title: Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.
abstract: BACKGROUND
  To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients.
  
  
  METHODS
  Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis.
  
  
  RESULTS
  Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%).
  
  
  CONCLUSIONS
  Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.
authors.affiliation: Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yue
authors.affiliation: Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Arsen
authors.initials: A
authors.lastname: Osipov
authors.affiliation: Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Benedick
authors.initials: B
authors.lastname: Fraass
authors.affiliation: Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Howard
authors.initials: H
authors.lastname: Sandler
authors.affiliation: Department of Biostatistics and Bioinformatics, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Nissen
authors.affiliation: Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Hendifar
authors.affiliation: Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Tuli
keywords: PET/CT; Pancreatic adenocarcinoma (PA); image texture; radiomics; therapy response