Radiomics has shown remarkable potential in predicting cancer prognosis by noninvasive and quantitative analysis of tumors through medical imaging. This review summarizes recent advances in the use of radiomics across various cancer types and imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and interventional radiology. Innovative sampling methods, including deep learning-based segmentation, multiregional analysis, and adaptive region of interest (ROI) methods, have contributed to improved model performance. The review examines various feature selection approaches, including least absolute shrinkage and selection operator (LASSO), minimum redundancy maximum relevance (mRMR), and ensemble methods, highlighting their roles in enhancing model robustness. The integration of radiomics with multi-omics data has further boosted predictive accuracy and enriched biological interpretability. Despite these advancements, challenges remain in terms of reproducibility, workflow standardization, clinical validation and acceptance. Future research should prioritize multicenter collaborations, methodological coordination, and clinical translation to fully unlock the prognostic potential of radiomics in oncology.
影像组学通过医学影像对肿瘤进行无创定量分析，在预测癌症预后方面展现出显著潜力。本综述系统总结了影像组学在不同癌症类型及多种影像模态中的应用进展，涵盖计算机断层扫描（CT）、磁共振成像（MRI）、超声、正电子发射断层扫描（PET）及介入放射学等领域。基于深度学习的分割技术、多区域分析及自适应感兴趣区域（ROI）等创新采样方法有效提升了模型性能。文章系统分析了包括最小绝对收缩与选择算子（LASSO）、最小冗余最大相关（mRMR）及集成方法在内的多种特征选择策略，阐明了其在增强模型鲁棒性方面的作用。影像组学与多组学数据的融合进一步提高了预测准确性，并增强了生物学可解释性。尽管取得这些进展，该领域在可重复性、工作流程标准化、临床验证与接受度方面仍面临挑战。未来研究应重点关注多中心协作、方法学协调及临床转化，以充分释放影像组学在肿瘤预后评估中的潜力。
肿瘤(癌症)患者之家