Diffusion Weighted Imaging in Gliomas: A Histogram-Based Approach for Tumor Characterization.

Area of Science:

• Neuroimaging
• Oncology
• Radiology

Background:

• Astrocytic gliomas exhibit overlapping appearances on conventional MRI, necessitating advanced diagnostic techniques.
• Quantitative diffusion-weighted imaging (DWI) using apparent diffusion coefficient (ADC) histograms offers valuable insights for tumor characterization and prognosis.
• Distinguishing low-grade gliomas (LGG) from high-grade gliomas (HGG) preoperatively remains a diagnostic challenge.

Purpose of the Study:

• To evaluate the efficacy of ADC histogram analysis (HA) in differentiating LGG from HGG.
• To investigate the association between ADC histogram parameters and Ki-67, IDH1 mutation, and MGMT promoter methylation profiles.
• To enhance preoperative diagnostic accuracy for astrocytic gliomas.

Main Methods:

• ADC histograms were computed for 82 gliomas.
• Statistical analyses were performed to correlate histogram features with WHO grade, Ki-67, IDH1, and MGMT status.
• Key histogram parameters analyzed included minimum, maximum, percentiles, median, modus, standard deviation (SD), entropy, and skewness.

Main Results:

• Lower ADC histogram values (minimum, lower percentiles, median, modus, entropy) were significantly associated with HGG.
• Significant differences in ADC histogram features were observed between IDH1-mutated and IDH1-wildtype gliomas.
• ADC HA demonstrated significant correlations with Ki-67 proliferation index, but not with MGMT promoter methylation status.

Conclusions:

• ADC histogram analysis is a valuable non-invasive tool for predicting WHO grade in astrocytic gliomas.
• ADC HA can help predict tumor proliferation rates (Ki-67) and the presence of IDH1 mutations.
• This quantitative MRI approach improves preoperative diagnostic capabilities for astrocytic gliomas.