Urinary nucleosides as potential tumor markers evaluated by learning vector quantization
Artificial Intelligence in Medicine
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Nucleosides in human urine are of interest as a biochemical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. A reversed-phase high-performance liquid chromatography (RP-HPLC) method with photodiode-array detection was used to quantitatively analyze urinary normal and modified nucleosides. 55 persons with malignant tumors of various types, 13 persons with benign tumors and 41 healthy controls were investigated within a clinical intervention study. Artificial neural networks (ANN) have been used as a practical pattern recognition tool to distinguish cancer patients from healthy persons. Using a multilayer perceptron (MPL), a specificity of 85%, and a sensitivity of 97% in differentiation between tumor patients and healthy persons was achieved. The differentiation between benign and malignant tumors had a sensitivity of 60% and a specificity of 84%. These results verify the usefulness of ANN and the RP-HPLC method for tumor recognition in agreement with existing studies.