Phosphorus magnetic resonance spectroscopy data analysis of the hepatocellular carcinoma using artificial neural networks

  • Authors:

  • Lijuan Wang;Yihui Liu;Jinyong Cheng;Qiang Liu;Baopeng Li

  • Affiliations:

  • Institute of Intelligence Information Processing, School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China;Institute of Intelligence Information Processing, School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China;Institute of Intelligence Information Processing, School of Information Science and Technology, Shandong Institute of Light Industry, Jinan, China;Department of Magnetic Resonance Imaging, Shandong Medical Imaging Research Institute, Jinan, China;Department of Magnetic Resonance Imaging, Shandong Medical Imaging Research Institute, Jinan, China

  • Venue:
  • IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
  • Year:
  • 2009

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Abstract

Through the evaluation of the 31Phosphorus Magnetic Resonance Spectroscopy (31P-MRS), we can distinguish three types of diagnosis: hepatocellular carcinoma, normal and cirrhosis. 71 samples of 31P-MRS data are selected including hepatocellular carcinoma, normal and cirrhosis tissue. Back-propagation neural network (BP) and Radial Basis Function Neural Network (RBF) are applied to analyze 31P-MRS data, develop neural network models of 31P-MRS for the diagnostic classification of hepatocellular carcinoma. The results suggest that BP models have better performance than RBF models. Neural network models based on 31P-MRS data offer an alternative and promising technique for diagnostic prediction of hepatocellular carcinoma in vivo.