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 full field digital mammograms (FFDM).

Method

A case-control study nested within a screening cohort (age 46-73 years) from Manchester UK was used to develop a texture feature risk score (264 cases diagnosed at the same time as mammogram of the contralateral breast, 787 controls) using the least absolute shrinkage and selection operator (LASSO) method for 112 features, and validated in a second case-control study from the same cohort but with cases diagnosed after the index mammogram (317 c

SUBMITTER: Wang C 

PROVIDER: S-EPMC5648465 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature