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Background <p>This study aimed to investigate the utility of quantitative T1ρ magnetic resonance imaging (T1ρ-MRI) in predicting the WHO grade, isocitrate dehydrogenase (IDH) mutation, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas, and compare their performance across different spin-lock frequencies (FSLs).</p> Methods <p>A total of 62 patients were prospectively recruited and underwent preoperative imaging, including morphological MRI (mMRI), diffusion-weighted imaging (DWI) and T1ρ-MRI. T1ρ was performed with FSLs set at 100&#xa0;Hz, 200&#xa0;Hz, and 500&#xa0;Hz. Group comparisons by IDH mutation, WHO grade, and MGMT status were performed using t-tests and chi-square tests. Logistic regression analysis was employed to identify features independently. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the predictive models.</p> Results <p>The apparent diffusion coefficient (ADC) and T1ρ values were significantly lower in patients with high-grade gliomas (HGGs) and IDH wildtype tumors compared to the control group (<i>p</i> &lt; 0.05). T1ρ (FSL = 500&#xa0;Hz) was identified as an independent predictor of IDH mutation status (<i>p</i> &lt; 0.05) and demonstrated the highest diagnostic performance for predicting IDH mutation (AUC = 0.873; 95% CI: 0.782–0.951; <i>p</i> &lt; 0.001). The combination of mMRI and ADC outperformed individual parameters. When T1ρ was added to the mMRI + ADC model, the resulting mMRI + ADC + T1ρ model achieved higher AUC values for both WHO grade and IDH mutation than the mMRI + ADC model alone, but these differences were not statistically significant (0.948 vs. 0.933, <i>p</i> = 0.205; 0.920 vs. 0.866, <i>p</i> = 0.150, respectively).</p> Conclusion <p>T1ρ-MRI demonstrates promising clinical value in predicting WHO grade and IDH mutation in gliomas. Notably, T1ρ at 500&#xa0;Hz showed superior diagnostic performance compared to ADC and lower FSL settings. Integrating mMRI and ADC further enhances diagnostic accuracy, whereas adding T1ρ-MRI provides no significant additional benefit despite a slight numerical gain.</p>

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Predictive markers for molecular subtypes and WHO grade: T1ρ-MRI in adult-type diffuse gliomas

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Xingwei Dai,
  • Siyi He,
  • Yulong Qi,
  • Hao Li,
  • Meiying Chen,
  • Qin Zhao,
  • Liping Qing,
  • Haibo Su,
  • Demin Xu,
  • Keyan Yu,
  • Huancheng Wang,
  • Zhonghua Li,
  • Guanxun Cheng,
  • Mingmei Li

摘要

Background

This study aimed to investigate the utility of quantitative T1ρ magnetic resonance imaging (T1ρ-MRI) in predicting the WHO grade, isocitrate dehydrogenase (IDH) mutation, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in adult-type diffuse gliomas, and compare their performance across different spin-lock frequencies (FSLs).

Methods

A total of 62 patients were prospectively recruited and underwent preoperative imaging, including morphological MRI (mMRI), diffusion-weighted imaging (DWI) and T1ρ-MRI. T1ρ was performed with FSLs set at 100 Hz, 200 Hz, and 500 Hz. Group comparisons by IDH mutation, WHO grade, and MGMT status were performed using t-tests and chi-square tests. Logistic regression analysis was employed to identify features independently. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the predictive models.

Results

The apparent diffusion coefficient (ADC) and T1ρ values were significantly lower in patients with high-grade gliomas (HGGs) and IDH wildtype tumors compared to the control group (p < 0.05). T1ρ (FSL = 500 Hz) was identified as an independent predictor of IDH mutation status (p < 0.05) and demonstrated the highest diagnostic performance for predicting IDH mutation (AUC = 0.873; 95% CI: 0.782–0.951; p < 0.001). The combination of mMRI and ADC outperformed individual parameters. When T1ρ was added to the mMRI + ADC model, the resulting mMRI + ADC + T1ρ model achieved higher AUC values for both WHO grade and IDH mutation than the mMRI + ADC model alone, but these differences were not statistically significant (0.948 vs. 0.933, p = 0.205; 0.920 vs. 0.866, p = 0.150, respectively).

Conclusion

T1ρ-MRI demonstrates promising clinical value in predicting WHO grade and IDH mutation in gliomas. Notably, T1ρ at 500 Hz showed superior diagnostic performance compared to ADC and lower FSL settings. Integrating mMRI and ADC further enhances diagnostic accuracy, whereas adding T1ρ-MRI provides no significant additional benefit despite a slight numerical gain.