Literature DB >> 179369

Breast patterns as an index of risk for developing breast cancer.

J N Wolfe.   

Abstract

The radiographic appearance of the breast parenchyma provides a method of predicting who will develop a breast cancer. This paper describes a restrospective study of 7,214 patients. On the basis of the radiographic appearance of the breast parenchyma, patients were placed into one of four groups of risk for developing carcinoma of the breast. Follow-up studies revealed a stepwise progression in the incidence of developing carcinoma of the breast at least 6 months after the radiographic examination. In one of the two substudies, there was a 37 times greater incidence for those at highest risk compared to the low risk group. The classifications presented are thought to be of value in the everyday practice of mammography as well as in planning screening programs.

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Year:  1976        PMID: 179369     DOI: 10.2214/ajr.126.6.1130

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  191 in total

Review 1.  Mammographic densities as a marker of human breast cancer risk and their use in chemoprevention.

Authors:  N F Boyd; L J Martin; J Stone; C Greenberg; S Minkin; M J Yaffe
Journal:  Curr Oncol Rep       Date:  2001-07       Impact factor: 5.075

Review 2.  Clinical and epidemiological issues in mammographic density.

Authors:  Valentina Assi; Jane Warwick; Jack Cuzick; Stephen W Duffy
Journal:  Nat Rev Clin Oncol       Date:  2011-12-06       Impact factor: 66.675

Review 3.  Breast tissue composition and susceptibility to breast cancer.

Authors:  Norman F Boyd; Lisa J Martin; Michael Bronskill; Martin J Yaffe; Neb Duric; Salomon Minkin
Journal:  J Natl Cancer Inst       Date:  2010-07-08       Impact factor: 13.506

4.  Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study.

Authors:  Huanjun Ding; Sabee Molloi
Journal:  Phys Med Biol       Date:  2012-07-06       Impact factor: 3.609

5.  International Consortium on Mammographic Density: Methodology and population diversity captured across 22 countries.

Authors:  Valerie A McCormack; Anya Burton; Isabel dos-Santos-Silva; John H Hipwell; Caroline Dickens; Dorria Salem; Rasha Kamal; Mikael Hartman; Charmaine Pei Ling Lee; Kee-Seng Chia; Vahit Ozmen; Mustafa Erkin Aribal; Anath Arzee Flugelman; Martín Lajous; Ruy Lopez-Riduara; Megan Rice; Isabelle Romieu; Giske Ursin; Samera Qureshi; Huiyan Ma; Eunjung Lee; Carla H van Gils; Johanna O P Wanders; Sudhir Vinayak; Rose Ndumia; Steve Allen; Sarah Vinnicombe; Sue Moss; Jong Won Lee; Jisun Kim; Ana Pereira; Maria Luisa Garmendia; Reza Sirous; Mehri Sirous; Beata Peplonska; Agnieszka Bukowska; Rulla M Tamimi; Kimberly Bertrand; Chisato Nagata; Ava Kwong; Celine Vachon; Christopher Scott; Beatriz Perez-Gomez; Marina Pollan; Gertraud Maskarinec; Graham Giles; John Hopper; Jennifer Stone; Nadia Rajaram; Soo-Hwang Teo; Shivaani Mariapun; Martin J Yaffe; Joachim Schüz; Anna M Chiarelli; Linda Linton; Norman F Boyd
Journal:  Cancer Epidemiol       Date:  2015-12-24       Impact factor: 2.984

6.  Randomized Double-Blind Placebo-Controlled Biomarker Modulation Study of Vitamin D Supplementation in Premenopausal Women at High Risk for Breast Cancer (SWOG S0812).

Authors:  Katherine D Crew; Garnet L Anderson; Dawn L Hershman; Mary Beth Terry; Parisa Tehranifar; Danika L Lew; Monica Yee; Eric A Brown; Sebastien S Kairouz; Nafisa Kuwajerwala; Therese Bevers; John E Doster; Corrine Zarwan; Laura Kruper; Lori M Minasian; Leslie Ford; Banu Arun; Marian Neuhouser; Gary E Goodman; Powel H Brown
Journal:  Cancer Prev Res (Phila)       Date:  2019-05-28

7.  Adaptive multi-cluster fuzzy C-means segmentation of breast parenchymal tissue in digital mammography.

Authors:  Brad Keller; Diane Nathan; Yan Wang; Yuanjie Zheng; James Gee; Emily Conant; Despina Kontos
Journal:  Med Image Comput Comput Assist Interv       Date:  2011

8.  Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Authors:  Songfeng Li; Jun Wei; Heang-Ping Chan; Mark A Helvie; Marilyn A Roubidoux; Yao Lu; Chuan Zhou; Lubomir M Hadjiiski; Ravi K Samala
Journal:  Phys Med Biol       Date:  2018-01-09       Impact factor: 3.609

9.  Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Meng-Kang Hsieh; Lauren Pantalone; Emily F Conant; Despina Kontos
Journal:  Acad Radiol       Date:  2018-02-01       Impact factor: 3.173

10.  Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Authors:  Richard Ha; Peter Chang; Jenika Karcich; Simukayi Mutasa; Eduardo Pascual Van Sant; Michael Z Liu; Sachin Jambawalikar
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

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