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Enhanced Glioblastoma Detection Through 13C Hyperpolarized MRI and Two-Dimensional Statistics
Abdallah Salemdawood1, Dirk Mayer1,2, Abubakr Eldirdiri1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, Maryland, USA.
Magnetic Resonance in Medicine
|July 21, 2026
Summary
A new hyperpolarized 13C MRI (hpMRI) method statistically delineates glioblastoma (GBM) tumors. This technique shows high accuracy in animal models and clinical brain cancer patients, aiding surgical and therapeutic decisions.
Area of Science:
- Oncology
- Medical Imaging
- Metabolic Imaging
Background:
- Glioblastoma (GBM) presents a poor prognosis and resistance to therapies.
- Hyperpolarized 13C MRI (hpMRI) offers metabolic insights for tumor boundary delineation.
- Statistically confident tumor outlines can guide treatment and surgical planning.
Purpose of the Study:
- To develop a statistically significant tumor delineation method using hpMRI.
- To enhance the precision of glioblastoma boundary identification.
- To improve surgical guidance and treatment evaluation for GBM.
Main Methods:
- Applied time-resolved hyperpolarized [1-13C]pyruvate data from rat glioma models and a clinical brain cancer patient.
- Utilized MATLAB for metabolite profile extraction and analysis.
- Combined high pyruvate and lactate signal time-points, smoothed data, and employed a sliding window for statistical analysis against normal tissue.
Main Results:
- Achieved 99% NPV and 59% PPV for glioblastoma delineation in small animals.
- Obtained 95% NPV and 92% PPV in a clinical brain cancer patient, accounting for bicarbonate.
- Demonstrated the method's effectiveness across different scales.
Conclusions:
- Combining high pyruvate and lactate signal time-points enhances statistical power in 2D testing.
- The developed hpMRI technique shows promise for accurate glioblastoma delineation.
- Further validation in larger patient cohorts is recommended.
