%0 Journal Article
%T 影像组学在临床预后预测中的价值与挑战：技术发展、临床应用及未来展望
The Value and Challenges of Radiomics in Clinical Prognostic Prediction: Technological Advancements, Clinical Applications, and Future Perspectives
%A 刘宇航
%A 汪建军
%J Advances in Clinical Medicine
%P 1617-1626
%@ 2161-8720
%D 2025
%I Hans Publishing
%R 10.12677/acm.2025.1561896
%X 在多种疾病领域，例如脑胶质瘤、肺癌、乳腺癌和消化系统肿瘤，影像组学已被成功应用于预后评估与治疗效果预测。近年来，深度学习算法的引入显著提升了影像组学特征提取与模型构建的能力，且多模态数据融合加速了复杂疾病的多维度解析。然而，发展中仍面临若干挑战，包括数据标准化不足、模型可解释性缺失以及多中心验证的局限性等。未来研究需加强学科交叉合作，优化数据共享平台及标准化路径，并开发新型深度学习架构以进一步提升模型效能和临床适用性。此外，影像组学与基因组学等多组学技术的结合，及在大规模临床试验中的应用将成为研究重点。本综述旨在总结影像组学在预后预测中的现状与进展，探讨当前技术瓶颈及展望未来发展方向，以推动影像组学应用的进一步深化。
Radiomics has been successfully applied to prognostic evaluation and treatment efficacy prediction in various disease domains, including glioma, lung cancer, breast cancer, and gastrointestinal tumors. In recent years, the introduction of deep learning algorithms has significantly enhanced the capabilities of radiomic feature extraction and model construction, while multimodal data fusion has accelerated multidimensional analysis of complex diseases. However, several challenges persist in its development, such as insufficient data standardization, lack of model interpretability, and limitations in multicenter validation. Future research should strengthen interdisciplinary collaboration, optimize data-sharing platforms and standardization protocols, and develop novel deep learning architectures to further improve model performance and clinical applicability. Additionally, the integration of radiomics with multi-omics technologies (e.g., genomics) and its application in large-scale clinical trials will become key research priorities. This review aims to summarize the current status and advancements of radiomics in prognostic prediction, discuss existing technical bottlenecks, and outline future directions to promote the deeper integration of radiomics into clinical practice.
%K 影像组学，
%K 临床预后预测，
%K 精准医疗，
%K 深度学习，
%K 多模态数据
Radiomics
%K Clinical Prognostic Prediction
%K Precision Medicine
%K Deep Learning
%K Multimodal Data
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=118059