Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR...

Area of Science:

• Radiology and Imaging
• Oncology
• Neuroscience

Background:

• Gliomas are primary brain tumors with variable aggressiveness.
• Ki-67 labeling index (LI) is a key biomarker for glioma proliferation and grading.
• Accurate non-invasive prediction of Ki-67 LI is crucial for treatment planning.

Purpose of the Study:

• To evaluate the predictive capability of multi-parametric advanced MR imaging for Ki-67 LI in glioma patients.
• To establish a robust model for non-invasively estimating glioma proliferation.

Main Methods:

• Retrospective analysis of 109 glioma patients undergoing advanced MR imaging (DSC, MRS, DWI, DTI).
• Extraction of 21 imaging parameters including relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA), and choline/creatine (Cho/Cr) ratio.
• Stepwise multivariate regression and Pearson correlation analysis to develop predictive models and assess correlations with glioma grade.

Main Results:

• A multivariate model incorporating rCBV_max, rCBF_max, rADC_min, rFA_max, and Cho/Cr ratio predicted Ki-67 LI with high accuracy (R² = 0.8025).
• Individual parameters like rCBV_max, rCBF_max, rFA_max, Cho/Cr, and Cho/NAA positively correlated with Ki-67 LI and glioma grade.
• rADC_min and relative mean diffusivity (rMD)_min showed negative correlations with Ki-67 LI and glioma grade.

Conclusions:

• Multi-parametric advanced MR imaging (DSC, DTI, MRS) provides precise prediction of Ki-67 LI in glioma patients.
• This approach enables accurate, non-invasive assessment of glioma proliferation and grade.