Diffusion & Connectomics In Precision Healthcare Research (DiCIPHR) Lab, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2023 Jan 18;13(1):963. doi: 10.1038/s41598-022-26448-9.
In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients' survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p < 0.01). Clustering of patients using the proposed markers reveals distinct survival groups (logrank; p < 10, Cox hazard ratio = 1.82; p < 0.005). Our findings provide a panel of markers as surrogates of infiltration that might capture novel insight about underlying biology of peritumoral microstructural heterogeneity, providing potential biomarkers of prognosis pertaining to survival and molecular stratification, with applicability in clinical decision making.
在恶性原发性脑肿瘤中，癌细胞浸润到肿瘤周围的脑结构中，导致不可避免的复发。对肿瘤周围区域浸润异质性的定量评估，即活检或切除可能有危险的区域，对临床决策至关重要。在这里，我们通过使用基于弥散张量成像（DTI）的自由水体积分数图来描述肿瘤周围区域的自由水运动受限，从而得出一组新的基于人工智能（AI）的标志物，这些标志物可以捕捉肿瘤浸润的异质性。我们利用转移瘤和胶质母细胞瘤（前者由血管源性组成，后者包含浸润性水肿）在肿瘤周围区域的差异，提取基于体素的深度学习肿瘤周围微环境指数（PMI）。然后，提取局部区域高均一 PMI 值的神经中枢的描述性特征作为 AI 标志物，以捕获浸润异质性的不同方面。所提出的标志物用于对 275 名成人型弥漫性神经胶质瘤（CNS WHO 分级 4）患者的生存和 IDH1 突变状态进行分层。我们的研究结果表明，在不同总生存和 IDH1 突变状态的患者中，所提出的标志物存在显著差异（t 检验、Wilcoxon 秩和检验、线性回归；p<0.01）。使用所提出的标志物对患者进行聚类，揭示了不同的生存组（logrank;p<10，Cox 风险比=1.82;p<0.005）。我们的研究结果提供了一组作为浸润替代物的标志物，这些标志物可能提供有关肿瘤周围微结构异质性潜在生物学的新见解，提供与生存和分子分层相关的预后潜在生物标志物，适用于临床决策。
