Section of Neurosurgery, Department of Surgery, Aga Khan University, Karachi, Pakistan.
Medical College, Aga Khan University, Karachi, Pakistan.
J Neurooncol. 2022 Jan;156(2):217-231. doi: 10.1007/s11060-021-03933-1. Epub 2022 Jan 12.
Being the most common primary brain tumor, glioblastoma presents as an extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying molecular epidemiology of glioblastoma between patients and intra-tumoral heterogeneity explains the failure of current one-size-fits-all treatment modalities. Radiomics uses machine learning to identify salient features of the tumor on brain imaging and promises patient-specific management in glioblastoma patients.
We performed a comprehensive review of the available literature on studies investigating the role of radiomics and radiogenomics models for the diagnosis, stratification, prognostication as well as treatment planning and monitoring of glioblastoma.
Classifiers based on a combination of various MRI sequences, genetic information and clinical data can predict non-invasive tumor diagnosis, overall survival and treatment response with reasonable accuracy. However, the use of radiomics for glioblastoma treatment remains in infancy as larger sample sizes, standardized image acquisition and data extraction techniques are needed to develop machine learning models that can be translated effectively into clinical practice.
Radiomics has the potential to transform the scope of glioblastoma management through personalized medicine.
作为最常见的原发性脑肿瘤，胶质母细胞瘤的治疗极具挑战性，尽管进行了治疗，但其预后仍较差。患者之间以及肿瘤内异质性的胶质母细胞瘤的不同分子流行病学解释了目前一刀切治疗方式的失败。放射组学使用机器学习来识别脑成像上肿瘤的显著特征，并有望为胶质母细胞瘤患者提供个体化管理。
我们对现有关于放射组学和放射基因组学模型在胶质母细胞瘤的诊断、分层、预后以及治疗计划和监测中的作用的研究进行了全面综述。
基于各种 MRI 序列、遗传信息和临床数据的组合分类器可以合理准确地预测无创性肿瘤诊断、总生存期和治疗反应。然而，放射组学在胶质母细胞瘤治疗中的应用仍处于起步阶段，需要更大的样本量、标准化的图像采集和数据提取技术，以开发能够有效转化为临床实践的机器学习模型。
通过个性化医疗，放射组学有可能改变胶质母细胞瘤的管理范围。
