title: Kinetic Curve Type Assessment for Classification of Breast Lesions Using Dynamic Contrast-Enhanced MR Imaging.
abstract: OBJECTIVE
  The aim of this study was to employ a kinetic model with dynamic contrast enhancement-magnetic resonance imaging to develop an approach that can efficiently distinguish malignant from benign lesions.
  
  
  METHODS
  A total of 43 patients with 46 lesions who underwent breast dynamic contrast enhancement-magnetic resonance imaging were included in this retrospective study. The distribution of malignant to benign lesions was 31/15 based on histological results. This study integrated a single-compartment kinetic model and dynamic contrast enhancement-magnetic resonance imaging to generate a kinetic modeling curve for improving the accuracy of diagnosis of breast lesions. Kinetic modeling curves of all different lesions were analyzed by three experienced radiologists and classified into one of three given types. Receiver operating characteristic and Kappa statistics were used for the qualitative method. The findings of the three radiologists based on the time-signal intensity curve and the kinetic curve were compared.
  
  
  RESULTS
  An average sensitivity of 82%, a specificity of 65%, an area under the receiver operating characteristic curve of 0.76, and a positive predictive value of 82% and negative predictive value of 63% was shown with the kinetic model (p = 0.017, 0.052, 0.068), as compared to an average sensitivity of 80%, a specificity of 55%, an area under the receiver operating characteristic of 0.69, and a positive predictive value of 79% and negative predictive value of 57% with the time-signal intensity curve method (p = 0.003, 0.004, 0.008). The diagnostic consistency of the three radiologists was shown by the κ-value, 0.857 (p<0.001) with the method based on the time-signal intensity curve and 0.826 (p<0.001) with the method of the kinetic model.
  
  
  CONCLUSIONS
  According to the statistic results based on the 46 lesions, the kinetic modeling curve method showed higher sensitivity, specificity, positive and negative predictive values as compared with the time-signal intensity curve method in lesion classification.
authors.affiliation: Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung City, Taiwan.|Department of Radiation Oncology, China Medical University Hospital, Taichung City, Taiwan.
authors.forename: Shih-Neng
authors.initials: SN
authors.lastname: Yang
authors.affiliation: Department of Radiation Oncology, Tri-Service General Hospital, Taipei City, Taiwan.
authors.forename: Fang-Jing
authors.initials: FJ
authors.lastname: Li
authors.affiliation: Department of Radiology, China Medical University Hospital, Taichung City, Taiwan.
authors.forename: Jun-Ming
authors.initials: JM
authors.lastname: Chen
authors.affiliation: Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology, Tri-Service General Hospital, Taipei City, Taiwan.
authors.forename: Yen-Hsiu
authors.initials: YH
authors.lastname: Liao
authors.affiliation: Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung City, Taiwan.|Department of Bioinformatics and Medical Engineering, Asia University, Taichung City, Taiwan.
authors.forename: Tzung-Chi
authors.initials: TC
authors.lastname: Huang
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011859:Radiography