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Tumor-Associated Tractography Derived from High-Angular-Resolution Q-Space MRI May Predict Patterns of Cellular Invasion in Glioblastoma †.

Authors :
Leary, Owen P.
Zepecki, John P.
Pizzagalli, Mattia
Toms, Steven A.
Liu, David D.
Suita, Yusuke
Ding, Yao
Wang, Jihong
He, Renjie
Chung, Caroline
Fuller, Clifton D.
Boxerman, Jerrold L.
Tapinos, Nikos
Gilbert, Richard J.
Source :
Cancers; Nov2024, Vol. 16 Issue 21, p3669, 21p
Publication Year :
2024

Abstract

Simple Summary: While tumor cell invasion beyond the surgically resectable "margin" of glioblastoma is thought to be associated with nearly 100% of recurrences and poor survival, no reliable methods exist for mapping the location of invading tumor cells within the human brain. Building on prior work demonstrating the ability of Q-space magnetic resonance imaging (QSI) to highlight structural alterations in tissue architecture, we hypothesized that using this imaging method to construct tumor-associated tractography might identify tumor-specific structures that underlie cellular invasion. Our results demonstrate that such tractography patterns can be observed in a tumor-specific manner, and provide preliminary evidence that those patterns may colocalize with invading tumor cells, and metrics derived from them may be associated with patient survival time. Background: The invasion of glioblastoma cells beyond the visible tumor margin depicted by conventional neuroimaging is believed to mediate recurrence and predict poor survival. Radiomic biomarkers that are associated with the direction and extent of tumor infiltration are, however, non-existent. Methods: Patients from a single center with newly diagnosed glioblastoma (n = 7) underwent preoperative Q-space magnetic resonance imaging (QSI; 3T, 64 gradient directions, b = 1000 s/mm<superscript>2</superscript>) between 2018 and 2019. Tumors were manually segmented, and patterns of inter-voxel coherence spatially intersecting each segmentation were generated to represent tumor-associated tractography. One patient additionally underwent regional biopsy of diffusion tract- versus non-tract-associated tissue during tumor resection for RNA sequencing. Imaging data from this cohort were compared with a historical cohort of n = 66 glioblastoma patients who underwent similar QSI scans. Associations of tractography-derived metrics with survival were assessed using t-tests, linear regression, and Kaplan–Meier statistics. Patient-derived glioblastoma xenograft (PDX) mice generated with the sub-hippocampal injection of human-derived glioblastoma stem cells (GSCs) were scanned under high-field conditions (QSI, 7T, 512 gradient directions), and tumor-associated tractography was compared with the 3D microscopic reconstruction of immunostained GSCs. Results: In the principal enrollment cohort of patients with glioblastoma, all cases displayed tractography patterns with tumor-intersecting tract bundles extending into brain parenchyma, a phenotype which was reproduced in PDX mice as well as in a larger comparison cohort of glioblastoma patients (n = 66), when applying similar methods. Reconstructed spatial patterns of GSCs in PDX mice closely mirrored tumor-associated tractography. On a Kaplan–Meier survival analysis of n = 66 patients, the calculated intra-tumoral mean diffusivity predicted the overall survival (p = 0.037), as did tractography-associated features including mean tract length (p = 0.039) and mean projecting tract length (p = 0.022). The RNA sequencing of human tissue samples (n = 13 tumor samples from a single patient) revealed the overexpression of transcripts which regulate cell motility in tract-associated samples. Conclusions: QSI discriminates tumor-specific patterns of inter-voxel coherence believed to represent white matter pathways which may be susceptible to glioblastoma invasion. These findings may lay the groundwork for future work on therapeutic targeting, patient stratification, and prognosis in glioblastoma. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20726694
Volume :
16
Issue :
21
Database :
Complementary Index
Journal :
Cancers
Publication Type :
Academic Journal
Accession number :
180784703
Full Text :
https://doi.org/10.3390/cancers16213669