进展期胃癌的影像学评估：从图像到大数据影像组学  

Radiological evaluation of advanced gastric cancer： from image to big data radiomics

作　　者：唐磊[1] Tang Lei(Department of Radiology,Key Laboratory of Carcinogenesis and Translational Research（Ministry of Education）,Peking University Cancer Hospital ＆ Institute,Beijing 100142,China)

机构地区：[1]北京大学肿瘤医院医学影像科、北京市肿瘤防治研究所、恶性肿瘤发病机制及转化研究教育部重点实验室,100142

出　　处：《中华胃肠外科杂志》2018年第10期1106-1112,共7页Chinese Journal of Gastrointestinal Surgery

基　　金：国家自然科学基金（81371715）;首都临床特色应用研究与成果推广（Z161100000516060）;北京市医管局青苗计划（QML20161102）

摘　　要：进展期胃癌个体化诊疗对影像学精准评价的需求不断增高，胃癌影像学若要突破现有机器分辨率及主观诊断缺陷的瓶颈，进一步提升分期和评效能力，亟需客观有力的辅助手段。影像组学在现有图像分辨率的基础上，通过纹理分析及大数据手段挖掘海量图像信息，利用人工智能深度学习等手段筛选、整合图像及临床特征，建模进行客观、量化评估，理论上有望进一步提高胃癌分期及评效水平。本文围绕胃癌影像及影像组学两个核心内容，从5个方面层递展开：（1）CT作为影像学分期及疗效评价的首选方法，其应用受到影像医生对图像特征挖掘及信息统合能力的限制，需要引入图像处理能力更强的手段。（2）影像组学纹理分析能够挖掘肉眼无法辨识的海量图像信息，较影像医生主观视觉分析更详细、且可定量评估病变特征，从而发掘微观潜在的医学影像信息；近两年在肿瘤的应用研究进展迅速，几乎涵盖全身各部位实体肿瘤，利用熵、偏度、异质性等纹理分析指标解决肿瘤临床治疗关注的各个方面。（3）从诊断、生物学行为及预后评估，分期及疗效预测与评价3个方面概要总结影像组学在胃癌影像学的研究进展，现有研究基本肯定了影像组学及纹理分析在区分胃癌不同类型、分期和疗效的较高效能，有潜力作为医生主观评估的补充。（4）总结影像组学技术本身缺陷及目前胃癌应用研究中存在的问题，避免盲目及陷阱。（5）展望人工智能作为医生助手的应用前景，影像医生不必担忧被取代，而应积极联合多学科、多中心同道开展临床研究，推动胃癌大数据影像组学的发展和落地。Following the increased demand of personalized medicine to precise radiology in advanced gastric cancer, there is particular need for objective and powerful surrogate to help the gastro-radiology to break through the bottleneck of imaging resolution and the defect of subjective diagnosis, which can further improve the efficacy of staging and response evaluation. On the basis of the existing imaging resolution, the radiomics can perform massive data mining through texture analysis and big data, using artificial intelligence deep learning and other algorithms to screen and integrate images and clinical features for modeling and diagnosis, which may further improve the efficacy of staging and response evaluation theoretically. In this paper, we focused on gastro-radiology and radiomics, and reviewed five dimensions progressively： （1） As the first choice for staging and response evaluation, CT application is limited by radiologists′ ability to excavate image features and information integration, which needs more powerful image processing method. （2） Radiomics texture analysis can provide massive objective image information that can not be identified by the radiologists′ naked eye. It is more detailed and provides quantitative evaluation of the characteristics of tumors better than the radiologists′ subjective vision analysis, which can dig potential microscopic information. In the recent two years, the research on the application has been progressing rapidly, covering almost all the solid tumors, and solving the various clinical focuses using entropy, skewness, heterogeneity and other texture analysis indicators. （3） The research progress of radiomics in gastric cancer from the following three directions was summarized： differential diagnosis and biological behavior analysis, staging, and response prediction and evaluation. The current research confirmed the high efficiency of radiomics and texture analysis in differentiating different types, stages and responders of gastric cancer, which can ac

关 键 词：胃肿瘤 进展期 影像学 影像组学 

分 类 号：R735.2[医药卫生—肿瘤]