Multiparametric study for glioma grading with FLAIR, ADC map, eADC map, T1 map, and SWI images

Abstract

PURPOSE: This paper is a preliminary attempt to compare the diagnostic efficiency and performance of Fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC) map, exponential ADC (eADC) map, T(1) map, and Susceptibility-weighted image (SWI) for glioma grading and combine these image data pairs to compare the diagnostic performance of different image data pairs for glioma grading. MATERIAL AND METHODS: Fifty-nine patients underwent FLAIR, ADC map, eADC map, Variable flip-angle (VFA) spoiled gradient recalled echo (SPGR) method, and SWI MRI imaging. The T(1) map was reconstructed by the VFA-SPGR method. The average Relative Signal Contrast (RSC) and receiver operating characteristic curve (ROC) was calculated in a different image. The multivariate binary logistic regression model combined different image data pairs. RESULTS: The average RSC of SWI and ADC maps in high-grade glioma is significantly lower than RSCs in low-grade. The average RSC of the eADC map and T(1) maps increased with glioma grade. No significant difference was detected between low and high-grade glioma on FLAIR images. The AUC for low and high-grade glioma differentiation on ADC maps, eADC maps, T(1) map, and SWI were calculated 0.781, 0.864, 0.942, and 0.904, respectively. Also, by adding different image data, diagnostic performance was increased. CONCLUSION: Interestingly, the T(1) map and SWI image have the potential to use in the clinic for glioma grading purposes due to their high performance. Also, the eADC map+T(1) map and T(1) map+SWI image weights have the highest diagnostic performance for glioma grading.