, Vejborg I.

BMC Cancer. 2016 Jul 7;16:414. doi: 10.1186/s12885-016-2450-7.

16.

Heterodyne frequency-domain multispectral diffuse optical tomography of breast cancer in the parallel-plane transmission geometry.

Ban HY, Schweiger M, Kavuri VC, Cochran JM, Xie L, Busch DR, Katrašnik J, Pathak S, Chung SH, Lee K, Choe R, Czerniecki BJ, Arridge SR, Yodh AG.

Med Phys. 2016 Jul;43(7):4383. doi: 10.1118/1.4953830.

17.

Gene Methylation and Cytological Atypia in Random Fine-Needle Aspirates for Assessment of Breast Cancer Risk.

Stearns V, Fackler MJ, Hafeez S, Bujanda ZL, Chatterton RT, Jacobs LK, Khouri NF, Ivancic D, Kenney K, Shehata C, Jeter SC, Wolfman JA, Zalles CM, Huang P, Khan SA, Sukumar S.

Cancer Prev Res (Phila). 2016 Aug;9(8):673-682. doi: 10.1158/1940-6207.CAPR-15-0377. Epub 2016 Jun 3.

18.

Breast cancer risk feedback to women in the UK NHS breast screening population.

Evans DG, Donnelly LS, Harkness EF, Astley SM, Stavrinos P, Dawe S, Watterson D, Fox L, Sergeant JC, Ingham S, Harvie MN, Wilson M, Beetles U, Buchan I, Brentnall AR, French DP, Cuzick J, Howell A.

Br J Cancer. 2016 Apr 26;114(9):1045-52. doi: 10.1038/bjc.2016.56. Epub 2016 Mar 29.

19.

Stromal characteristics may hold the key to mammographic density: the evidence to date.

Ironside AJ, Jones JL.

Oncotarget. 2016 May 24;7(21):31550-62. doi: 10.18632/oncotarget.6912. Review.

20.

Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry.

Dite GS, MacInnis RJ, Bickerstaffe A, Dowty JG, Allman R, Apicella C, Milne RL, Tsimiklis H, Phillips KA, Giles GG, Terry MB, Southey MC, Hopper JL.

Cancer Epidemiol Biomarkers Prev. 2016 Feb;25(2):359-65. doi: 10.1158/1055-9965.EPI-15-0838. Epub 2015 Dec 16.

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