Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, 550000, Guizhou, Guiyang, China.
Cancer Imaging. 2024 Mar 14;24(1):36. doi: 10.1186/s40644-024-00682-y.
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.
神经胶质瘤表现出特定的遗传亚型，导致不同的临床病程，需要涉及神经科医生、癫痫专家、神经肿瘤学家和神经外科医生组成的多学科团队。目前，神经胶质瘤的诊断主要围绕初步的影像学发现和随后的明确手术诊断（通过手术取样）展开。放射组学和放射基因组学通过从 MRI 数据以及基因组数据中提取的形态、纹理和功能特征，具有精确诊断和预测生存及治疗反应的潜力。尽管具有这些优势，但不同研究小组之间仍然缺乏标准化的特征提取和分析方法流程，这使得外部验证变得不可行。放射组学和放射基因组学可用于更好地了解神经胶质瘤的基因组基础，例如肿瘤空间异质性、治疗反应、分子分类和肿瘤微环境免疫浸润。这些新技术还被用于预测神经胶质瘤的组织学特征、分级，甚至总生存情况。在这篇综述中，阐述了放射组学和放射基因组学的工作流程，并介绍了在神经胶质瘤中应用机器学习或人工智能的最新研究。
