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Updated: Nov 17, 2025
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
Quantitative Characterization of Tumor Proximity to Stem Cell Niches: Implications on Recurrence and Survival in GBM
Yi Lao1, Victoria Yu2, Anthony Pham3
1Department of Radiation Oncology, University of California - Los Angeles, California.
Purpose:
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.
Methods And Materials:
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 (PSm-SCN and PSmax-SCN) 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.
Results:
Among all SCN-derived features, PSm-SCN was the strongest survival predictor (P < .0001). PSmax-SCN 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, PSm-SCN,PSm-SVZ, and center-to-ventricle metrics revealed a significantly closer SCN distribution of recurrence than primary tumors.
Conclusions:
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.
