A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

A novel and fully automated mammographic texture analysis for risk prediction: results from two case-control studies

Abstract Background The percentage of mammographic dense tissue (PD) is an important risk factor for breast cancer, and there is some evidence that texture features may further improve predictive ability. However, relatively little work has assessed or validated textural feature algorithms using raw...

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Bibliographic Details
Main Authors: Chao Wang, Adam R. Brentnall, Jack Cuzick, Elaine F. Harkness, D. Gareth Evans, Susan Astley
Format: Article
Language:English
Published: BMC 2017-10-01
Series:Breast Cancer Research
Subjects:
Breast density
Texture
Digital mammogram
Risk prediction
Breast cancer
Online Access:http://link.springer.com/article/10.1186/s13058-017-0906-6

Internet

http://link.springer.com/article/10.1186/s13058-017-0906-6

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