title: Differential Diagnosis of Benign and Malignant Breast Tumors Using Apparent Diffusion Coefficient Value Measured Through Diffusion-Weighted Magnetic Resonance Imaging.
abstract: OBJECTIVE
  Apparent diffusion coefficient (ADC) value measurement of nodes in diffusion-weighted imaging was widely used in differentiating different types of human tumors. The aim of this meta-analysis was to evaluate the clinical value of ADC measurement through diffusion-weighted magnetic resonance imaging (DW-MRI) in the differential diagnosis of benign and malignant breast tumors.
  
  
  METHODS
  Relevant studies were identified through computer-based search of databases, which were supplemented through manual search strategies. Case-control studies were selected in adherence with our strict inclusion and exclusion criteria. Statistical analysis was conducted using Stata 12.0 statistical software (StataCorp, College Station, Tex).
  
  
  RESULTS
  Our database searches initially retrieved 602 studies (320 studies in Chinese and 282 studies in English), and 31 studies (18 studies in English and 13 studies in Chinese) were eventually selected for meta-analysis. These 31 case-control studies included a total of 926 normal breast tissues and 2323 breast tumors (911 benign tumors and 1412 malignant tumors). Our meta-analysis showed that ADC values measured through DW-MRI were higher in benign breast tumors compared with malignant breast tumors, and this difference was statistically significant. In addition, the ADC values in the normal breast tissues were markedly higher than the benign breast tumors, which were also at a statistically significant level. Consistent with these observations, the ADC values in the normal breast tissues were significantly higher when compared with the values found in the malignant breast tumors.
  
  
  CONCLUSIONS
  Our data strongly support the conclusion that the ADC value measured through DW-MRI is an important radiographic index for differential diagnosis of benign and malignant breast tumors and is critical to our assessment of the internal structure of tumors.
authors.affiliation: From the *Department of Cardiology, First Bethune Hospital of Jilin University; †Department of Hematology and Oncology, the Second Hospital of Jilin University, Changchun; ‡Department of Oncology, Affiliated Hospital of Beihua University, Jilin; and §Department of Ultrasonic Diagnosis, the Second Hospital of Jilin University, Changchun, China.
authors.forename: Rong-Feng
authors.initials: RF
authors.lastname: Qu
authors.forename: Dong-Rui
authors.initials: DR
authors.lastname: Guo
authors.forename: Zhe-Xing
authors.initials: ZX
authors.lastname: Chang
authors.forename: Jie
authors.initials: J
authors.lastname: Meng
authors.forename: Yan
authors.initials: Y
authors.lastname: Sun
authors.forename: Shu-Hong
authors.initials: SH
authors.lastname: Hao
authors.forename: Guang
authors.initials: G
authors.lastname: Shi
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
mesh_terms: D001940:Breast / Q000473:pathology; D001941:Breast Diseases / Q000175:diagnosis; D001943:Breast Neoplasms / Q000175:diagnosis*; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity