title: Circumscribed mass lesions on mammography: dynamic contrast-enhanced MR imaging to differentiate malignancy and benignancy.
abstract: OBJECTIVE
  We evaluated the magnetic resonance (MR) features of breast lesions showing circumscribed mass on mammography to understand the characteristics that differentiate malignancy and benignancy.
  
  
  METHODS
  Our institutional review board approved the study, and informed consent was waived. Using logistic regression analysis, we examined morphologic and kinetic MR imaging data of 90 breast lesions (43 malignant, 47 benign) that showed circumscribed mass on mammography.
  
  
  RESULTS
  Features identified as having high odds for malignancy included: rim enhancement (odds ratio, 70.894; 95% confidence interval (CI), 7.525-667.938); heterogeneous enhancement (odds ratio, 10.839; 95% CI, 1.032-113.856); and washout dynamic pattern (odds ratio, 46.262; 95% CI, 3.716-575.901). Combinations of washout dynamic pattern and either rim or heterogeneous enhancement reflected excessively high prediction probability for malignancy (>0.95), whereas combinations lacking washout dynamic pattern and with either homogeneous enhancement or dark internal septation revealed excessively low prediction probability for malignancy (<0.05).
  
  
  CONCLUSIONS
  Breast cancers with circumscribed mass on mammography could be differentiated from benign masses using internal enhancement and the kinetic pattern of contrast-enhanced breast MR imaging.
authors.affiliation: Department of Clinical Radiology, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Okafuji
authors.forename: Hidetake
authors.initials: H
authors.lastname: Yabuuchi
authors.forename: Hiroyasu
authors.initials: H
authors.lastname: Soeda
authors.forename: Yoshio
authors.initials: Y
authors.lastname: Matsuo
authors.forename: Takeshi
authors.initials: T
authors.lastname: Kamitani
authors.forename: Shuji
authors.initials: S
authors.lastname: Sakai
authors.forename: Masamitsu
authors.initials: M
authors.lastname: Hatakenaka
authors.forename: Syoji
authors.initials: S
authors.lastname: Kuroki
authors.forename: Eriko
authors.initials: E
authors.lastname: Tokunaga
authors.forename: Hidetaka
authors.initials: H
authors.lastname: Yamamoto
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Honda
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography; D008875:Middle Aged; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011336:Probability; D055815:Young Adult