title: Breast cancer: importance of spiculation in computer-aided detection.
abstract: OBJECTIVE
  To determine the prevalence of spiculation in a large series of screening-detected breast cancers appearing as masses on mammograms and to assess the sensitivity of a computer-aided detection (CAD) algorithm that uses spiculation measures in the detection of such lesions.
  
  
  METHODS
  Six hundred seventy-seven consecutive cases of breast cancers detected as masses on mammograms were independently reviewed by three radiologists who determined if the lesions were spiculated. All cancers were then analyzed by the CAD system.
  
  
  RESULTS
  All three radiologists interpreted 375 (55%) of the 677 masses as being spiculated on at least one view. The CAD algorithm correctly marked 322 (86%) of the 375 clearly spiculated masses, with a mean of 0.24 additional mass mark per image. With a looser definition of spiculation, 585 (86%) of the 677 masses were called spiculated by at least one radiologist on one view. The algorithm correctly marked 464 (79%) of the 585 lesions that were spiculated or possibly spiculated.
  
  
  CONCLUSIONS
  Spiculation was clearly present in a majority (55%) of consecutive screening-detected breast cancer masses found on mammograms in a large clinical trial. Incorporation of spiculation measures is, therefore, an important strategy in the detection of breast cancer with CAD. A present-generation CAD algorithm correctly identified a large proportion (86%) of spiculated breast cancers.
authors.affiliation: Department of Radiology, LaGrange Memorial Hospital, LaGrange, IL 60525, USA.
authors.forename: C J
authors.initials: CJ
authors.lastname: Vyborny
authors.forename: T
authors.initials: T
authors.lastname: Doi
authors.forename: K F
authors.initials: KF
authors.lastname: O'Shaughnessy
authors.forename: H M
authors.initials: HM
authors.lastname: Romsdahl
authors.forename: A C
authors.initials: AC
authors.lastname: Schneider
authors.forename: A A
authors.initials: AA
authors.lastname: Stein
mesh_terms: D000465:Algorithms; D001706:Biopsy; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016009:Chi-Square Distribution; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D008403:Mass Screening / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data; D008875:Middle Aged; D015588:Observer Variation