Quantitative characterization of tumor proximity to stem cell niches: implications on recurrence and survival in GBM patients.

Abstract Purpose s: Emerging evidence has linked glioblastoma multiforme (GBM) recurrence and survival to the stem cell niches (SCN). However, the traditional tumor-ventricle distance is insufficiently powered for accurate prediction. We aim to use a novel inverse distance map for improved prediction. Methods Two T1-MRI datasets were included: 237 pre-operative scans for prognostic stratification and 55 follow-up scans for recurrent pattern identification. SCN, including subventricular zones (SVZ) and subgranular zones (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 max proximity scores ( P S m − S C N and P S m a x − S C N ) were calculated for each primary/recurrent tumor, deformably transformed into the template. The prognostic capacity of 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 the recurrent tumors. For comparison, the same analyses were conducted on PS derived from SVZ alone and traditional edge/center-to-ventricle (EV/CV) metrics. Results Among all SCN-derived features, P S m − S C N was the strongest survival predictor (p P S m a x − S C N was the best in risk stratification, using either evenly sorted (p=0.0001) or k-means clustering methods (p=0.0045). PS metrics based on SVZ only also correlated with OS and risk stratification, while to a lesser degree of significance. In contrast, CV and EV metrics showed weak to no prediction capacities in either task. Moreover, P S m − S C N , P S m − S V Z and CV revealed a significantly closer SCN distribution of recurrence than primary tumors. Conclusions We introduced a novel inverse distance-based metric to comprehensively capture the anatomical relationship between GBM tumors to SCN zones. The derived metrics outperformed traditional edge or center distance-based measurements in OS prediction, risk stratification, and recurrent pattern differentiation. Our results reveal the potential role of SGZ in recurrence besides SVZ.