Feng Na, Wang Kun, Jiao Yan
Department of Sports Medicine, Orthopedics' Clinic, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China.
Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China.
World J Gastrointest Oncol. 2025 Jul 15;17(7):106610. doi: 10.4251/wjgo.v17.i7.106610.
Hepatocellular carcinoma (HCC) is a prevalent and aggressive liver cancer that poses significant challenges in diagnosis and prognosis. Recent advancements in radiomics and machine learning (ML) offer promising solutions to enhance the accuracy of HCC diagnosis, treatment response prediction, and survival prognosis. Radiomics, which extracts quantitative features from medical images, captures the complex tumor heterogeneity that is often undetectable with traditional imaging methods. When combined with ML algorithms, these features can be used to differentiate between various stages of HCC, predict treatment outcomes, and assess long-term survival. This review explores key radiomic features, including texture, shape, and intensity, and their integration with ML techniques like binary classification models, XGBoost, LightGBM, and deep learning architectures. We also discuss the challenges faced in model interpretation, data heterogeneity, and the integration of multi-modal data. Despite the promising potential of these technologies, the clinical adoption of radiomics and ML models in HCC management will require overcoming these obstacles through standardization and improved interpretability.
肝细胞癌（HCC）是一种常见且侵袭性强的肝癌，在诊断和预后方面带来了重大挑战。放射组学和机器学习（ML）的最新进展为提高HCC诊断的准确性、治疗反应预测和生存预后提供了有前景的解决方案。放射组学从医学图像中提取定量特征，捕捉传统成像方法通常无法检测到的复杂肿瘤异质性。当与ML算法结合时，这些特征可用于区分HCC的不同阶段、预测治疗结果和评估长期生存。本综述探讨了关键的放射组学特征，包括纹理、形状和强度，以及它们与二元分类模型、XGBoost、LightGBM等ML技术和深度学习架构的整合。我们还讨论了模型解释、数据异质性以及多模态数据整合方面面临的挑战。尽管这些技术具有广阔的潜力，但放射组学和ML模型在HCC管理中的临床应用需要通过标准化和提高可解释性来克服这些障碍。
