摘要
[背景]经导管动脉化疗栓塞术(TACE)是治疗肝细胞癌的主要手段之一.患者的个体差异使得医生需要在规范化治疗的基础上重视个体化策略.高精度的TACE术后预后和疗效预测模型可以辅助医生制定肝细胞癌患者的临床治疗方案,但目前预测TACE转归的生物指标仍未达成共识.[进展]随着人工智能技术的进步,越来越多研究利用机器学习模型挖掘患者个体差异与TACE术后预后和疗效之间的关系,以达到辅助医疗决策、个性化诊疗的目的.本文总结了当前人工智能技术应用于TACE术后预后和疗效预测的研究进展,着重关注人工智能技术的应用范式.[展望]目前的深度学习算法无法完全利用所有医学特征,随着深度学习技术的继续发展,基于深度学习的自动分割技术将提供更准确的分割结果.更深的网络结构可帮助医生更好地预测患者的TACE预后,为医师提供更精确的决策支持.
[Background]Transcatheter arterial chemoembolization(TACE)stands as one of the main treatment methods for hepatocellular carcinoma(HCC).Individual differences of patients require doctors to prioritize individualized strategies based on standardized treatment.Although a high-precision postoperative TACE prediction model can assist doctors in formulating clinical treatment plans for HCC patients,there remains a lack of consensus on the biological indicators for predicting TACE outcomes.The alternative prognostic features of TACE include clinical,biochemical,and imaging omics features,which are high-dimensional and complicated and are mostly analyzed using artificial intelligence(AI)algorithms.[Progress]This paper reviews the application of AI in TACE prognosis prediction from four key perspectives:region of interest segmentation,feature extraction,feature selection,and model construction.Currently,region-of-interest segmentation mainly employs a UNet-like model,yet the segmentation accuracy varies widely across studies.The segmentation accuracy of the same model differs between various tasks and datasets from different centers.According to different feature acquisition approaches,prognostic prediction features can be divided into non-radiomics features and radiomics features.The former uses texture analysis techniques to extract image features such as shape features and texture features.The latter mainly uses convolutional neural networks(CNNs)to extract deep learning features from images and can directly output prediction results.The methods for filtering features can be divided into feature reduction and feature selection.Feature reduction compresses high-dimensional features into low-dimensional features.Although the key information in the features is preserved,this feature dimension reduction method is relatively less interpretable and thus less used.Feature selection includes filter,wrapper,and embedded methods.In current research,filtering and embedding methods are mainly used to filter features.The former ca
作者
王侃琦
毛景松
赵扬
刘刚
WANG Kanqi;MAO Jingsong;ZHAO Yang;LIU Gang(Institute of Artificial Intelligence,Xiamen University,Xiamen 361102,China;Vascular Intervention Department,Affiliated Hospital of Guilin Medical College,Guilin 541199,China;Shenzhen Research Institute,Xiamen University,Shenzhen 518000,China;School of Public Health,Xiamen University,Xiamen 361102,China)
出处
《厦门大学学报（自然科学版）》
CAS
CSCD
北大核心
2024年第1期13-23,共11页
Journal of Xiamen University：Natural Science
基金
国家重点研发计划(2023YFB3810003)
国家自然科学基金杰出青年基金(81925019)
广东省基础与应用基础研究基金(2021A1515012462)。
关键词
肝细胞癌
经导管动脉化疗栓塞术
预后预测
疗效预测
人工智能
hepatocellular carcinoma
transcatheter arterial chemoembolization
prognosis prediction
therapeutic effect prediction
artificial intelligence
