Imaging Diagnosis of Various Hepatocellular Carcinoma Subtypes and Its Hypervascular Mimics: Differential Diagnosis Based on Conventional Interpretation and Artificial Intelligence.

Background: Hepatocellular carcinoma (HCC) is unique among malignancies, and its characteristics on contrast imaging modalities allow for a highly accurate diagnosis. The radiological differentiation of focal liver lesions is playing an increasingly important role, and the Liver Imaging Reporting and Data System adopts a combination of major features including arterial phase hyper-enhancement (APHE) and the washout pattern.

Summary: Specific HCCs such as well or poorly differentiated type, subtypes including fibrolamellar or sarcomatoid and combined hepatocellular-cholangiocarcinoma do not often demonstrate APHE and washout appearance. Meanwhile, hypervascular liver metastases and hypervascular intrahepatic cholangiocarcinoma can demonstrate APHE and washout. There are still other hypervascular malignant liver tumors (i.e., angiosarcoma, epithelioid hemangioendothelioma) and hypervascular benign liver lesions (i.e., adenoma, focal nodular hyperplasia, angiomyolipoma, flash filling hemangioma, reactive lymphoid hyperplasia, inflammatory lesion, arterioportal shunt), which need to be distinguished from HCC. When a patient has chronic liver disease, differential diagnosis of hypervascular liver lesions can be even more complicated. Meanwhile, artificial intelligence (AI) in medicine has been widely explored, and recent advancement in the field of deep learning has provided promising performance for the analysis of medical images, especially radiological imaging data contain diagnostic, prognostic, and predictive information which AI can extract. The AI research studies have demonstrated high accuracy (over 90% accuracy) for classifying lesions with typical imaging features from some hepatic lesions. The AI system has a potential to be implemented in clinical routine as decision support tools. However, for the differential diagnosis of many types of hypervascular liver lesions, further large-scale clinical validation is still required.

Key messages: Clinicians should be aware of the histopathological features, imaging characteristics, and differential diagnoses of hypervascular liver lesions to a precise diagnosis and more valuable treatment plan. We need to be familiar with such atypical cases to prevent a diagnostic delay, but AI-based tools also need to learn a large number of typical and atypical cases.