Zou Zhi-Min, Chang De-Hua, Liu Hui, Xiao Yu-Dong
Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, 410011, China.
Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120, Heidelberg, Germany.
Insights Imaging. 2021 Mar 6;12(1):31. doi: 10.1186/s13244-021-00977-9.
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.
随着机器学习（ML）算法的发展，已经建立了越来越多的预测模型，用于预测肝细胞癌（HCC）患者在接受各种治疗方式后的治疗结果。通过使用临床和放射学变量的不同组合，ML算法可以模拟人类学习以检测数据中的隐藏模式，并在人工智能技术中发挥关键作用。与传统统计方法相比，ML方法具有更强的预测效果。ML算法广泛应用于模型建立的几乎所有步骤，如图像特征提取、预测因子分类和模型开发。因此，本综述介绍了与ML算法相关的文献，旨在总结ML的优势和局限性，以及其在HCC各种治疗方式后的预后预测中的潜在价值。
