深度学习和影像组学在脑胶质瘤诊疗中的研究进展

Deep learning and radiomics in diagnosis and treatment of glioma:a review

深度学习能从图像数据中自动学习代表性的影像特征应用于图像分析,如脑胶质瘤分期/分级、分子标志物状态预测、肿瘤假性进展与真实进展鉴别和生存预测等。影像组学旨在从常规医学图像中提取影像学特征来定量描述肿瘤,捕捉组织和病变微小的变化,如肿瘤体积内的异质性、肿瘤形状以及其在连续成像中随时间的变化。基于影像组学和深度学习的图像分析技术可以实现脑胶质瘤诊疗步骤的简化和自动化,具有较高的准确性。本研究对深度学习技术及影像组学方法进行概述,对近几年深度学习技术及影像组学方法在脑胶质瘤诊疗中的应用进行综述,以期为脑胶质瘤患者临床治疗方案选取提供术前依据。

Deep learning can automatically learn representative features from image data for clinical analysis,such as glioma staging/grading,prediction of molecular marker status,differentiation of tumor pseudoprogression from true progression,and survival prediction.Radiomics aims to quantitatively describe tumors based on imaging features extracted from routine medical images,and it can capture small changes in tissues and lesions,such as heterogeneity within tumor volume,tumor shape,and their changes over time during serial imaging.Image analysis technology based on radiomics and deep learning can simplify and automate the diagnosis and treatment of glioma,with high accuracy.The review gives a brief introduction of radiomics methods and deep learning technologies,and then summarizes the application of radiomics methods and deep learning technologies in the diagnosis and treatment of glioma in recent years,expecting to provide a preoperative basis for the treatment scheme selection for glioma patients.