Deep Learning-aided ¹H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma

Abstract

Purpose: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (¹H-MRS) is a well-established tool for noninvasive metabolomics, but has been challenging in the liver allowing only a few metabolites to be detected other than lipids. This study aims to explore the potential of ¹H-MRS of the liver in conjunction with deep learning to differentiate between HBV-LC patients with and without HCC.

Methods: Between August 2018 and March 2021, ¹H-MRS data were collected from 37 HBV-LC patients who underwent MRI for HCC surveillance, without HCC (HBV-LC group, n = 20) and with HCC (HBV-LC-HCC group, n = 17). Based on a priori knowledge from the first 10 patients from each group, big spectral datasets were simulated to develop 2 kinds of convolutional neural networks (CNNs): CNNs quantifying 15 metabolites and 5 lipid resonances (qCNNs) and CNNs classifying patients into HBV-LC and HBV-LC-HCC (cCNNs). The performance of the cCNNs was assessed using the remaining patients in the 2 groups (10 HBV-LC and 7 HBV-LC-HCC patients).

Results: Using a simulated dataset, the quantitative errors with the qCNNs were significantly lower than those with a conventional nonlinear-least-squares-fitting method for all metabolites and lipids (P ≤0.004). The cCNNs exhibited sensitivity, specificity, and accuracy of 100% (7/7), 90% (9/10), and 94% (16/17), respectively, for identifying the HBV-LC-HCC group.

Conclusion: Deep-learning-aided ¹H-MRS with data augmentation by spectral simulation may have potential in differentiating between HBV-LC patients with and without HCC.