Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-10

AUTHORS

Amanda Eng, Zoe Gallant, John Shepherd, Valerie McCormack, Jingmei Li, Mitch Dowsett, Sarah Vinnicombe, Steve Allen, Isabel dos-Santos-Silva

ABSTRACT

INTRODUCTION: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM). METHODS: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables. RESULTS: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65). CONCLUSIONS: Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments. More... »

PAGES

439

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1

DOI

http://dx.doi.org/10.1186/s13058-014-0439-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003025389

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25239205


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s13058-014-0439-1'


 

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287 schema:name Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
288 rdf:type schema:Organization
 




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