title: Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.
abstract: BACKGROUND
  In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge.
  
  
  OBJECTIVE
  To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials.
  
  
  METHODS
  A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline.
  
  
  RESULTS
  The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%).
  
  
  CONCLUSIONS
  This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials.
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA qiuyuchen@ou.edu.
authors.forename: Yuchen
authors.initials: Y
authors.lastname: Qiu
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: Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma, USA.
authors.forename: Scott
authors.initials: S
authors.lastname: McMeekin
authors.affiliation: Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma, USA.
authors.forename: Theresa
authors.initials: T
authors.lastname: Thai
authors.affiliation: Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Ding
authors.affiliation: Health Science Center of University of Oklahoma, Oklahoma City, Oklahoma, USA.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Moore
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA.
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000628:therapy*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Computed tomography (CT); adults; clinical trial of treating ovarian cancer; computer-aided detection; genital; prediction of 6-month progression-free survival; progress-free survival (PFS); quantitative CT image feature analysis; reproductive; treatment effects