title: Improving CT prediction of treatment response in patients with metastatic colorectal carcinoma using statistical learning theory.
abstract: BACKGROUND
  Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. To address this problem, we have chosen to study patients with metastatic colorectal carcinoma to learn whether statistical learning theory can improve the performance of radiologists using CT in predicting patient treatment response to therapy compared with the more traditional RECIST (Response Evaluation Criteria in Solid Tumors) standard.
  
  
  RESULTS
  Predictions of survival after 8 months in 38 patients with metastatic colorectal carcinoma using the Support Vector Machine (SVM) technique improved 30% when using additional information compared to WHO (World Health Organization) or RECIST measurements alone. With both Logistic Regression (LR) and SVM, there was no significant difference in performance between WHO and RECIST. The SVM and LR techniques also demonstrated that one radiologist consistently outperformed another.
  
  
  CONCLUSIONS
  This preliminary research study has demonstrated that SLT algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both RECIST and WHO scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from CT and/or MRI scans be added to the SLT feature vector for processing.
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY 13903-6000, USA. wland@binghamton.edu
authors.forename: Walker H
authors.initials: WH
authors.lastname: Land
authors.forename: Dan
authors.initials: D
authors.lastname: Margolis
authors.forename: Ronald
authors.initials: R
authors.lastname: Gottlieb
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Krupinski
authors.forename: Jack Y
authors.initials: JY
authors.lastname: Yang
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor; D002277:Carcinoma / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000401:mortality / Q000556:secondary*; D015179:Colorectal Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000401:mortality / Q000473:pathology; D006801:Humans; D016015:Logistic Models; D016017:Odds Ratio; D012372:ROC Curve; D012984:Software; D016019:Survival Analysis; D014057:Tomography, X-Ray Computed
chemical_list: D014408:Biomarkers, Tumor