中国医疗设备2025，Vol.40Issue(4)：170-176,7.DOI:10.3969/j.issn.1674-1633.20240858
影像组学和深度学习在预测肝细胞癌微血管侵犯中的研究进展
Advances in Radiomics and Deep Learning in Predicting Microvascular Invasion in Hepatocellular Carcinoma
何变红 1武志峰2
作者信息
- 1. 山西医科大学 医学影像学院,山西 太原 030001
- 2. 山西白求恩医院/山西医学科学院 放射科,山西 太原 030032
- 折叠
摘要
Abstract
Hepatocellular carcinoma(HCC)is a common malignant tumor that poses a huge threat to human health.Microvascular invasion(MVI)is an important cause of postoperative recurrence and metastasis in HCC patients.Currently,the diagnosis of MVI is mainly confirmed through postoperative pathological examination,which,however,is an invasive method.Non-invasive prediction of MVI before surgery is beneficial for guiding individualized treatment and improving prognosis.In recent years,researchers have achieved remarkable results in predicting MVI in HCC using radiomics and Deep learning(DL)methods.The application of these methods has significantly improved the accuracy of predicting MVI in HCC,providing more precise guidance for patients'treatment.This article elaborated on the research achievements in predicting MVI in HCC based on radiomics and DL in recent years,with the expectation of providing a reference for the clinical realization of precise non-invasive prediction methods,facilitating the formulation of individualized treatment plans,improving patients'prognosis,and promoting the development of diagnostic and treatment technologies for liver cancer.关键词
肝细胞癌/微血管侵犯/计算机断层成像/磁共振成像/影像组学/深度学习Key words
hepatocellular carcinoma/microvascular invasion/computed tomography/magnetic resonance imaging/radiomics/deep learning分类
医药卫生引用本文复制引用
何变红,武志峰..影像组学和深度学习在预测肝细胞癌微血管侵犯中的研究进展[J].中国医疗设备,2025,40(4):170-176,7.
