title: Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography.
abstract: BACKGROUND
  Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).
  
  
  METHODS
  Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.
  
  
  RESULTS
  All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).
  
  
  CONCLUSIONS
  Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.
authors: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. brad.keller@uphs.upenn.edu.
authors: Brad M
authors: BM
authors: Keller
authors: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 203 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, USA. jinboche@mail.med.upenn.edu.
authors: Jinbo
authors: J
authors: Chen
authors: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. dania.daye@gmail.com.
authors: Dania
authors: D
authors: Daye
authors: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. emily.conant@uphs.upenn.edu.
authors: Emily F
authors: EF
authors: Conant
authors: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA. despina.kontos@uphs.upenn.edu.
authors: Despina
authors: D
authors: Kontos
mesh_terms: D001940:Breast / Q000473:pathology; D000071060:Breast Density; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D016022:Case-Control Studies; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D016015:Logistic Models; D042361:Mammary Glands, Human / Q000002:abnormalities* / Q000473:pathology; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D012189:Retrospective Studies; D012307:Risk Factors; D012984:Software