31P-MRS data analysis of liver based on back-propagation neural networks / 中国医学影像技术

Objective To explore the value of distinguishment of hepatocellular carcinoma (HCC), cirrhosis nodules and normal liver based on neural networks in the ~(31)P-MR spectroscopy. MethodsA total of 66 data of ~(31)P-MRS were analysed using back-propagation neural network, including 37 samples of liver cirrhosis, 13 samples of HCC and 16 samples of normal liver. ResultsThe cross-valiation experiments showed that diagnostic accuracy rate of HCC increased from 85.47% to 92.31% with neural network model based on the ~(31)P-MR spectroscopy data analysis. Conclusion ~(31) P-MRS data analysis based on neural network model provides a valuable diagnostic tool of HCC in vivo.