Artificial intelligence/neural network system that accurately diagnoses hepatocellular carcinoma in nonalcoholic steatohepatitis.

Abstract

The aim of this study was to develop a novel noninvasive test using an artificial intelligence/neural network system (called HCC-Scope) to diagnose early-stage hepatocellular carcinoma (HCC) on the background of nonalcoholic steatohepatitis (NASH). In total, 175 patients with histologically proven nonalcoholic fatty liver disease and 55 patients with NASH-HCC were enrolled for training and validation studies. Of the 55 patients with NASH-HCC, 27 (49.1%) had very early-stage HCC, and six (10.9%) had early-stage HCC based on the Barcelona Clinic Liver Cancer staging system. Diagnosis with HCC-Scope was performed based on 12 items: age, sex, height, weight, AST level, ALT level, gamma-glutamyl transferase level, cholesterol level, triglyceride level, platelet count, diabetes status, and IgM-free apoptosis inhibitor of macrophage level. The FMVWG2U47 hardware (Fujitsu Co. Ltd, Tokyo, Japan) and the originally developed software were used. HCC-Scope had sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100% for the differential diagnosis between non-HCC and HCC in a training study with gray zone analysis. It was also excellent in the validation study (95.0% sensitivity, 100% specificity, 100% PPV, and 97.1% NPV with gray zone analysis and 95.2% sensitivity, 100% specificity, 100% PPV, and 97.1% NPV without gray zone analysis). HCC-Scope had a significantly higher sensitivity (85.3%) and specificity (85.1%) than alpha-fetoprotein (AFP) level, AFP-L3 level, des-gamma-carboxy prothrombin (DCP) level, and the gender-age-AFP-L3-AFP-DCP (GALAD) score. HCC-Scope can accurately differentially diagnose between non-HCC NASH and NASH-HCC, including very early-stage NASH-HCC.