Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form

Br J Radiol. 2006 May;79(941):378-82. doi: 10.1259/bjr/24769358.

Abstract

Limitations of area based measures of breast density have led several research groups to develop volumetric measures of breast density, for use in predicting risk and in epidemiological research. In this paper, we describe our initial experiences using an automated algorithm (standard mammogram form, SMF) to estimate the volume of the breast that is dense from digitized film mammograms. We performed analyses on 3816 mammograms of 626 women, who were part of the Glasgow Alumni Cohort and had mammograms taken within the Scottish Breast Screening Programme between 1989 and 2002. Absolute volume of dense breast tissue (SMF volume) and the percentage of the volume of the breast that is dense (SMF%) were calculated. The median (interquartile range) of SMF volume was 66 cm3 (48 to 98), and of SMF% was 23.4% (18.6 to 29.7). SMF%, but not SMF volume, was positively related to a six category classification (SCC) of visually assigned area-based breast density (increase in ln(SMF%) per category increase in SCC: 0.04% (95% CI: 0.03-0.05). The SMF algorithm produced lower SMF volume for craniocaudal (CC) compared with mediolateral oblique (MLO) views, but CC/MLO differences for SMF% were small. The mean right/left difference for ln(SMF volume) was -0.027 cm3 (95% confidence interval (CI) -0.044 to -0.009) and of ln(SMF%) was 0.005% (95% CI -0.008% to 0.019%). We present these initial data as a background for future analytical work using SMF.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Absorptiometry, Photon
  • Adult
  • Aged
  • Algorithms*
  • Breast Neoplasms / diagnosis*
  • Breast Neoplasms / diagnostic imaging
  • Female
  • Humans
  • Mammography*
  • Middle Aged
  • Models, Biological
  • Models, Statistical*
  • Pilot Projects
  • Radiographic Image Enhancement
  • Radiographic Image Interpretation, Computer-Assisted*
  • Scotland
  • Statistics, Nonparametric