Classification of prostate magnetic resonance spectra using Support Vector Machine

Prostate cancer is the most common cancer in men over 50 years of age and it has been shown that nuclear magnetic resonance spectra are sensitive enough to distinguish normal and cancer tissues. In this paper, we propose a classification technique of spectra from magnetic resonance spectroscopy. We studied automatic classification with and without quantification of metabolite signals. The dataset is composed of 22 patient datasets with a biopsy-proven cancer, from which we extracted 2464 spectra from the whole prostate and of which 1062 were localised in the peripheral zone. The spectra were manually classed into 3 different categories by a spectroscopist with 4 years experience in clinical spectroscopy of prostate cancer: undetermined, healthy and pathologic. We used different preprocessing methods (module, phase correction only, phase correction and baseline correction) as input for Support Vector Machine and for Multilayer Perceptron, and we compared the results with those from the expert. If we class only healthy and pathologic spectra we reach a total error rate of 4.51%. However, if we class all spectra (undetermined, healthy and pathologic) the total error rate rises to 11.49%. We have shown in this paper that the best results are obtained using the pre-processed spectra without quantification as input for the classifiers and we confirm that Support Vector Machine are more efficient than Multilayer Perceptron in processing high dimensional data.

► Automatic supervised classification of spectra from nuclear magnetic resonance of prostate. ► Automatic and efficient separation of healthy spectra from cancer spectra. ► Comparison and selection of several pre-processing on spectra (phase and baseline correction, normalization, estimation of concentration of metabolites). ► Comparison of two classification methods: Support Vector Machine and Neural Network. ► We reach the minimum classification error rate of 4.51% by using SVM (sensitivity: 83.57%, specificity: 98.11%).