Research advances of radiomics in auxiliary diagnosis of hepatocellular carcinoma
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摘要: 肝细胞癌是常见的恶性肿瘤之一,其恶性程度高,预后性差,具有高发病率和高死亡率的特点。影像组学提供了一种量化分析方法,可将医学影像中的组织病理学、肿瘤生物学等信息转化为高维量化特征信息,并结合人工智能算法进行数据挖掘与统计分析,协助临床进行肿瘤的早期诊疗。本文介绍了影像组学在肝细胞癌鉴别诊断、病理分级、微血管侵犯与免疫组化标志物预测中的研究进展,讨论了在数据量、模型可靠性等方面存在的不足,并指出未来可向多中心、多任务等方向发展,以期为肝细胞癌的辅助诊断提供参考。Abstract: Hepatocellular carcinoma is one of the common malignant tumours. It has a high degree of malignancy and poor prognosis and is characterized by high morbidity and mortality. Radiomics provides a quantitative analysis method. It transforms histopathology and tumour biology information in medical images into high-dimensional quantitative feature information and combines with artificial intelligence algorithms for data mining and statistical analysis to assist in the early clinical diagnosis and treatment of tumours. This paper reviews the research progress of radiomics in differential diagnosis, pathological grading, microvascular invasion, and immunohistochemical marker prediction of hepatocellular carcinoma. Meanwhile, it discusses the deficiencies in data volume and model reliability. It points out that it can be developed towards multicentre and multitasking to provide a reference for the auxiliary diagnosis of hepatocellular carcinoma.
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