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For: Malkov S, Shepherd JA, Scott CG, Tamimi RM, Ma L, Bertrand KA, Couch F, Jensen MR, Mahmoudzadeh AP, Fan B, Norman A, Brandt KR, Pankratz VS, Vachon CM, Kerlikowske K. Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Res 2016;18:122. [PMID: 27923387 DOI: 10.1186/s13058-016-0778-1] [Cited by in Crossref: 24] [Cited by in F6Publishing: 18] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
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3 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. A comparison of five methods of measuring mammographic density: a case-control study. Breast Cancer Res 2018;20:10. [PMID: 29402289 DOI: 10.1186/s13058-018-0932-z] [Cited by in Crossref: 38] [Cited by in F6Publishing: 31] [Article Influence: 9.5] [Reference Citation Analysis]
4 Balleyguier C, Arfi-rouche J, Boyer B, Gauthier E, Helin V, Loshkajian A, Ragusa S, Delaloge S. A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations. Eur Radiol 2019;29:3830-8. [DOI: 10.1007/s00330-019-06016-y] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
5 Porembka JH, Ma J, Le-Petross HT. Breast density, MR imaging biomarkers, and breast cancer risk. Breast J 2020;26:1535-42. [PMID: 32654416 DOI: 10.1111/tbj.13965] [Reference Citation Analysis]
6 Häberle L, Hack CC, Heusinger K, Wagner F, Jud SM, Uder M, Beckmann MW, Schulz-Wendtland R, Wittenberg T, Fasching PA. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound. Eur J Med Res 2017;22:30. [PMID: 28854966 DOI: 10.1186/s40001-017-0270-0] [Cited by in Crossref: 3] [Article Influence: 0.6] [Reference Citation Analysis]
7 Warner ET, Rice MS, Zeleznik OA, Fowler EE, Murthy D, Vachon CM, Bertrand KA, Rosner BA, Heine J, Tamimi RM. Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study. NPJ Breast Cancer 2021;7:68. [PMID: 34059687 DOI: 10.1038/s41523-021-00272-2] [Reference Citation Analysis]
8 Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Strand F, Kerlikowske K, Shepherd J. Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case-case analysis. Med Phys 2019;46:1309-16. [PMID: 30697755 DOI: 10.1002/mp.13410] [Reference Citation Analysis]
9 Malkov S, Shepherd JA, Scott CG, Tamimi RM, Ma L, Bertrand KA, Couch F, Jensen MR, Mahmoudzadeh AP, Fan B, Norman A, Brandt KR, Pankratz VS, Vachon CM, Kerlikowske K. Erratum to: Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Res 2017;19:1. [PMID: 28052757 DOI: 10.1186/s13058-016-0797-y] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 0.8] [Reference Citation Analysis]
10 Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Conant EF, Scott CG, Winham SJ, Kerlikowske K, Vachon C, Maidment ADA, Kontos D. Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation. Cancers (Basel) 2021;13:5497. [PMID: 34771660 DOI: 10.3390/cancers13215497] [Reference Citation Analysis]
11 Marinov S, Buliev I, Cockmartin L, Bosmans H, Bliznakov Z, Mettivier G, Russo P, Bliznakova K. Radiomics software for breast imaging optimization and simulation studies. Phys Med 2021;89:114-28. [PMID: 34364255 DOI: 10.1016/j.ejmp.2021.07.014] [Reference Citation Analysis]
12 Hinton B, Ma L, Mahmoudzadeh AP, Malkov S, Fan B, Greenwood H, Joe B, Lee V, Kerlikowske K, Shepherd J. Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study. Cancer Imaging 2019;19:41. [PMID: 31228956 DOI: 10.1186/s40644-019-0227-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Hugo HJ, Tourell MC, O’gorman PM, Paige AE, Wellard RM, Lloyd T, Momot KI, Thompson EW. Looking beyond the mammogram to assess mammographic density: A narrative review. BSI 2018;7:63-80. [DOI: 10.3233/bsi-180176] [Cited by in Crossref: 3] [Article Influence: 0.8] [Reference Citation Analysis]
14 Vinnicombe SJ. Breast density: why all the fuss? Clin Radiol 2018;73:334-57. [PMID: 29273225 DOI: 10.1016/j.crad.2017.11.018] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 3.8] [Reference Citation Analysis]
15 Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Easton DF, French D, Sroczynski G, Hall P, Cuzick J, Evans DG, Simard J, Garcia-Closas M, Schmutzler R, Wegwarth O, Pharoah P, Moorthie S, De Montgolfier S, Baron C, Herceg Z, Turnbull C, Balleyguier C, Rossi PG, Wesseling J, Ritchie D, Tischkowitz M, Broeders M, Reisel D, Metspalu A, Callender T, de Koning H, Devilee P, Delaloge S, Schmidt MK, Widschwendter M. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol 2020;17:687-705. [PMID: 32555420 DOI: 10.1038/s41571-020-0388-9] [Cited by in Crossref: 21] [Cited by in F6Publishing: 22] [Article Influence: 10.5] [Reference Citation Analysis]
16 Tamez-Peña JG, Rodriguez-Rojas JA, Gomez-Rueda H, Celaya-Padilla JM, Rivera-Prieto RA, Palacios-Corona R, Garza-Montemayor M, Cardona-Huerta S, Treviño V. Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer. PLoS One 2018;13:e0193871. [PMID: 29596496 DOI: 10.1371/journal.pone.0193871] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 1.3] [Reference Citation Analysis]
17 Tan M, Mariapun S, Yip CH, Ng KH, Teo SH. A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort. Phys Med Biol 2019;64:035016. [PMID: 30577031 DOI: 10.1088/1361-6560/aafabd] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
18 Wanders JOP, van Gils CH, Karssemeijer N, Holland K, Kallenberg M, Peeters PHM, Nielsen M, Lillholm M. The combined effect of mammographic texture and density on breast cancer risk: a cohort study. Breast Cancer Res 2018;20:36. [PMID: 29720220 DOI: 10.1186/s13058-018-0961-7] [Cited by in Crossref: 16] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
19 Bedrosian I. Screening Mammography: Getting to Version 2.0. Ann Surg Oncol 2018;25:2500-1. [DOI: 10.1245/s10434-018-6522-6] [Cited by in Crossref: 3] [Article Influence: 0.8] [Reference Citation Analysis]
20 Kerlikowske K, Ma L, Scott CG, Mahmoudzadeh AP, Jensen MR, Sprague BL, Henderson LM, Pankratz VS, Cummings SR, Miglioretti DL, Vachon CM, Shepherd JA. Combining quantitative and qualitative breast density measures to assess breast cancer risk. Breast Cancer Res 2017;19:97. [PMID: 28830497 DOI: 10.1186/s13058-017-0887-5] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 3.8] [Reference Citation Analysis]