title: Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.
abstract: To investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome.
  
  
  
  A computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients.
  
  
  
  The correlation coefficients between automated and radiologist's manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients' PFS (p < 0.01).
  
  
  
  Using a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. yunzhi.wang-1@ou.edu.
authors: Yunzhi
authors: Y
authors: Wang
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
authors: Yuchen
authors: Y
authors: Qiu
authors: Health Science Center of University of Oklahoma, Oklahoma City, OK, 73104, USA.
authors: Theresa
authors: T
authors: Thai
authors: Health Science Center of University of Oklahoma, Oklahoma City, OK, 73104, USA.
authors: Kathleen
authors: K
authors: Moore
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
authors: Hong
authors: H
authors: Liu
authors: School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
authors: Bin
authors: B
authors: Zheng
mesh_terms: D050153:Abdominal Fat / Q000000981:diagnostic imaging*; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D018572:Disease-Free Survival; D004358:Drug Therapy; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016015:Logistic Models; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging / Q000188:drug therapy*; D012189:Retrospective Studies; D016019:Survival Analysis; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents
keywords: Clinical image markers for cancer prognosis prediction; Computer-aided detection (CAD); Prediction of chemotherapy outcome; Quantitative image feature analysis