Abstract
Aim: Apply the statistical classification strategy (SCS) to magnetic resonance spectroscopy (MRS) data from liver biopsies and test its potential to discriminate between normal liver, cirrhotic nodules and nodules of hepatocellular carcinoma with a high degree of accuracy. Methods: Liver tissue specimens from 54 patients undergoing either partial (hemi) or total hepatectomy were analysed by one-dimensional proton MRS at 8.5 Tesla. Histologically, these specimens were confirmed as normal (n = 31), cirrhotic (n = 59), and hepatocellular carcinoma (HCC, n = 32). Diagnostic correlation was performed between the MR spectra and histopathology. An SCS was applied consisting of pre-processing MR magnitude spectra to identify spectral regions of maximal discriminatory value, and cross-validated linear discriminant analysis. Results: SCS applied to MRS data distinguished normal liver tissue from HCC with an accuracy of 100%. Normal liver tissue was distinguished from cirrhotic liver with an accuracy of 92% and cirrhotic liver was distinguished from HCC with an accuracy of 98%. Conclusions: SCS applied to proton MRS of liver biopsies provides a robust method to distinguish, with a high degree of accuracy, HCC from both cirrhotic and normal liver.