Conclusion
Different DWI metrics fitted within different b-value ranges (low to ultra-high b values) have different efficacies as a surrogate indicator for molecular expression or microstructural complexity in gliomas. Further studies are needed to better explain the biological meanings of these DWI parameters in gliomas.
Methods
A retrospective study was performed for 41 patients with gliomas. The standard apparent diffusion coefficient (ADCst) and ADC under ultra-high b values (ADCuh) (b values: 2500 to 5000 s/mm2) were calculated based on monoexponential model. The fraction of fast diffusion (f), pseudo ADC (ADCfast) and true ADC (ADCslow) were calculated by bi-exponential model (b values: 0 to 2000 s/mm2). The apparent diffusional kurtosis (Kapp) was derived from the simplified diffusion kurtosis imaging (DKI) model (b values: 200 to 3000 s/mm2). Potential correlations between DWI parameters and immunohistological indices (i.e. Aquaporin (AQP)1, AQP4, AQP9 and Ki-67) were investigated and DWI parameters were compared between high- and low-grade gliomas, and between tumor center and peritumor. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were calculated to determine the performance of independent or combined DWI parameters in grading gliomas.
Results
The ADCslow and ADCuh at tumor center showed a stronger correlation with Ki-67 than other DWI metrics. The ADCst, ADCslow and ADCuh at tumor center presented correlations with AQP1 and AQP4 while AQP9 did not correlate with any DWI metric. Kapp showed a correlation with Ki-67 while no significant correlation with AQPs. ADCst (p < 0.001) and ADCslow (p = 0.001) were significantly lower while the ADCuh (p = 0.006) and Kapp (p = 0.005) were significantly higher in the high-grade than in the low-grade gliomas. ADCst, f, ADCfast, ADCslow, ADCuh, Kapp at the tumor center had significant differences with those in peritumor when the gliomas grade became high (p < 0.05). Involving ADCuh and Kapp simultaneously into an independent ADCst model (AUC = 0.833) could further improve the grading performance (ADCst+ADCuh+Kapp: AUC = 0.923).