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For: 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]
Number Citing Articles
1 Hopper JL, Nguyen TL, Schmidt DF, Makalic E, Song YM, Sung J, Dite GS, Dowty JG, Li S. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk. J Clin Med 2020;9:E627. [PMID: 32110975 DOI: 10.3390/jcm9030627] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
2 Arieno A, Chan A, Destounis SV. A Review of the Role of Augmented Intelligence in Breast Imaging: From Automated Breast Density Assessment to Risk Stratification. American Journal of Roentgenology 2019;212:259-70. [DOI: 10.2214/ajr.18.20391] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
3 von Euler-Chelpin M, Lillholm M, Vejborg I, Nielsen M, Lynge E. Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark. Breast Cancer Res 2019;21:111. [PMID: 31623646 DOI: 10.1186/s13058-019-1203-3] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 4.3] [Reference Citation Analysis]
4 Fishman MDC, Rehani MM. Monochromatic X-rays: The future of breast imaging. Eur J Radiol 2021;144:109961. [PMID: 34562745 DOI: 10.1016/j.ejrad.2021.109961] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Dench E, Bond-Smith D, Darcey E, Lee G, Aung YK, Chan A, Cuzick J, Ding ZY, Evans CF, Harvey J, Highnam R, Hsieh MK, Kontos D, Li S, Mariapun S, Nickson C, Nguyen TL, Pertuz S, Procopio P, Rajaram N, Repich K, Tan M, Teo SH, Trinh NH, Ursin G, Wang C, Dos-Santos-Silva I, McCormack V, Nielsen M, Shepherd J, Hopper JL, Stone J. Measurement challenge: protocol for international case-control comparison of mammographic measures that predict breast cancer risk. BMJ Open 2019;9:e031041. [PMID: 31892647 DOI: 10.1136/bmjopen-2019-031041] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
6 Miranda DA, Pertuz S. Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment. Med Hypotheses 2020;136:109511. [PMID: 31837523 DOI: 10.1016/j.mehy.2019.109511] [Reference Citation Analysis]
7 Skarping I, Förnvik D, Heide-Jørgensen U, Sartor H, Hall P, Zackrisson S, Borgquist S. Mammographic density changes during neoadjuvant breast cancer treatment: NeoDense, a prospective study in Sweden. Breast 2020;53:33-41. [PMID: 32563178 DOI: 10.1016/j.breast.2020.05.013] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 1.5] [Reference Citation Analysis]
8 Chan HP, Samala RK, Hadjiiski LM. CAD and AI for breast cancer-recent development and challenges. Br J Radiol 2020;93:20190580. [PMID: 31742424 DOI: 10.1259/bjr.20190580] [Cited by in Crossref: 14] [Cited by in F6Publishing: 13] [Article Influence: 4.7] [Reference Citation Analysis]
9 Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology 2019;293:246-59. [PMID: 31549948 DOI: 10.1148/radiol.2019182627] [Cited by in Crossref: 56] [Cited by in F6Publishing: 42] [Article Influence: 18.7] [Reference Citation Analysis]
10 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]
11 Díaz O, Rodríguez-ruiz A, Gubern-mérida A, Martí R, Chevalier M. Are artificial intelligence systems useful in breast cancer screening programmes? Radiología (English Edition) 2021;63:236-44. [DOI: 10.1016/j.rxeng.2020.11.005] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB, Carlsen JF. Impact of adding breast density to breast cancer risk models: A systematic review. European Journal of Radiology 2020;127:109019. [DOI: 10.1016/j.ejrad.2020.109019] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
13 Pederson HJ, Pruthi S. Personalized Screening and Prevention Based on Genetic Risk of Breast Cancer. Curr Breast Cancer Rep. [DOI: 10.1007/s12609-022-00443-5] [Reference Citation Analysis]
14 Pertuz S, Sassi A, Karivaara-mäkelä M, Holli-helenius K, Lääperi A, Rinta-kiikka I, Arponen O, Kämäräinen J. Micro-parenchymal patterns for breast cancer risk assessment. Biomed Phys Eng Express 2019;5:065008. [DOI: 10.1088/2057-1976/ab42f4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
15 Fowler EEE, Smallwood A, Miltich C, Drukteinis J, Sellers TA, Heine J. Generalized breast density metrics. Phys Med Biol 2018;64:015006. [PMID: 30523909 DOI: 10.1088/1361-6560/aaf307] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
16 Díaz O, Rodríguez-Ruiz A, Gubern-Mérida A, Martí R, Chevalier M. Are artificial intelligence systems useful in breast cancer screening programs? Radiologia (Engl Ed) 2021;63:236-44. [PMID: 33461750 DOI: 10.1016/j.rx.2020.11.006] [Reference Citation Analysis]
17 Heine J, Fowler E, Scott CG, Jensen MR, Shepherd J, Hruska CB, Winham SJ, Brandt KR, Wu FF, Norman AD, Pankratz VS, Miglioretti DL, Kerlikowske K, Vachon CM. Mammographic Variation Measures, Breast Density, and Breast Cancer Risk. AJR Am J Roentgenol 2021;217:326-35. [PMID: 34161135 DOI: 10.2214/AJR.20.22794] [Reference Citation Analysis]
18 Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, Forrai G, Fuchsjäger MH, Helbich TH, Killburn-Toppin F, Lesaru M, Panizza P, Pediconi F, Pijnappel RM, Pinker K, Sardanelli F, Sella T, Thomassin-Naggara I, Zackrisson S, Gilbert FJ, Kuhl CK; European Society of Breast Imaging (EUSOBI). Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 2022. [PMID: 35258677 DOI: 10.1007/s00330-022-08617-6] [Reference Citation Analysis]