Department of Radiation Oncology, University of California - Los Angeles, California.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Int J Radiat Oncol Biol Phys. 2021 Jul 15;110(4):1180-1188. doi: 10.1016/j.ijrobp.2021.02.020. Epub 2021 Feb 16.
Emerging evidence has linked glioblastoma multiforme (GBM) recurrence and survival to stem cell niches (SCNs). However, the traditional tumor-ventricle distance is insufficiently powered for an accurate prediction. We aimed to use a novel inverse distance map for improved prediction.
Two T1-magnetic resonance imaging data sets were included for a total of 237 preoperative scans for prognostic stratification and 55 follow-up scans for recurrent pattern identification. SCN, including the subventricular zone (SVZ) and subgranular zone (SGZ), were manually defined on a standard template. A proximity map was generated using the summed inverse distances to all SCN voxels. The mean and maximum proximity scores (PS and PS) were calculated for each primary/recurrent tumor, deformably transformed into the template. The prognostic capacity of proximity score (PS)-derived metrics was assessed using Cox regression and log-rank tests. To evaluate the impact of SCNs on recurrence patterns, we performed group comparisons of PS-derived metrics between the primary and recurrent tumors. For comparison, the same analyses were conducted on PS derived from SVZ alone and traditional edge/center-to-ventricle metrics.
Among all SCN-derived features, PS was the strongest survival predictor (P < .0001). PS was the best in risk stratification, using either evenly sorted (P = .0001) or k-means clustering methods (P = .0045). PS metrics based on SVZ only also correlated with overall survival and risk stratification, but to a lesser degree of significance. In contrast, edge/center-to-ventricle metrics showed weak to no prediction capacities in either task. Moreover, PS,PS, and center-to-ventricle metrics revealed a significantly closer SCN distribution of recurrence than primary tumors.
We introduced a novel inverse distance-based metric to comprehensively capture the anatomic relationship between GBM tumors and SCN zones. The derived metrics outperformed traditional edge or center distance-based measurements in overall survival prediction, risk stratification, and recurrent pattern differentiation. Our results reveal the potential role of SGZ in recurrence aside from SVZ.
越来越多的证据表明，多形性胶质母细胞瘤（GBM）的复发和存活与干细胞龛（SCN）有关。然而，传统的肿瘤-脑室距离不足以进行准确的预测。我们旨在使用新的逆距离图来提高预测精度。
共纳入 237 例术前 T1 磁共振成像扫描用于预后分层，55 例随访扫描用于识别复发模式，总共纳入 237 例术前扫描用于预后分层，55 例随访扫描用于识别复发模式。手动在标准模板上定义 SCN，包括侧脑室下区（SVZ）和颗粒下区（SGZ）。使用所有 SCN 体素的总和逆距离生成接近度图。为每个原发/复发肿瘤计算平均和最大接近度评分（PS 和 PS），并将其变形为模板。使用 Cox 回归和对数秩检验评估接近度评分（PS）衍生指标的预后能力。为了评估 SCN 对复发模式的影响，我们在原发肿瘤和复发肿瘤之间比较了 PS 衍生指标的组间差异。为了比较，还对仅基于 SVZ 的 PS 衍生指标和传统的边缘/中心到脑室的指标进行了相同的分析。
在所有 SCN 衍生特征中，PS 是最强的生存预测指标（P <.0001）。PS 是风险分层的最佳指标，无论是均匀排序（P =.0001）还是 K-均值聚类方法（P =.0045）。仅基于 SVZ 的 PS 指标也与总生存和风险分层相关，但显著程度较低。相比之下，边缘/中心到脑室的指标在这两个任务中都显示出较弱或没有预测能力。此外，PS、PS 和中心到脑室的指标显示复发肿瘤与原发肿瘤的 SCN 分布更为接近。
我们引入了一种新的基于逆距离的指标，全面捕捉了 GBM 肿瘤与 SCN 区之间的解剖关系。与传统的边缘或中心距离测量方法相比，衍生指标在总生存预测、风险分层和复发模式分化方面表现更好。我们的结果揭示了 SGZ 在复发中的潜在作用，而不仅仅是 SVZ。
