title: Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images.
abstract: OBJECTIVE
  The aim of this study was to explore the use of texture features generated from liver computed tomographic (CT) datasets as potential image-based indicators of patient response to radioembolization (RE) with yttrium-90 ((90)Y) resin microspheres, an emerging locoregional therapy for advanced-stage liver cancer.
  
  
  METHODS
  Overall posttherapy survival and percent change in serologic tumor marker at 3 months posttherapy represent the primary clinical outcomes in this study. Thirty advanced-stage liver cancer cases (primary and metastatic) treated with RE over a 3-year period were included. Texture signatures for tumor regions, which were delineated to reveal boundaries with normal regions, were computed from pretreatment contrast-enhanced liver CT studies and evaluated for their ability to classify patient serologic response and survival.
  
  
  RESULTS
  A series of systematic leave-one-out cross-validation studies using soft-margin support vector machine (SVM) classifiers showed hepatic tumor texton and local binary pattern (LBP) signatures both achieve high accuracy (96%) in discriminating subjects in terms of their serologic response. The image-based indicators were also accurate in classifying subjects by survival status (80% and 93% accuracy for texton and LBP signatures, respectively).
  
  
  CONCLUSIONS
  Hepatic texture signatures generated from tumor regions on pretreatment triphasic CT studies were highly accurate in differentiating among subjects in terms of serologic response and survival. These image-based computational markers show promise as potential predictive tools in candidate evaluation for locoregional therapy such as RE.
authors: Center for Biomedical Imaging & Informatics, The Cancer Institute of New Jersey, 195 Little Albany St, Room 3521, New Brunswick, NJ 08903, USA. gensurrh@umdnj.edu
authors: Rebekah H
authors: RH
authors: Gensure
authors: David J
authors: DJ
authors: Foran
authors: Vincent M
authors: VM
authors: Lee
authors: Vyacheslav M
authors: VM
authors: Gendel
authors: Salma K
authors: SK
authors: Jabbour
authors: Darren R
authors: DR
authors: Carpizo
authors: John L
authors: JL
authors: Nosher
authors: Lin
authors: L
authors: Yang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D019275:Radiopharmaceuticals / Q000627:therapeutic use; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016896:Treatment Outcome; D015021:Yttrium Radioisotopes / Q000627:therapeutic use*
chemical_list: D019275:Radiopharmaceuticals; D015021:Yttrium Radioisotopes