Nonnegative matrix factorization of DCE-MRI for prostate cancer classification

Abstract

The purpose of the study is to analyze whether certain components can be extracted in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the classification of prostate cancer (PCa). Nonnegative matrix factorization (NMF) was used to extract the characteristic curve from DCE-MRI. The peak sharpness of the characteristic curve was evaluated to classify prostates with and without PCa. Results showed that the peak sharpness of the characteristic curve was significantly different in prostates with and without PCa (p = 0.008) and the area under the receiver operating characteristic curve was 0.86 ± 0.08. We conclude that the NMF can decompose DCE-MRI into components and the peak sharpness of the characteristic curve has the promise to classify prostates with and without PCa accurately.