%0 Journal Article
%T 基于深度学习的影像组学在预测结直肠癌基因状态中的应用进展<br>Application Advances in Deep Learning-Based Imaging Radiomics for Predicting Colorectal Cancer Gene Status
%A 李鹏飞
%A 马晓明
%J World Journal of Cancer Research
%P 27-34
%@ 2164-9057
%D 2024
%I Hans Publishing
%R 10.12677/WJCR.2024.141005
%X 结直肠癌(CRC)在中国的发病率和死亡率持续上升，多数病人在确诊时已属于中晚期。个性化的治疗策略和预后分析在很大程度上依赖于对CRC患者遗传特征的了解。近年来，影像组学已经成为一种有价值的工具，它通过图形分析和特征提取在肿瘤基因表达和影像表型之间建立了联系。随着机器深度学习的介入，这种非侵入性的技术可在术前预测肿瘤相关的基因型。目前，越来越多的研究致力于研究图像特征和CRC基因型之间的关系，从而为CRC基因型的诊断和预测提供更高的准确性。本综述旨在总结基于CRC的影像技术在预测结直肠癌遗传状态方面的临床应用、进展和目前的局限性。最终目的是加强临床医师对CRC成像技术的理解，提高CRC的诊断、预后和治疗方面的潜力。<br />
The incidence and mortality of colorectal cancer (CRC) continue to rise in China, and most patients are in the middle to advanced stages at the time of diagnosis. Personalized treatment strategies and prognostic analyses rely heavily on the knowledge of genetic characteristics of CRC patients. In re-cent years, imaging genomics has emerged as a valuable tool that establishes a link between tumor gene expression and imaging phenotype through graphical analysis and feature extraction. With the intervention of deep machine learning, this non-invasive technique can predict tumor-associated genotypes preoperatively. Currently, more and more studies are devoted to investigating the relationship between image features and CRC genotypes, thus providing higher accuracy in diagnosis and prediction of CRC genotypes. The aim of this review is to summarize the clinical applications, advances, and current limitations of CRC-based imaging techniques in predicting the genetic status of colorectal cancer. The ultimate goal is to enhance clinicians’ understanding of CRC imaging techniques and to improve the potential of CRC in terms of diagnosis, prognosis, and treatment.
%K 结直肠癌，影像组学，深度学习，基因状态，微卫星不稳定性<br>Colorectal Cancer
%K Imaging Radiomics
%K Deep Learning
%K Gene Status
%K Microsatellite Instability
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=79488