Room: Track 2
Understanding the individual patterns of recurrence in glioblastoma multiforme (GBM) and patient survival is essential for personalized radiotherapy. Emerging evidence has linked recurrence and survival to the stem cell niches (SCN). However, the correlation based on tumor-ventricle distance alone is insufficiently powered for accurate prediction. The study aims to capture a comprehensive geometrical relationship using the MRI inverse distance map for improved prediction.
Two T1w MRI datasets were included: 102 preoperative scans for prognostic stratification, and 65 follow-up scans for recurrent pattern identification. SCN segmentation, including bilateral subventricular zones and subgranular zones, were manually defined based on a standard template. A normalized proximity map was generated using the sum of the power 1 inverse distance weightings to all SCN observations. A mean proximity score (PS) was calculated for each primary/recurrent tumor, deformably transformed into the common template. The prognostic capacity of PS was evaluated using Cox regression with overall survival (OS) and Log-rank tests between sorted and evenly divided high-risk and low-risk groups. To further evaluate the impact of SCNs on recurrence patterns, group comparisons of PS between the primary and the recurrent tumors were performed. For comparison, the same analyses were conducted on traditional SCN geometrical features, including tumor edge to the ventricle (EV) and center to the ventricle (CV).
Among 3 SCN features, PS is the only significant predictor of OS (p = 0.0297) and the best performer in risk stratification (log-rank p = 0.0474). All metrics revealed significant differences between primary and recurrent tumors in SCN proximity, while PS results in the lowest p-value (p = 0.0017).
We introduced a novel inverse distance-based metric, PS, to more effectively characterize GBM tumor to SCN zones geometrical relationships. PS outperformed traditional edge or center distance-based measurements in OS prediction, risk stratification, and recurrent pattern differentiation.
Not Applicable / None Entered.