Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging
1Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Related Experiment Videos
Quantitative Immunohistochemistry of the Cellular Microenvironment in Patient Glioblastoma Resections
Published on: July 31, 2017
06:32Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
Published on: January 9, 2019
09:17Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma
Published on: September 13, 2022
View abstract on PubMed
Advanced MR imaging parameters accurately predict glioma cell proliferation (Ki-67 LI). Combining dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), diffusion-tensor imaging (DTI), and MR spectroscopy imaging (MRS) offers precise non-invasive assessment for glioma patients.
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 rCBVmax, rCBFmax, rADCmin, rFAmax, and Cho/Cr ratio predicted Ki-67 LI with high accuracy (R2 = 0.8025).
- Individual parameters like rCBVmax, rCBFmax, rFAmax, Cho/Cr, and Cho/NAA positively correlated with Ki-67 LI and glioma grade.
- rADCmin 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.