Nonnegative matrix factorization of DCE-MRI for prostate cancer classification

Aijie Hou; Yahui Peng; Xinchun Li

doi:10.1117/12.2604770

6 August 2021 Nonnegative matrix factorization of DCE-MRI for prostate cancer classification

Aijie Hou, Yahui Peng, Xinchun Li

Proceedings Volume 11913, Sixth International Workshop on Pattern Recognition; 1191305 (2021) https://doi.org/10.1117/12.2604770
Event: Sixth International Workshop on Pattern Recognition, 2021, Chengdu, China

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.

Citation Download Citation

Aijie Hou, Yahui Peng, and Xinchun Li "Nonnegative matrix factorization of DCE-MRI for prostate cancer classification", Proc. SPIE 11913, Sixth International Workshop on Pattern Recognition, 1191305 (6 August 2021); https://doi.org/10.1117/12.2604770

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