Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Tissue microarray study for classification of breast tumors

Clinical and pathological heterogeneity of breast cancer hinders selection of appropriate treatment for individual cases. Molecular profiling at gene or protein levels may elucidate the biological variance of tumors and provide a new classification system that correlates better with biological, clinical and prognostic parameters. We studied the immunohistochemical profile of a panel of seven important biomarkers using tumor tissue arrays. The tumor samples were then classified with a monothetic (binary variables) clustering algorithm. Two distinct groups of tumors are characterized by the estrogen receptor (ER) status and tumor grade (p = 0.0026). Four biomarkers, c-erbB2, Cox-2, p53 and VEGF, were significantly overexpressed in tumors with the ER-negative (ER-) phenotype. Eight subsets of tumors were further identified according to the expression status of VEGF, c-erbB2 and p53. The malignant potential of the ER-/VEGF+ subgroup was associated with the strong correlations of Cox-2 and c-erb132 with VEGF. Our results indicate that this molecular classification system, based on the statistical analysis of immunohistochemical profiling, is a useful approach for tumor grouping. Some of these subgroups have a relative genetic homogeneity that may allow further study of specific genetically-controlled metabolic pathways. This approach may hold great promise in rationalizing the application of different therapeutic strategies for different subgroups of breast tumors. (C) 2003 Elsevier Inc. All rights reserved.