各种肝细胞癌亚型及其高血管模拟的影像学诊断:基于传统解释和人工智能的鉴别诊断。

IF 11.6 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Liver Cancer Pub Date : 2023-06-01 DOI:10.1159/000528538
Yasunori Minami, Naoshi Nishida, Masatoshi Kudo
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引用次数: 2

摘要

背景:肝细胞癌(HCC)在恶性肿瘤中是独特的,其在对比成像模式上的特点允许高度准确的诊断。肝局灶性病变的影像学鉴别越来越重要,肝脏影像学报告和数据系统采用动脉期超增强(APHE)和洗脱模式等主要特征相结合。摘要:特异性hcc,如高分化型或低分化型,亚型包括纤维板层或肉瘤样和合并肝细胞-胆管癌,通常不表现出APHE和冲洗样表现。同时,高血管性肝转移和高血管性肝内胆管癌可表现为APHE和冲洗。还有其他高血管恶性肝肿瘤(如血管肉瘤、上皮样血管内皮瘤)和肝脏高血管良性病变(如腺瘤、局灶性结节增生、血管平滑肌脂肪瘤、闪充性血管瘤、反应性淋巴样增生、炎性病变、动脉门静脉分流),需要与HCC区分。当患者患有慢性肝病时,对高血管性肝脏病变的鉴别诊断可能更加复杂。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的分析提供了很好的表现,特别是放射成像数据中包含AI可以提取的诊断、预后和预测信息。人工智能研究表明,对某些肝脏病变具有典型影像学特征的病变进行分类,准确率高达90%以上。人工智能系统有可能作为决策支持工具在临床常规中实施。然而,对于许多类型的肝高血管病变的鉴别诊断,仍需要进一步的大规模临床验证。关键信息:临床医生应了解肝脏高血管病变的组织病理特征、影像学特征和鉴别诊断,以便准确诊断和制定更有价值的治疗方案。我们需要熟悉这些非典型病例,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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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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.

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来源期刊
Liver Cancer
Liver Cancer Medicine-Oncology
CiteScore
20.80
自引率
7.20%
发文量
53
审稿时长
16 weeks
期刊介绍: Liver Cancer is a journal that serves the international community of researchers and clinicians by providing a platform for research results related to the causes, mechanisms, and therapy of liver cancer. It focuses on molecular carcinogenesis, prevention, surveillance, diagnosis, and treatment, including molecular targeted therapy. The journal publishes clinical and translational research in the field of liver cancer in both humans and experimental models. It publishes original and review articles and has an Impact Factor of 13.8. The journal is indexed and abstracted in various platforms including PubMed, PubMed Central, Web of Science, Science Citation Index, Science Citation Index Expanded, Google Scholar, DOAJ, Chemical Abstracts Service, Scopus, Embase, Pathway Studio, and WorldCat.
期刊最新文献
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