摘要
Hepatocellular carcinoma(HCC)is the most common form of primary liver cancer with low 5-year survival rate.The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC.However,current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers.The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semiquantitative analysis by radiologists.Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms.Using the radiomics method could quantify tumoral phenotypes and heterogeneity,which may provide benefits in clinical decision-making at a lower cost.Here,we review the workflow and application of radiomics in HCC.
