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

Abstract

AbstractBackground and AimThe 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).MethodsIn 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.ResultsHCC‐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.ConclusionsHCC‐Scope can accurately differentially diagnose between non‐HCC NASH and NASH‐HCC, including very early‐stage NASH‐HCC.