Poursaeed Roya, Mohammadzadeh Mohsen, Safaei Ali Asghar
Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
BMC Cancer. 2024 Dec 27;24(1):1581. doi: 10.1186/s12885-024-13320-4.
Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain cancer in humans. The median survival time of GBM patients is only 12-15 months, making it the most lethal type of brain tumor. Every year, about 200,000 people worldwide succumb to this disease. GBM is also highly heterogeneous, meaning that its characteristics and behavior vary widely among different patients. This leads to different outcomes and survival times for each individual. Predicting the survival of GBM patients accurately can have multiple benefits. It can enable optimal and personalized treatment planning based on the patient's condition and prognosis. It can also support the patients and their families to cope with the possible outcomes and make informed decisions about their care and quality of life. Furthermore, it can assist the researchers and scientists to discover the most relevant biomarkers, features, and mechanisms of the disease and to design more effective and personalized therapies. Artificial intelligence methods, such as machine learning and deep learning, have been widely applied to survival prediction in various fields, such as breast cancer, lung cancer, gastric cancer, cervical cancer, liver cancer, prostate cancer, and covid 19. This systematic review summarizes the current state-of-the-art methods for predicting glioblastoma survival using different types of input data, such as clinical features, molecular markers, imaging features, radiomics features, omics data or a combination of them. Following PRISMA guidelines, we searched databases from 2015 to 2024, reviewing 107 articles meeting our criteria. We analyzed the data sources, methods, performance metrics and outcomes of the studies. We found that random forest was the most popular method, and a combination of radiomics and clinical data was the most common input data.
多形性胶质母细胞瘤（GBM）被世界卫生组织（WHO）归类为IV级胶质瘤，是一种常见且极具侵袭性的源自神经胶质细胞的脑肿瘤形式。它是人类原发性脑癌最严重的形式之一。GBM患者的中位生存时间仅为12 - 15个月，使其成为最致命的脑肿瘤类型。每年，全球约有20万人死于这种疾病。GBM也是高度异质性的，这意味着其特征和行为在不同患者之间差异很大。这导致每个个体有不同的结果和生存时间。准确预测GBM患者的生存情况有多种益处。它可以基于患者的病情和预后实现最佳的个性化治疗规划。它还可以帮助患者及其家人应对可能的结果，并就他们的护理和生活质量做出明智的决定。此外，它可以协助研究人员和科学家发现该疾病最相关的生物标志物、特征和机制，并设计更有效和个性化的疗法。人工智能方法，如机器学习和深度学习，已被广泛应用于各种领域的生存预测，如乳腺癌、肺癌、胃癌、宫颈癌、肝癌、前列腺癌和新冠19。本系统综述总结了使用不同类型输入数据（如临床特征、分子标志物、影像特征、放射组学特征、组学数据或它们的组合）预测胶质母细胞瘤生存的当前最先进方法。遵循PRISMA指南，我们检索了2015年至2024年的数据库，审查了107篇符合我们标准的文章。我们分析了这些研究的数据来源、方法、性能指标和结果。我们发现随机森林是最受欢迎的方法，放射组学和临床数据的组合是最常见的输入数据。
