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 (LI-RADS) adopts a combination of major features including arterial phase hyperenhancement (APHE) and washout pattern. Summary. Specific HCCs such as well or poorly differentiated type, subtypes including fibrolamellar or sarcomatoid and combined hepatocellular-cholangiocarcinoma does not often demonstrate APHE and washout appearance. Meanwhile, hypervascular liver metastases and hypervascular intrahepatic cholangiocarcinoma (ICC) 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 researches have demonstrated high accuracy (over 90% accuracy) for classifying lesions with typical imaging features from some hepatic lesions. AI system has a potential to implement in clinical routine as decision support tools. However, for the differential diagnosis of many types of hypervascular liver lesions, further large-scale clinical validation still is 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 the 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 lean a large number of typical and atypical cases.


中文翻译：

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各种HCC亚型及其富血管模拟物的影像诊断：基于常规判读和人工智能的鉴别诊断

背景。肝细胞癌（HCC）在恶性肿瘤中是独一无二的，其对比成像模式的特征允许高度准确的诊断。肝脏局灶性病变的放射学鉴别正发挥着越来越重要的作用，肝脏成像报告和数据系统（LI-RADS）采用了动脉期过度增强（APHE）和冲洗模式等主要特征的组合。概括。特定的 HCC，例如分化良好或分化不良的类型、包括纤维板层或肉瘤样的亚型以及混合性肝细胞胆管癌，通常不会表现出 APHE 和冲洗外观。同时，富血管性肝转移瘤和富血管性肝内胆管癌（ICC）可表现出 APHE 和清除。还有其他富含血管的恶性肝肿瘤（即 血管肉瘤、上皮样血管内皮瘤）和富含血管的良性肝脏病变（即腺瘤、局灶性结节性增生、血管平滑肌脂肪瘤、闪光充盈性血管瘤、反应性淋巴样增生、炎性病变、动门静脉分流），需要与HCC相鉴别。当患者患有慢性肝病时，富血管性肝脏病变的鉴别诊断可能会更加复杂。与此同时，人工智能（AI）在医学领域得到了广泛的探索，深度学习领域的最新进展为医学图像分析提供了有前景的性能。特别是，放射成像数据包含人工智能可以提取的诊断、预后和预测信息。AI研究已经证明，对某些肝脏病变具有典型影像特征的病变进行分类具有很高的准确性（准确率超过90%）。人工智能系统有潜力作为决策支持工具在临床常规中实施。然而，对于多种类型的富血管性肝脏病变的鉴别诊断，仍需要进一步大规模的临床验证。关键信息。临床医生应了解富血管性肝脏病变的组织病理学特征、影像学特征及鉴别诊断，以便做出更准确的诊断和更有价值的治疗方案。我们需要熟悉此类非典型病例以防止诊断延误，但基于人工智能的工具也需要学习大量的典型和非典型病例。人工智能系统有潜力作为决策支持工具在临床常规中实施。然而，对于多种类型的富血管性肝脏病变的鉴别诊断，仍需要进一步大规模的临床验证。关键信息。临床医生应了解富血管性肝脏病变的组织病理学特征、影像学特征及鉴别诊断，以便做出更准确的诊断和更有价值的治疗方案。我们需要熟悉此类非典型病例以防止诊断延误，但基于人工智能的工具也需要学习大量的典型和非典型病例。人工智能系统有潜力作为决策支持工具在临床常规中实施。然而，对于多种类型的富血管性肝脏病变的鉴别诊断，仍需要进一步大规模的临床验证。关键信息。临床医生应了解富血管性肝脏病变的组织病理学特征、影像学特征及鉴别诊断，以便做出更准确的诊断和更有价值的治疗方案。我们需要熟悉此类非典型病例以防止诊断延误，但基于人工智能的工具也需要学习大量的典型和非典型病例。丰富血管性肝脏病变的影像学特征及鉴别诊断有助于精确诊断和制定更有价值的治疗方案。我们需要熟悉此类非典型病例以防止诊断延误，但基于人工智能的工具也需要学习大量的典型和非典型病例。丰富血管性肝脏病变的影像学特征及鉴别诊断有助于精确诊断和制定更有价值的治疗方案。我们需要熟悉此类非典型病例以防止诊断延误，但基于人工智能的工具也需要学习大量的典型和非典型病例。