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Predictive markers for molecular subtypes and WHO grade: T1ρ-MRI in adult-type diffuse gliomas
Xingwei Dai1, Siyi He2, Yulong Qi1
1Medical Imaging Center, Peking University Shenzhen Hospital, Shenzhen, Guangdong, 518036, China.
Journal of Neuro-Oncology
|October 14, 2025
Summary
Quantitative T1ρ magnetic resonance imaging (T1ρ-MRI) shows potential for predicting glioma WHO grade and IDH mutation status. T1ρ-MRI at 500 Hz demonstrated superior performance for IDH mutation prediction compared to apparent diffusion coefficient (ADC) and lower frequencies.
Area of Science:
- Neuroimaging
- Oncology
- Radiology
Background:
- Adult-type diffuse gliomas are a significant challenge in neuro-oncology.
- Accurate prediction of World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) mutation, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is crucial for treatment planning.
- Quantitative T1ρ magnetic resonance imaging (T1ρ-MRI) is explored for its potential in non-invasively assessing glioma characteristics.
Purpose of the Study:
- To investigate the utility of quantitative T1ρ-MRI in predicting WHO grade, IDH mutation, and MGMT promoter methylation in adult-type diffuse gliomas.
- To compare the performance of T1ρ-MRI across different spin-lock frequencies (FSLs).
- To evaluate the diagnostic performance of T1ρ-MRI, apparent diffusion coefficient (ADC), and morphological MRI (mMRI) in glioma characterization.
Main Methods:
- Prospective recruitment of 62 patients undergoing preoperative imaging, including mMRI, diffusion-weighted imaging (DWI), and T1ρ-MRI.
- T1ρ-MRI acquisition at three FSLs: 100 Hz, 200 Hz, and 500 Hz.
- Statistical analysis using t-tests, chi-square tests, logistic regression, and receiver operating characteristic (ROC) curve analysis to assess predictive performance.
Main Results:
- Lower apparent diffusion coefficient (ADC) and T1ρ values were observed in high-grade gliomas (HGGs) and IDH wildtype tumors.
- T1ρ at 500 Hz was an independent predictor of IDH mutation status, showing the highest diagnostic performance (AUC = 0.873).
- The combination of mMRI and ADC improved diagnostic accuracy; however, adding T1ρ-MRI did not yield statistically significant improvements.
Conclusions:
- Quantitative T1ρ-MRI shows promise for predicting WHO grade and IDH mutation in gliomas.
- T1ρ-MRI at 500 Hz exhibited superior diagnostic performance for IDH mutation prediction compared to ADC and lower FSLs.
- While integrating mMRI and ADC enhances diagnostic accuracy, the additional benefit of T1ρ-MRI in the combined model was not statistically significant.
