title: Quantitative measures confirm the inverse relationship between lesion spiculation and detection of breast masses.
abstract: OBJECTIVE
  To identify specific mammographic appearances that reduce the mammographic detection of breast cancer.
  
  
  METHODS
  This study received institutional board review approval and all readers gave informed consent. A set of 60 mammograms each consisting of craniocaudal and mediolateral oblique projections were presented to 129 mammogram Breastscreen readers. The images consisted of 20 positive cases with single and multicentric masses in 16 and 4 cases, respectively (resulting in a total of 24 cancers), and readers were asked to identify and locate the lesions. Each lesion was then ranked according to a detectability rating (ie, the number of observers who correctly located the lesion divided by the total number of observers), and this was correlated with breast density, lesion size, and various descriptors of lesion shape and texture.
  
  
  RESULTS
  Negative and positive correlations between lesion detection and density (r = -0.64, P = .007) and size (r = 0.65, P = .005), respectively, were demonstrated. In terms of lesion size and shape, there were significant correlations between the probability of detection and area (r = 0.43, P = .04), perimeter (r = 0.66, P = .0004), lesion elongation (r = 0.49, P = .02), and lesion nonspiculation (r = 0.78, P < .0001).
  
  
  CONCLUSIONS
  The results of this study have identified specific lesion characteristics associated with shape that may contribute to reduced cancer detection. Mammographic sensitivity may be adversely affected without appropriate attention to spiculation.
authors.affiliation: Medical Image Optimisation and Perception Group (MIOPeG), Medical Imaging & Radiation Sciences, Faculty Research Group, The Faculty of Health Sciences, The University of Sydney, Cumberland Campus, 75 East Street, Lidcombe, NSW 2141, Australia. mraw2971@uni.sydney.edu.au
authors.forename: Mohammad A
authors.initials: MA
authors.lastname: Rawashdeh
authors.forename: Roger M
authors.initials: RM
authors.lastname: Bourne
authors.forename: Elaine A
authors.initials: EA
authors.lastname: Ryan
authors.forename: Warwick B
authors.initials: WB
authors.lastname: Lee
authors.forename: Mariusz W
authors.initials: MW
authors.lastname: Pietrzyk
authors.forename: Warren M
authors.initials: WM
authors.lastname: Reed
authors.forename: Natacha
authors.initials: N
authors.lastname: Borecky
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Brennan
mesh_terms: D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic