title: A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations.
abstract: OBJECTIVE
  To investigate the feasibility of applying a new quantitative image analysis method to improve breast cancer diagnosis performance using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) by integrating background parenchymal enhancement (BPE) features into the decision making process.
  
  
  METHODS
  A dataset involving 115 DCE-MRI examinations was used in this study. Each examination depicts one identified suspicious breast tumor. Among them, 75 cases were verified as malignant and 40 were benign by the biopsy results. A computer-aided detection scheme was applied to segment breast regions and the suspicious tumor depicted on the sequentially scanned MR images of each case. We then computed 18 kinetic features in which 6 were computed from the segmented breast tumor and 12 were BPE features from the background parenchymal regions (excluding the tumor). Support vector machine (SVM) based statistical learning classifiers were trained and optimized using different combinations of features that were computed either from tumor only or from both tumor and BPE. Each SVM was tested using a leave-one-case-out validation method and assessed using an area under the receiver operating characteristic curve (AUC).
  
  
  RESULTS
  When using kinetic features computed from tumors only, the maximum AUC is 0.865 ± 0.035. After fusing with the BPE features, AUC increased to 0.919 ± 0.029. At 90% specificity, the tumor classification sensitivity increased by 13.2%.
  
  
  CONCLUSIONS
  The proposed quantitative BPE features provide valuable supplementary information to the kinetic features of breast tumors in DCE-MRI. Their addition to computer-aided diagnosis methodologies could improve breast cancer diagnosis based on DCE-MRI examinations.
authors.affiliation: Department of Biomedical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Yang
authors.affiliation: Department of Biomedical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
authors.forename: Lihua
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
authors.forename: Guoliang
authors.initials: G
authors.lastname: Shao
authors.affiliation: Department of Biomedical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China and School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D005240:Feasibility Studies; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA