title: Mammography image quality: model for predicting compliance with posterior nipple line criterion.
abstract: OBJECTIVE
  To develop a model using measurements of pectoral muscle width and length together with the acceptability of the posterior nipple line criteria (PNL) to predict the acceptability of the presentation of the pectoral muscle in the mediolateral oblique view of the breast.
  
  
  METHODS
  A total of 400 mediolateral oblique mammogram images were randomly selected from BreastScreen NSW South West, Australia. Measurements of length and width of the pectoral muscle and the acceptability of the pectoral muscle position relative to the PNL were recorded. Data analysis involved logistic regression and ROC analysis to test the predictors of width and length and the performance of the model. The model was then used to predict the outcome of acceptable or unacceptable PNL criterion for each case.
  
  
  RESULTS
  The estimated odds ratio for an increase of 10mm was 1.98 (CI=1.68, 2.34) for the length predictor and 2.14 (CI=1.56, 2.93) for the width predictor. A cut off point of 0.6083 was derived from the training set and applied with the developed model to the test set. The area under the ROC curve was 0.9339 demonstrating an accurate model.
  
  
  CONCLUSIONS
  This paper describes a model to predict the acceptability of the PNL criterion using the width and length of the pectoral muscle. This model could be used in the automated assessment of image quality which has the potential to enhance the consistency in mammographic image quality evaluation. Optimising image quality contributes to increased accuracy in radiological interpretation, which maximises the early detection of breast cancer and potentially reduces mortality rates.
authors.affiliation: Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, PO Box 170, Lidcombe, NSW 1825, Australia. kspuur@csu.edu.au
authors.forename: Kelly
authors.initials: K
authors.lastname: Spuur
authors.forename: Wai Tak
authors.initials: WT
authors.lastname: Hung
authors.forename: Ann
authors.initials: A
authors.lastname: Poulos
authors.forename: Mary
authors.initials: M
authors.lastname: Rickard
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D009558:Nipples / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010369:Pectoralis Muscles / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity