Faculty, Staff and Student Publications

Publication Date

7-1-2025

Journal

Journal of Computer Assisted Tomography

DOI

10.1097/RCT.0000000000001716

PMID

39876523

PMCID

PMC12237096

PubMedCentral® Posted Date

1-27-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Treatment-related changes may occur due to radiation and temozolomide in glioblastoma and can mimic tumor progression on conventional MRI. DCE-MRI enables quantification of the extent of blood-brain barrier (BBB) disruption, providing information about areas of suspicious postcontrast T1 enhancement. We compared DCE-MRI processing methods for distinguishing true disease progression from pseudoprogression in high-grade gliomas (HGGs).

Methods: We identified 110 patients with HGG treated with surgery and chemoradiation who underwent DCE-MRI to further interrogate areas of new/increasing enhancement. All patients had confirmatory surgery/biopsy with pathology-confirmed progression or pseudoprogression. Scans were performed at 3T and analyzed using nordicICE. The MCA, SSS, and Parker models are three standardized processing methodologies used to create k trans maps, a parameter that quantifies BBB permeability. Three equal regions of interest were placed at sites of peak contrast enhancement within each lesion. Data from each method was processed for mean and maximum k trans . We conducted several rounds of analysis and finalized a strategy on penalized support vector machines based on engineered features with bootstrap sampling.

Results: The Parker method was significant for k trans maximum in the combined pathology and clinical as well as the pathology-only data sets. MCA and SSS did not perform well under the SVM classifier for pathology only. For clinical follow-up subjects, the Parker method yielded statistically significant results for maximum and mean k trans .

Conclusions: The Parker method was effective in distinguishing PD and PsP for pathology and clinical data sets. MCA and SSS techniques were effective for the clinical data set.

Keywords

Humans, Glioma, Brain Neoplasms, Contrast Media, Magnetic Resonance Imaging, Disease Progression, Male, Female, Middle Aged, Adult, Aged, Diagnosis, Differential, Image Enhancement, Image Interpretation, Computer-Assisted, Neoplasm Grading, Brain, tumor progression, pseudoprogression, glioma, dynamic contrast-enhanced (DCE)-MRI, perfusion MRI, ktrans

Published Open-Access

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