NIMG-59. COMPARATIVE ANALYSIS OF LONG-TERM GLIOBLASTOMA SURVIVORS USING MACHINE LEARNING GENERATED RADIO-PATHOMIC METRICS

Abstract BACKGROUND Glioblastoma (GBM) remains the most devastating central nervous system malignancy with poor overall survival (OS), even with gross total resection (GTR) and chemoradiation. Identifying factors associated with long-term survival (LTS) in GBM patients is challenging but crucial for improving prognostic models. This study compares an internal dataset of the top 10% long-term survivors of GBM (n=14) against a combined external cohort of 528 GBM patients who underwent GTR. METHODS We used a previously published machine learning-generated radio-pathomic model based on autopsy tissue as ground-truth to generate whole-brain maps of cell density and tumor probability maps (TPM) using conventional preoperative MRI sequences in both our internal dataset and a combined external cohort of GBM patients who underwent GTR from the UCSF-PDGM-v2 and UPenn-GBM databases. We then analyzed clinical and radio-pathomic metrics, including cell density and TPM values within contrast-enhancing (CE) and FLAIR hyperintense regions. Statistical comparisons were performed using t-tests. RESULTS Cell density metrics showed significant differences, with lower cell density in contrast-enhancing regions (t-value: 4.709, p<0.001; median: 1559 vs. 1959) and in FLAIR hyperintense regions (t-value: 3.585, p<0.001; median: 1356 vs. 1568) within the LTS group. TPM metrics also showed significant differences, with lower TPM values in contrast-enhancing regions (t-value: 3.312, p<0.001; median: 0.71 vs. 0.72) and in non-enhancing regions (t-value: 2.606, p<0.01; median: 0.68 vs. 0.69) in the LTS group. CONCLUSION Our comparative analysis reveals distinct radio-pathomic characteristics associated with long-term survival in GBM patients. Reduced cell density and lower TPM values in both contrast-enhancing and FLAIR hyperintense regions are significantly correlated with long-term survival. These findings suggest that integrating radio-pathomic data may enhance prognostic models and warrants further studies evaluating the relationship of these markers and OS.