Abstract
Radiomics was first introduced by Lambin et al. in 2012, and since then, research in this field has grown rapidly. Researchers have shown great interest in developing efficient methods for automatically extracting a large number of quantitative features from medical images, aiming to enhance diagnostic accuracy and predictive capability. Although there has been a rise in Radiomics studies focusing on intrahepatic cholangiocarcinoma (ICC) in recent years, comprehensive reviews are still relatively scarce. This study explores how Radiomics technology can be utilized in modeling analyses to predict lymph node metastasis, microvascular invasion, and early recurrence of ICC, as well as the application of deep learning in these analyses. This paper provides a brief overview of the current state of Radiomics research and offers references for future studies.
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