title: Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.
abstract: OBJECTIVE
  To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy.
  
  
  METHODS
  The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method.
  
  
  RESULTS
  In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC=0.85±0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96±0.03 (p<0.01).
  
  
  CONCLUSIONS
  This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Faranak
authors.initials: F
authors.lastname: Aghaei
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Maxine
authors.initials: M
authors.lastname: Tan
authors.affiliation: Mercy Women's Center, Mercy Health Center, Oklahoma City, Oklahoma 73120.
authors.forename: Alan B
authors.initials: AB
authors.lastname: Hollingsworth
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas, El Paso, Texas 79968.
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000008:administration & dosage; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000473:pathology*; D016903:Drug Monitoring / Q000379:methods*; 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; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents