PDF(Pubmed)
Abstract：
Glioblastoma (GBM) is the most aggressive and the most common primary brain tumor, defined by nearly uniform rapid progression despite the current standard of care involving maximal surgical resection followed by radiation therapy (RT) and temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) of less than 30% at 2 years. The diagnosis of tumor progression in the clinic is based on clinical assessment and the interpretation of MRI of the brain using Response Assessment in Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity of precise measures of progression. Given that imaging is the primary modality that generates the most quantitative data capable of capturing change over time in the standard of care for GBM, this renders it pivotal in optimizing and advancing response criteria, particularly given the lack of biomarkers in this space. In this study, we employed artificial intelligence (AI)-derived MRI volumetric parameters using the segmentation mask output of the nnU-Net to arrive at four classes (background, edema, non-contrast enhancing tumor (NET), and contrast-enhancing tumor (CET)) to determine if dynamic changes in AI volumes detected throughout therapy can be linked to PFS and clinical features. We identified associations between MR imaging AI-generated volumes and PFS independently of tumor location, MGMT methylation status, and the extent of resection while validating that CET and edema are the most linked to PFS with patient subpopulations separated by district rates of change throughout the disease. The current study provides valuable insights for risk stratification, future RT treatment planning, and treatment monitoring in neuro-oncology.
摘要：
胶质母细胞瘤（GBM）是最具侵袭性和最常见的原发性脑肿瘤，定义为几乎一致的快速进展，尽管目前的治疗标准包括最大程度的手术切除，然后进行放射治疗（RT）和替莫唑胺（TMZ）或同步化疗（CRT），2年总生存率（OS）低于30％。临床上肿瘤进展的诊断基于临床评估和使用神经肿瘤学反应评估（RANO）标准对脑部MRI的解释。它受到一些限制，包括缺乏精确的进展措施。鉴于成像是产生能够捕获GBM护理标准中随时间变化的最定量数据的主要方式。这使得它在优化和推进响应标准方面至关重要，特别是考虑到这个领域缺乏生物标志物。在这项研究中,我们采用人工智能(AI)推导的MRI体积参数，使用nnU-Net的分割掩模输出得出四个类(背景，水肿,非对比增强肿瘤（NET），和对比增强肿瘤（CET）），以确定在整个治疗过程中检测到的AI体积的动态变化是否与PFS和临床特征有关。我们确定了MR成像AI生成的体积与PFS之间的关联，而与肿瘤位置无关。MGMT甲基化状态，和切除程度，同时验证CET和水肿与PFS的相关性最大，患者亚群按整个疾病的地区变化率分开。当前的研究为风险分层提供了有价值的见解，未来的RT治疗计划，和神经肿瘤学的治疗监测。
