Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know?

Insights into Imaging

De-Hua Chang¹  Zhi-Min Zou²  Hui Liu²  Yu-Dong Xiao² 

[1] Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120, Heidelberg, Germany;Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, 410011, Changsha, China;

关键词: Hepatocellular carcinoma;    Machine learning;    Predictive;    Modality;   

DOI  :  10.1186/s13244-021-00977-9

来源: Springer

【 摘 要 】

With the development of machine learning (ML) algorithms, a growing number of predictive models have been established for predicting the therapeutic outcome of patients with hepatocellular carcinoma (HCC) after various treatment modalities. By using the different combinations of clinical and radiological variables, ML algorithms can simulate human learning to detect hidden patterns within the data and play a critical role in artificial intelligence techniques. Compared to traditional statistical methods, ML methods have greater predictive effects. ML algorithms are widely applied in nearly all steps of model establishment, such as imaging feature extraction, predictive factor classification, and model development. Therefore, this review presents the literature pertaining to ML algorithms and aims to summarize the strengths and limitations of ML, as well as its potential value in prognostic prediction, after various treatment modalities for HCC.

【 授权许可】

CC BY