Department of Computer Science & Engineering, International Institute of Information Technology, Bhubaneswar, 751003, India.
Victory Biotechnology Co., Ltd., Taipei, 114757, Taiwan.
J Cancer Res Clin Oncol. 2024 Jan 31;150(2):57. doi: 10.1007/s00432-023-05566-5.
Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions.
Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups.
By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category.
Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.
准确且无创地评估胶质母细胞瘤（GBM）患者的 MGMT 启动子甲基化状态具有至关重要的临床意义，因为它是一种与总生存期（OS）改善相关的预测生物标志物。为了满足临床需求，最近的研究集中在开发基于人工智能（AI）的非侵入性 MGMT 估计方法上。在本系统评价中，我们不仅深入探讨了这些 AI 驱动的 MGMT 估计方法的技术方面，还强调了它们的深远临床意义。具体来说，我们探讨了准确的非侵入性 MGMT 估计对 GBM 患者护理和治疗决策的潜在影响。
我们采用 PRISMA 搜索策略，从包括 PubMed、ScienceDirect、Google Scholar 和 IEEE Explore 在内的知名数据库中确定了 33 项相关研究。我们使用 21 种不同的属性对这些研究进行了全面评估，这些属性包括成像模式的类型、机器学习（ML）方法和队列规模等，对属性评分有明确的理由。随后，我们对这些研究进行了排名，并确定了一个截断值，将它们分为低偏倚和高偏倚组。
通过分析研究的“平均得分累积图”和“频率图曲线”，我们确定了一个 6.00 的截断值。较高的平均得分表示偏倚风险较低，得分高于截断值的研究被归类为低偏倚（73%），而 27%的研究归入高偏倚类别。
我们的研究结果强调了基于人工智能的机器学习（ML）和深度学习（DL）方法在无创确定 MGMT 启动子甲基化状态方面的巨大潜力。重要的是，这些 AI 驱动的进展的临床意义在于它们能够通过提供治疗决策的准确和及时信息来改变 GBM 患者的护理。然而，将这些技术进展转化为临床实践面临挑战，包括需要大型多机构队列和整合不同类型的数据。解决这些挑战将是关键，以充分发挥人工智能在提高 MGMT 估计的可靠性和可及性的同时降低临床决策偏倚风险的潜力。
