title: Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
abstract: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy.
  
  
  
  A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance.
  
  
  
  From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05).
  
  
  
  This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA.
authors.forename: Faranak
authors.initials: F
authors.lastname: Aghaei
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA.
authors.forename: Maxine
authors.initials: M
authors.lastname: Tan
authors.affiliation: Mercy Women's Center, Mercy Health Center, Oklahoma City, Oklahoma, USA.
authors.forename: Alan B
authors.initials: AB
authors.lastname: Hollingsworth
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA. Bin.Zheng-1@ou.edu.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000328:Adult; D000368:Aged; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy* / Q000473:pathology; D017024:Chemotherapy, Adjuvant; D016903:Drug Monitoring / Q000379:methods*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D000970:Antineoplastic Agents
keywords: assessment of breast cancer prognosis; bilateral asymmetry of parenchyma breast MR enhancement; dynamic contrast-enhanced breast magnetic resonance imaging; quantitative image feature analysis; tumor response to neoadjuvant chemotherapy