title: Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging.
abstract: Perfusion computed tomography (CTp) is an emerging functional imaging modality that uses physiological models to quantify characteristics pertaining to the passage of fluid through blood vessels. Perfusion characteristics provide physiological correlates for neovascularization induced by tumor angiogenesis. Thus CTp offers promise as a non-invasive quantitative functional imaging tool for cancer detection, prognostication, and treatment monitoring. In this article, we develop a Bayesian probabilistic framework for simultaneous supervised classification of multivariate correlated objects using separable covariance. The classification approach is applied to discriminate between regions of liver that contain pathologically verified metastases from normal liver tissue using five perfusion characteristics. The hepatic regions tend to be highly correlated due to common vasculature. We demonstrate that simultaneous Bayesian classification yields dramatic improvements in performance in the presence of strong correlation among intra-subject units, yet remains competitive with classical methods in the presence of weak or no correlation.
authors: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Yuan
authors: Y
authors: Wang
authors: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Brian P
authors: BP
authors: Hobbs
authors: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Jianhua
authors: J
authors: Hu
authors: Department of Diagnostic Radiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Chaan S
authors: CS
authors: Ng
authors: Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
authors: Kim-Anh
authors: KA
authors: Do
mesh_terms: D000792:Angiography / Q000379:methods*; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009362:Neoplasm Metastasis / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Bayesian decision analysis; Cancer detection; Metastatic liver cancer; Perfusion imaging; Spatial correlation