title: Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics.
abstract: OBJECTIVE
  To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer.
  
  
  METHODS
  This study included 20 patients who underwent trimodality therapy (CRT+surgery) and underwent 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]max, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC.
  
  
  RESULTS
  When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)-results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model.
  
  
  CONCLUSIONS
  The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
authors.forename: Shan
authors.initials: S
authors.lastname: Tan
authors.affiliation: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Wengen
authors.initials: W
authors.lastname: Chen
authors.affiliation: Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Seth
authors.initials: S
authors.lastname: Kligerman
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Grace
authors.initials: G
authors.lastname: Kim
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Warren D
authors.initials: WD
authors.lastname: D'Souza
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland.
authors.forename: Mohan
authors.initials: M
authors.lastname: Suntharalingam
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland. Electronic address: wlu@umm.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Lu
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging / Q000473:pathology / Q000628:therapy; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging / Q000473:pathology / Q000628:therapy; D059186:Chemoradiotherapy, Adjuvant / Q000379:methods*; D004938:Esophageal Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000628:therapy*; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D064847:Multimodal Imaging / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D012372:ROC Curve; D019275:Radiopharmaceuticals; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed; D016896:Treatment Outcome; D047368:Tumor Burden
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18