BIOM-27. VOXEL-WISE SURVIVAL ANALYSIS IN GLIOBLASTOMA: TUMOR PROBABILITY MAPS REVEAL CRITICAL REGIONS NEAR WHITE MATTER TRACTS

Abstract BACKGROUND Glioblastoma is the most aggressive primary brain tumor, characterized by rapid growth and poor prognosis. Understanding spatial determinants of survival in GBM is crucial for improving prognostic accuracy and treatment strategies. Our study employs voxel-wise survival analysis (VSA) of tumor probability maps (TPMs) derived from MRI to identify high-risk brain regions. TPMs are generated through radio-pathomic mapping and can predict non-enhancing tumor regions not captured by traditional imaging. We aim to determine if high tumor probability regions are near previously identified critical white matter (WM) tracts and assess their impact on survival outcomes. METHODS We analyzed data from 374 GBM patients from the UCSF-PDGM-v2 dataset. Using a pretrained radio-pathomic model, TPMs were predicted based on T1, T1C, ADC, and FLAIR MRI sequences. All TPMs were rigidly registered to MNI space using ANTs. A Cox proportional hazards model was applied voxel-wise to assess the association between tumor probability and overall survival (OS). A p-value map was generated, thresholded, and cluster corrected (p < 0.05) for further analysis. RESULTS The VSA revealed that high tumor probability regions were significantly associated with poorer OS in GBM. Gray matter (GM) regions near WM tracts with high tumor probability showed markedly reduced survival. GM regions adjacent to the left IFOF (p < 0.05, HR = 1.19, 845 vs. 961 days for high/low TPM), Corpus Callosum Forceps Minor (p = 0.0001, HR = 1, 740 vs. 902 days), Body of the Corpus Callosum (p = 0.005, HR = 0.97, 748 vs. 798 days for high/low TPM) and other WM tracts, such as the left thalamic radiation superior and left reticulospinal tracts had similar trends. CONCLUSIONS Our study demonstrates that regions with high tumor probability, particularly those in the GM near critical WM tracts, are strongly associated with worse overall survival in GBM patients.

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Journal
Neuro-Oncology
Published
2024-11-01
DOI
https://doi.org/10.1093/neuonc/noae165.0100
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

BIOM-27. VOXEL-WISE SURVIVAL ANALYSIS IN GLIOBLASTOMA: TUMOR PROBABILITY MAPS REVEAL CRITICAL REGIONS NEAR WHITE MATTER TRACTS

Fitzgerald Kyereme, Daniel Aaronson, Max Krucoff, Biprojit Nath et al.
Neuro-Oncology
Radiomics and Machine Learning in Medical Imaging
article

BIOM-27. VOXEL-WISE SURVIVAL ANALYSIS IN GLIOBLASTOMA: TUMOR PROBABILITY MAPS REVEAL CRITICAL REGIONS NEAR WHITE MATTER TRACTS

Fitzgerald Kyereme, Daniel Aaronson, Max Krucoff, Biprojit Nath, Kelly Mrachek, Aleksandra Winiarz, Peter S. LaViolette, Anjishnu Banerjee, Samuel Bobholz, Savannah Duenweg, Allison K. Lowman, Jennifer Connelly
article en

Abstract

Abstract BACKGROUND Glioblastoma is the most aggressive primary brain tumor, characterized by rapid growth and poor prognosis. Understanding spatial determinants of survival in GBM is crucial for improving prognostic accuracy and treatment strategies. Our study employs voxel-wise survival analysis (VSA) of tumor probability maps (TPMs) derived from MRI to identify high-risk brain regions. TPMs are generated through radio-pathomic mapping and can predict non-enhancing tumor regions not captured by traditional imaging. We aim to determine if high tumor probability regions are near previously identified critical white matter (WM) tracts and assess their impact on survival outcomes. METHODS We analyzed data from 374 GBM patients from the UCSF-PDGM-v2 dataset. Using a pretrained radio-pathomic model, TPMs were predicted based on T1, T1C, ADC, and FLAIR MRI sequences. All TPMs were rigidly registered to MNI space using ANTs. A Cox proportional hazards model was applied voxel-wise to assess the association between tumor probability and overall survival (OS). A p-value map was generated, thresholded, and cluster corrected (p < 0.05) for further analysis. RESULTS The VSA revealed that high tumor probability regions were significantly associated with poorer OS in GBM. Gray matter (GM) regions near WM tracts with high tumor probability showed markedly reduced survival. GM regions adjacent to the left IFOF (p < 0.05, HR = 1.19, 845 vs. 961 days for high/low TPM), Corpus Callosum Forceps Minor (p = 0.0001, HR = 1, 740 vs. 902 days), Body of the Corpus Callosum (p = 0.005, HR = 0.97, 748 vs. 798 days for high/low TPM) and other WM tracts, such as the left thalamic radiation superior and left reticulospinal tracts had similar trends. CONCLUSIONS Our study demonstrates that regions with high tumor probability, particularly those in the GM near critical WM tracts, are strongly associated with worse overall survival in GBM patients.

Neuro-OncologyVol. 26(Supplement_8)
Medical College of Wisconsin (US)
No poverty
Openalex Percentile: Top 67%
Radiomics and Machine Learning in Medical Imaging
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