摘要
胶质瘤是最常见的颅内原发性肿瘤,影像学检查对于胶质瘤的诊断具有非常重要的临床意义,例如MRI、CT和PET等。影像组学是人工智能与医学影像大数据结合的新技术,它从海量数据中高效挖掘并整合大量高级影像特征,并建立预测模型。针对影像组学的临床应用,本文分别从胶质瘤分级、预测基因表达、鉴别诊断、预后评估四个方面,阐述影像组学在胶质瘤诊断中的应用。
Glioma is the most common primary intracranial tumor.Imaging examination has important clinical significance for the diagnosis of glioma,such as magnetic resonance imaging(MRI),computed tomography(CT),and positron emission tomography(PET).Imageomics is a new technology combining artificial intelligence and medical imaging big data.It efficiently mines and integrates a large number of advanced imaging features from massive data,and establishes predictive models.For the clinical application of imageomics,this article expounds the application of imageomics in glioma diagnosis from four aspects:glioma grading,prediction of gene expression,differential diagnosis,and prognostic evaluation.
作者
帅明
王娟
张伟
Shuai Ming;Wang Juan;Zhang Wei(Clinical Medical College of Hunan University of Traditional Chinese Medicine,Changsha 410208,China;Department of Radiology,Hunan Provincial Brain Hospital,Changsha 410021,China)
出处
《中国医师杂志》
CAS
2023年第11期1758-1760,F0003,共4页
Journal of Chinese Physician
