PURPOSE: To categorize the disease severity of mammary tumors in an animal model through the application of a novel tumor permeability mixture model within a hierarchical modeling framework. MATERIALS AND METHODS: Thirty-six rats with mammary tumors of varying grade were imaged via dynamic contrast-enhanced (CE) MRI using albumin-(Gd-DTPA)30. Time-dependent contrast agent concentration curves for blood and tumor tissue were obtained and a mathematical model of microvascular blood-tissue exchange was developed under the hypothesis that endothelial integrity is disrupted in a manner proportional to the degree of malignancy, with benign tumors showing no disruption of the vasculature endothelium. This permeability model was incorporated into a statistical model for the benign and malignant tumor subgroups that enabled automatic subject classification. The structural and statistical models were implemented using the software Nonlinear Mixed Effects Modeling (NONMEM) to statistically separate subjects into the two subgroups. RESULTS: Individual tumor classifications (as benign or malignant) were evaluated against the Scarff-Bloom-Richardson microscopic scoring method as applied to the tumor histology of each subject. The model-based classification resulted in 90.9% sensitivity, 92.9% specificity, and 91.7% accuracy. CONCLUSION: Mixture model analysis provides a robust method for subject classification without user intervention and bias. Although the present results are promising, additional research is needed to further evaluate this technique for diagnostic purposes. (c) 2005 Wiley-Liss, Inc.
PURPOSE: To categorize the disease severity of mammary tumors in an animal model through the application of a novel tumor permeability mixture model within a hierarchical modeling framework. MATERIALS AND METHODS: Thirty-six rats with mammary tumors of varying grade were imaged via dynamic contrast-enhanced (CE) MRI using albumin-(Gd-DTPA)30. Time-dependent contrast agent concentration curves for blood and tumor tissue were obtained and a mathematical model of microvascular blood-tissue exchange was developed under the hypothesis that endothelial integrity is disrupted in a manner proportional to the degree of malignancy, with benign tumors showing no disruption of the vasculature endothelium. This permeability model was incorporated into a statistical model for the benign and malignant tumor subgroups that enabled automatic subject classification. The structural and statistical models were implemented using the software Nonlinear Mixed Effects Modeling (NONMEM) to statistically separate subjects into the two subgroups. RESULTS: Individual tumor classifications (as benign or malignant) were evaluated against the Scarff-Bloom-Richardson microscopic scoring method as applied to the tumor histology of each subject. The model-based classification resulted in 90.9% sensitivity, 92.9% specificity, and 91.7% accuracy. CONCLUSION: Mixture model analysis provides a robust method for subject classification without user intervention and bias. Although the present results are promising, additional research is needed to further evaluate this technique for diagnostic purposes. (c) 2005 Wiley-Liss, Inc.