Diagnosis of Hepatocellular Carcinoma using Proton Magnetic Resonance Spectroscopy Ex Vivo (2001)

Introduction One of the commonest internal malignancies worldwide today is Hepatocellular Carcinoma (HCC)1. HCC may form a large mass with or without smaller satellite nodules, or may be multi-nodular and scattered throughout the liver. Nodules greater than 15mm diameter are assumed to be HCC. Small nodules (less than 10mm diameter) may also contain HCC but definitive diagnosis is difficult as it is often not possible using histopathology to distinguish HCC from tumour look alikes such as regenerative nodules, and particularly in small transcaval or percutaneous biopsies. HCC is common in cirrhotic livers. Proton magnetic resonance spectroscopy (1H MRS) of tissue or fine needle biopsies, when combined with a statistical classification strategies (SCS), has been shown to accurately distinguish malignant from benign tissue from organs including thyroid2, breast2 and prostate3 and may therefore be able to assist or replace histological diagnosis of HCC. Methods Specimen collection and handling: Biopsies from apparently normal liver, cirrhotic liver and HCC were obtained for MRS and correlative histopathology from 43 patients undergoing hemihepatectomy or total hepatectomy. Specimens were placed in 0.3 ml PBS/D2O, frozen in liquid nitrogen and stored at -70°C for up to three months. MR spectroscopy: 1H MRS was carried out at 37°C on a Bruker Avance spectrometer equipped with a Magnex magnet operating at 360.1 MHz (1H) and a 5 mm 1H, 13C inverse detection probe. 1D 1H and 2D COSY spectra were acquired and processed as previously described2,3. Statistical Classification Strategies (SCS): Pair-wise classification was performed for HCC, cirrhotic and normal liver. Magnitude MR spectra were analyzed by agenetic-algorithm-based Optimal Region Selector to identify the three maximally discriminatory subregions using rank-ordered first derivatives of the spectra4. A Linear Discriminant Analysis based classifier was developed. The robustness of the classifier was tested by bootstrap-based crossvalidation (1000 repeats)5. Class assignment was called crisp if class probabilities were > 0.75. Histopathology: Specimens were bisected prior to MRS. Adjacent tissue and post MRS tissue were examined histologically. Results Liver biopsies (n=106) were examined by MRS, including 31 healthy liver, 48 cirrhotic liver and 27 HCC. Proton 1D MR spectra of healthy, cirrhotic liver and HCC are shown Figure 1. Resonance assignments were from COSY spectra (not shown). Spectra from all tissues contained signals from lipids, carbohydrates (glucose, glycogen), amino acids (lysine, glutamine/ glutamate) and cholinebased metabolites (PE, GPE, PC, GPC). Visual differences in MR spectra of HCC compared to the other diagnostic categories included a relative decrease in the amount of lipids and a relative increase in phosphocholine (PC), glycerophosphocholine (GPC) and glycero-phosphoethanolamine (GPE). There was no consistent visual difference between MR spectra (1D and COSY) of normal and cirrhotic liver. However, SCS on 1D MR spectra distinguished the three diagnostic categories with high accuracy (Table 1). Figure 1: Proton MR spectra from (A) hepatocellular carcinoma, (B) cirrhotic liver and (C) normal liver Discussion Histopathological distinction of HCC from tumour look alikes can be difficult. Clinically, the distinction between apparently healthy liver, HCC and cirrhotic liver is important as carcinomas are more common in cirrhotic liver. Visual inspection of the MR data alone distinguished HCC from cirrhotic and normal tissues. The distinction between normal and cirrhotic tissue required the use of SCS. Using SCS the three pathological entities were distinguished with a high degree of accuracy. The potential thus exists for accurate identification of HCC in small biopsies and thus improved patient management of malignant liver disease. References 1. Simonetti et al., Dig. Dis. Sci. 36, 962-972, 1991 2. Lean etal, J. Women's Imaging, 2,19-30,2000 3. McCredie et al., Proc. 7th ISMRM #1508, 1999. 4. Nikulin et al., NMR Biomed. 11, 209-217, 1998. 5. Efron & Tibshirani: An Introduction to the Bootstrap. Monograms of Stat. & Appl. Probability, Vol 57, 1993.