PDF(Pubmed)
Abstract：
Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving \"decision-making processes\" for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.
摘要：
胶质母细胞瘤的手术决策由于其复杂性和变异性而面临重大挑战。这项研究调查了人工智能（AI）工具在改善胶质母细胞瘤手术的“决策过程”方面的潜力。对文献的系统回顾确定了10项相关研究，主要集中在预测可切除性和手术相关的神经系统结局。AI工具,尤其是植根于影像组学和连接组学，在通过精确的肿瘤分割和肿瘤网络关系预测切除程度方面表现出希望。然而,由于患者相关因素的动态性和不可量化性,他们在预测术后神经系统方面的有效性有限.认识到这些挑战，包括有限的数据集和医疗应用中的可解释性要求，强调标准化的必要性，算法优化，并解决模型性能的变异性，然后在临床环境中进一步验证。虽然AI有潜力，它目前不具备模仿有经验的神经外科医生在胶质母细胞瘤手术的综合方法中使用的细微差别决策过程的能力.
