Balana Carmen, Castañer Sara, Carrato Cristina, Moran Teresa, Lopez-Paradís Assumpció, Domenech Marta, Hernandez Ainhoa, Puig Josep
Medical Oncology Service, Institut Català d'Oncologia Badalona (ICO), Badalona Applied Research Group in Oncology (B-ARGO Group), Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain.
Diagnostic Imaging Institute (IDI), Hospital Universitari Germans Trias I Pujol, Institut Investigació Germans Trias i Pujol (IGTP), Barcelona, Spain.
Front Neurol. 2022 May 26;13:865171. doi: 10.3389/fneur.2022.865171. eCollection 2022.
Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.
胶质瘤是中枢神经系统肿瘤的一个异质性群体,具有不同的预后和不同的治疗需求。胶质母细胞瘤是成人中最常见的亚型,预后很差且会导致致残后果。世界卫生组织(WHO)分类规定,胶质瘤的分型和分级应包括分子标志物。胶质瘤的分子特征对预后、治疗计划以及治疗反应的预测具有重要意义。目前,胶质瘤通过肿瘤切除或活检进行诊断,这些方法始终具有侵入性且风险较高。然而,近年来,通过分析体液中释放的产物来开发不同的肿瘤分子特征分析方法取得了重大进展。这些分析被称为液体活检,有可能提供诊断和预后信息、治疗选择指导以及肿瘤状态的实时信息。此外,磁共振成像(MRI)是肿瘤数据的另一个良好来源;放射组学和放射基因组学可以将成像表型与基因表达模式联系起来,并为肿瘤生物学和潜在分子特征提供见解。机器学习、深度学习和计算技术也可以利用定量成像特征来无创检测基因突变。通过液体活检和放射基因组学获得的关键分子信息不仅在胶质瘤的诊断中有用,还可以帮助预测对特定治疗的反应,并为个性化医疗提供指导。在本文中,我们回顾了使用液体活检和MRI等非侵入性方法对胶质瘤进行分子特征分析的现有数据,并建议这些工具未来可用于胶质瘤的术前诊断。