title: Evaluation of breast cancer with a computer-aided detection system by mammographic appearance and histopathology.
abstract: BACKGROUND
  The objective of this study was to evaluate the performance of a computer-aided detection (CAD) system for the detection of breast cancer, based on mammographic appearance and histopathology.
  
  
  METHODS
  From 1000 consecutive screening mammograms from women with biopsy-proven breast carcinoma, 273 mammograms were selected randomly for retrospective evaluation by CAD. The sensitivity of the CAD system for breast cancer was assessed from the proportion of masses and microcalcifications detected. The corresponding tumor histopathologies also were evaluated. Normal mammograms (n = 155 patients) were used to determine the false-positive rate of the system.
  
  
  RESULTS
  Of the 273 breast carcinomas, 149 appeared mammographically as masses, and 88 appeared as microcalcifications, including 36 carcinomas that presented as mixed lesions. The CAD system marked 125 of 149 masses correctly (84%), marked 86 of 88 microcalcifications correctly (98%), and marked 32 of 36 of mixed lesions correctly (89%.). The system showed a high sensitivity for the detection of ductal carcinoma in situ (95%; 73 of 77 lesions), invasive lobular carcinoma (95%; 18 of 19 lesions), invasive ductal carcinoma (85%; 125 of 147 lesions), and invasive mammary carcinoma (90%; 27 of 30 lesions). The highest CAD system sensitivity was for all invasive carcinomas that presented as microcalcifications (100%). On normal mammograms, there was an average of 1.3 false-positive CAD marks per image.
  
  
  CONCLUSIONS
  The CAD system correctly marked a large majority of biopsy-proven breast cancers, with a greater sensitivity for lesions with microcalcifications and without significant impact of performance based on tumor histopathology. CAD was highly effective in detecting invasive lobular carcinoma (sensitivity, 95%) and ductal carcinoma in situ (sensitivity, 95%). CAD represents a useful tool for the detection of breast cancer.
authors.affiliation: Department of Radiology, The George Washington University Medical Center, Washington, DC, USA. rbrem@mfa.gwu.edu
authors.forename: Rachel F
authors.initials: RF
authors.lastname: Brem
authors.forename: Jocelyn A
authors.initials: JA
authors.lastname: Rapelyea
authors.forename: Gilat
authors.initials: G
authors.lastname: Zisman
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Hoffmeister
authors.forename: Martin P
authors.initials: MP
authors.lastname: Desimio
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis; D018275:Carcinoma, Lobular / Q000175:diagnosis; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged