Nuclear Medicine & Radiation Therapy

Steven H Lin

Publications

• Research Article
  Textural Features on Computed Tomography Scans Predict Overall Survival in Patients with Esophageal Cancer
  Author(s): Jingya Wang, Laurence E Court, Arvind Rao, Roland Bassett, Joon K Lee, Luke Hunter, Bevan Myles, Zhongxing Liao and Steven H LinJingya Wang, Laurence E Court, Arvind Rao, Roland Bassett, Joon K Lee, Luke Hunter, Bevan Myles, Zhongxing Liao and Steven H Lin
  
  Abstract Purpose: To predict overall survival (OS) in non-metastatic esophageal cancer using texture analysis of pre-therapy computed tomography (CT) images. Materials and Methods: Records from 762 non-metastatic esophageal cancer patients with non-contrast CT scans (obtained from 1998-2011) before receiving chemoradiation were retrospectively reviewed. 328 quantitative image features were extracted from the esophageal gross tumor volume (GTV). A random survival forest model compared how well five of these features (entropy, histogram 10th percentile, volume, volume-to-area ratio, fraction GTV pruned after thresholding) predicted OS versus all 328 features. Cox proportional hazards modeling was used to derive scores, based on these five features, which could stratify patients by survival in a training set consisting of 50% of the 762 cases, chosen randomly from the d.. Read More»
  DOI: 10.4172/2155-9619.1000196

  Abstract PDF