Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
MAGMA. 2023 Feb;36(1):33-42. doi: 10.1007/s10334-022-01045-z. Epub 2022 Oct 26.
Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI.
The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome.
Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases.
The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.
高级别胶质瘤（HGG）患者的治疗反应评估严重依赖于 MRI 上病变大小的变化。然而，在常规 MRI 中，治疗相关的变化可能表现为增强组织，与活跃肿瘤组织的表现相似。我们提出了一种基于动态对比增强（DCE）MRI 的无模型数据驱动方法来区分这些组织。
这项研究共包括 66 名胶质母细胞瘤患者的扫描。其中，48 个来自 1 个 MRI 供应商，18 个扫描来自不同的 MRI 供应商，并用作测试数据。在 48 个中，有 24 个扫描有活检结果。分析包括半自动动脉输入函数（AIF）提取、直接 DCE 药代动力学样特征提取以及两种组织类型的无监督聚类。通过（a）与活检结果的比较，（b）与每种组织类型的文献中基于 DCE 曲线的相关性，以及（c）与临床结果的比较进行验证。
在 24 个活检病例中，模型预测与活检结果之间存在 20 个病例的一致性。在活跃肿瘤和治疗相关变化之间，预测成分与基于人群的模板之间的平均相关性分别为 82%和 90%。根据 RANO 标准，预测结果与放射科医生评估之间存在 11/12 个病例的一致性。
所提出的方法可以作为区分病变组织和治疗相关变化的一种非侵入性方法。
