[关键词]
[摘要]
影像组学(radiomics)技术通过从眼科影像中提取高通量定量特征，挖掘人眼难以辨识的亚视觉信息，为视网膜疾病的精确诊疗提供了新思路。该技术能够量化视网膜在结构、纹理及血流特征等方面的细微差异，并与临床信息相结合，在视网膜疾病的早期筛查、疾病分层、治疗反应预测及个体化风险评估中展现出显著潜力，尤其有助于糖尿病视网膜病变、年龄相关性黄斑变性等常见视网膜疾病的个体化管理。然而，现阶段影像组学的临床转化仍面临一定挑战，包括部分研究模型效能及泛化能力不足，以及影像组学特征与疾病病理机制之间的临床可解释性有限，从而制约其在实际诊疗流程中的推广应用。文章基于近 5 a发表的相关研究，对组学结合机器学习在视网膜疾病诊断与预后评估中的应用进展进行了系统综述，并对其当前局限性及未来发展方向进行了总结与展望。
[Key word]
[Abstract]
Radiomics enables the extraction of high-throughput quantitative features from ophthalmic images, allowing the identification of subvisual information that is imperceptible to the human eye and offering a novel strategy for the precise diagnosis and treatment of retinal diseases. By quantitatively characterizing subtle differences in retinal structure, texture, and hemodynamic characteristics, and integrating these features with clinical data, radiomics has demonstrated substantial potential in early screening, disease stratification, prediction of treatment responses, and individualized risk assessment of retinal diseases, particularly in common conditions such as diabetic retinopathy and age-related macular degeneration. Despite these promising advances, the clinical translation of radiomics remains challenging. Current limitations include suboptimal model performance and generalizability,as well as insufficient clinical interpretability of radiomic feature and predictive models, which hampers their integration into existing imaging systems and routine clinical workflows. Based on a systematic analysis of relevant articles published over the past five years, this paper summarizes recent progress in the application of radiomics combined with machine learning for the diagnosis and prognostic assessment of retinal diseases, and to discuss the key challenges and future directions for its clinical implementation.
[中图分类号]
[基金项目]
湖南省自然科学基金资助项目(No.2023JJ70038)
