Literature DB >> 24722754

Enhancement of mammographic density measures in breast cancer risk prediction.

Abbas Cheddad1, Kamila Czene1, John A Shepherd2, Jingmei Li3, Per Hall1, Keith Humphreys4.   

Abstract

BACKGROUND: Mammographic density is a strong risk factor for breast cancer.
METHODS: We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density.
RESULTS: From analysis of data from a Swedish case-control study (1,286 breast cancer cases and 1,391 control subjects, ages 50-75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75-0.88; P = 6 × 10(-7)] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002).
CONCLUSIONS: MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment. IMPACT: Inclusion of MIP in risk models should be considered for studies using area PD from analog films. ©2014 American Association for Cancer Research.

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Year:  2014        PMID: 24722754      PMCID: PMC5085828          DOI: 10.1158/1055-9965.EPI-13-1240

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  25 in total

1.  Measurement of breast density with dual X-ray absorptiometry: feasibility.

Authors:  John A Shepherd; Karla M Kerlikowske; Rebecca Smith-Bindman; Harry K Genant; Steve R Cummings
Journal:  Radiology       Date:  2002-05       Impact factor: 11.105

2.  Novel use of single X-ray absorptiometry for measuring breast density.

Authors:  John A Shepherd; Lionel Herve; Jessie Landau; Bo Fan; Karla Kerlikowske; Steve R Cummings
Journal:  Technol Cancer Res Treat       Date:  2005-04

3.  The influence of mammographic density on breast tumor characteristics.

Authors:  Louise Eriksson; Kamila Czene; Lena Rosenberg; Keith Humphreys; Per Hall
Journal:  Breast Cancer Res Treat       Date:  2012-06-19       Impact factor: 4.872

4.  Dense Breast Tissue as an Important Risk Factor for Breast Cancer and Implications for Early Detection.

Authors:  Ingrid Schreer
Journal:  Breast Care (Basel)       Date:  2009-04-24       Impact factor: 2.860

5.  Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study.

Authors:  Jun Wei; Heang-Ping Chan; Yi-Ta Wu; Chuan Zhou; Mark A Helvie; Alexander Tsodikov; Lubomir M Hadjiiski; Berkman Sahiner
Journal:  Radiology       Date:  2011-03-15       Impact factor: 11.105

6.  An automated approach for estimation of breast density.

Authors:  John J Heine; Michael J Carston; Christopher G Scott; Kathleen R Brandt; Fang-Fang Wu; Vernon Shane Pankratz; Thomas A Sellers; Celine M Vachon
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2008-11       Impact factor: 4.254

7.  Texture features from mammographic images and risk of breast cancer.

Authors:  Armando Manduca; Michael J Carston; John J Heine; Christopher G Scott; V Shane Pankratz; Kathy R Brandt; Thomas A Sellers; Celine M Vachon; James R Cerhan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-03       Impact factor: 4.254

8.  Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.

Authors:  Hatef Darabi; Kamila Czene; Wanting Zhao; Jianjun Liu; Per Hall; Keith Humphreys
Journal:  Breast Cancer Res       Date:  2012-02-07       Impact factor: 6.466

9.  High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer.

Authors:  Jingmei Li; Laszlo Szekely; Louise Eriksson; Boel Heddson; Ann Sundbom; Kamila Czene; Per Hall; Keith Humphreys
Journal:  Breast Cancer Res       Date:  2012-07-30       Impact factor: 6.466

10.  Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors.

Authors:  Valerie A McCormack; Ralph Highnam; Nicholas Perry; Isabel dos Santos Silva
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-06       Impact factor: 4.254

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  6 in total

Review 1.  Risk determination and prevention of breast cancer.

Authors:  Anthony Howell; Annie S Anderson; Robert B Clarke; Stephen W Duffy; D Gareth Evans; Montserat Garcia-Closas; Andy J Gescher; Timothy J Key; John M Saxton; Michelle N Harvie
Journal:  Breast Cancer Res       Date:  2014-09-28       Impact factor: 6.466

2.  Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer.

Authors:  Abbas Cheddad; Kamila Czene; Mikael Eriksson; Jingmei Li; Douglas Easton; Per Hall; Keith Humphreys
Journal:  PLoS One       Date:  2014-10-20       Impact factor: 3.240

3.  Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.

Authors:  Fredrik Strand; Keith Humphreys; Abbas Cheddad; Sven Törnberg; Edward Azavedo; John Shepherd; Per Hall; Kamila Czene
Journal:  Breast Cancer Res       Date:  2016-10-05       Impact factor: 6.466

4.  E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.

Authors:  Ola Spjuth; Andreas Karlsson; Mark Clements; Keith Humphreys; Emma Ivansson; Jim Dowling; Martin Eklund; Alexandra Jauhiainen; Kamila Czene; Henrik Grönberg; Pär Sparén; Fredrik Wiklund; Abbas Cheddad; Þorgerður Pálsdóttir; Mattias Rantalainen; Linda Abrahamsson; Erwin Laure; Jan-Eric Litton; Juni Palmgren
Journal:  J Am Med Inform Assoc       Date:  2017-09-01       Impact factor: 4.497

5.  Explainable Multimedia Feature Fusion for Medical Applications.

Authors:  Stefan Wagenpfeil; Paul Mc Kevitt; Abbas Cheddad; Matthias Hemmje
Journal:  J Imaging       Date:  2022-04-08

6.  Addition of a polygenic risk score, mammographic density, and endogenous hormones to existing breast cancer risk prediction models: A nested case-control study.

Authors:  Xuehong Zhang; Megan Rice; Shelley S Tworoger; Bernard A Rosner; A Heather Eliassen; Rulla M Tamimi; Amit D Joshi; Sara Lindstrom; Jing Qian; Graham A Colditz; Walter C Willett; Peter Kraft; Susan E Hankinson
Journal:  PLoS Med       Date:  2018-09-04       Impact factor: 11.069

  6 in total

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