Imperfect Correlation of Mammographic and Clinical Breast Tissue Density

被引:4
|
作者
Alipour, Sadaf [1 ,4 ]
Bayani, Leila [2 ]
Saberi, Azin [1 ]
Alikhassi, Afsaneh [2 ]
Hosseini, Ladan [3 ]
Eslami, Bita [4 ]
机构
[1] Arash Womens Hosp, Dept Surg, Tehran, Iran
[2] Arash Womens Hosp, Dept Radiol, Tehran, Iran
[3] Arash Womens Hosp, Res Dev Ctr, Tehran, Iran
[4] Univ Tehran Med Sci, Vali e Asr Reprod Hlth Res Ctr, Tehran, Iran
关键词
Breast; density; clinical examination; mammography; neoplasm; CANCER; RISK; PATTERNS; WOMEN;
D O I
10.7314/APJCP.2013.14.6.3685
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Clinicians determine degree of mammographic density based on tissue firmness on breast examination. The study aimed to compare breast density in mammography and clinical breast examination. Materials and Methods: Six-hundred sixty three women 40 years of age or older were studied. The breast exam density was graded from 1 to 4 by two expert surgeons and the mammographic parenchymal density by two expert radiologists. Then for practical reasons, grades 1 and 2 were considered as low-density and grades 3 and 4 as high-density. Results: High and low densities were detected in 84.5% and 15.5% of clinical breast examinations and 59.7% and 40.3% of mammographies, respectively. The statistical analysis showed a significant difference between the breast tissue densities in breast examination with those in mammography. Conclusions: A clinically dense breast does not necessarily imply a dense mammographic picture.
引用
收藏
页码:3685 / 3688
页数:4
相关论文
共 50 条
  • [41] A review of the influence of mammographic density on breast cancer clinical and pathological phenotype
    Michael S. Shawky
    Cecilia W. Huo
    Michael A. Henderson
    Andrew Redfern
    Kara Britt
    Erik W. Thompson
    Breast Cancer Research and Treatment, 2019, 177 : 251 - 276
  • [42] Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation
    Lehman, Constance D.
    Yala, Adam
    Schuster, Tal
    Dontchos, Brian
    Bahl, Manisha
    Swanson, Kyle
    Barzilay, Regina
    RADIOLOGY, 2019, 290 (01) : 52 - 58
  • [43] Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective
    Mohamed, Aly A.
    Luo, Yahong
    Peng, Hong
    Jankowitz, Rachel C.
    Wu, Shandong
    JOURNAL OF DIGITAL IMAGING, 2018, 31 (04) : 387 - 392
  • [44] Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density
    Mullooly, Maeve
    Bejnordi, Babak Ehteshami
    Palakal, Maya
    Vacek, Pamela M.
    Weaver, Donald L.
    Shepherd, John A.
    Fan, Bo
    Mahmoudzadeh, Amir Pasha
    Wang, Jeff
    Johnson, Jason M.
    Herschorn, Sally D.
    Sprague, Brian L.
    Pfeiffer, Ruth M.
    Brinton, Louise A.
    Sherman, Mark E.
    Beck, Andrew
    Gierach, Gretchen L.
    CANCER RESEARCH, 2017, 77
  • [45] Mammographic Density and Breast Cancer Risk: Evaluation of a Novel Method of Measuring Breast Tissue Volumes
    Boyd, Norman
    Martin, Lisa
    Gunasekar, Anoma
    Melnichouk, Olga
    Maudsley, Gord
    Peressotti, Chris
    Yaffe, Martin
    Minkin, Salomon
    CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, 2009, 18 (06) : 1754 - 1762
  • [46] Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers
    Lusine Yaghjyan
    Andreas Pettersson
    Graham A Colditz
    Laura C Collins
    Stuart J Schnitt
    Andrew H Beck
    Bernard Rosner
    Celine Vachon
    Rulla M Tamimi
    British Journal of Cancer, 2015, 113 : 1104 - 1113
  • [47] Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers
    Yaghjyan, Lusine
    Pettersson, Andreas
    Colditz, Graham A.
    Collins, Laura C.
    Schnitt, Stuart J.
    Beck, Andrew H.
    Rosner, Bernard
    Vachon, Celine
    Tamimi, Rulla M.
    BRITISH JOURNAL OF CANCER, 2015, 113 (07) : 1104 - 1113
  • [48] Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density
    Mullooly, Maeve
    Bejnordi, Babak Ehteshami
    Pfeiffer, Ruth M.
    Fan, Shaoqi
    Palakal, Maya
    Hada, Manila
    Vacek, Pamela M.
    Weaver, Donald L.
    Shepherd, John A.
    Fan, Bo
    Mahmoudzadeh, Amir Pasha
    Wang, Jeff
    Malkov, Serghei
    Johnson, Jason M.
    Herschorn, Sally D.
    Sprague, Brian L.
    Hewitt, Stephen
    Brinton, Louise A.
    Karssemeijer, Nico
    van der Laak, Jeroen
    Beck, Andrew
    Sherman, Mark E.
    Gierach, Gretchen L.
    NPJ BREAST CANCER, 2019, 5 (1)
  • [49] Vision 20/20: Mammographic breast density and its clinical applications
    Ng, Kwan-Hoong
    Lau, Susie
    MEDICAL PHYSICS, 2015, 42 (12) : 7059 - 7077
  • [50] Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk
    Kerlikowske, Karla
    Ichikawa, Laura
    Miglioretti, Diana L.
    Buist, Diana S. M.
    Vacek, Pamela M.
    Smith-Bindman, Rebecca
    Yankaskas, Bonnie
    Carney, Patricia A.
    Ballard-Barbash, Rachel
    JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2007, 99 (05) : 386 - 395