基于CT和MRI影像组学的机器学习模型预测肝癌早期复发的研究进展

Research progress of machine learning model based on CT and MRI radiomics for predicting early recurrence of hepatocellular carcinoma

肝细胞癌(hepatocellular carcinoma,HCC)早期复发患者往往较晚期复发患者有更差的预后,且该疾病的早期症状大多是非特异性的。随着人工智能(artificial intelligence,AI)日益发展,影像组学与其核心分支机器学习结合,打破了人眼识别的局限性,深度挖掘医学影像图像中隐藏着的纹理、形态等反映细胞一定生物学特征的信息,处理并筛选高维特征进行定量数据分析,构建HCC早期复发预测模型,可以使更多的患者尽早受益于临床诊疗,从而提高生存率,在疾病诊断及预后方面显示出巨大潜力。本文对比分析文献中基于CT及MRI预测HCC早期复发的影像组学模型,并综述其研究进展。

Patients with early recurrence of hepatocellular carcinoma(HCC)tend to have a worse prognosis than those with late recurrence,and most of the early symptoms of the disease are non-specific.Machine learning(ML)is the core branch of artificial intelligence(AI),with the increasing development of AI,radiomics combined with ML breaks the limitations of human eye recognition,deeply explores the hidden information of texture and morphology in medical images that reflect certain biological characteristics of cells,processes and screens high-dimensional features for quantitative data analysis.Building HCC early recurrence prediction models can benefit more patients from clinical treatment as early as possible and thus improve survival rates.In this article,we compare and analyze the CT and MRI based radiomics models in the literature for predicting early recurrence of HCC and review their research progress.