Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adreno- cortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototype- based classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the de- tection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a non- invasive high-throughput diagnostic tool.