Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Abstract

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F ( P = 0.018), PS ( P < 0.001 ), Vp ( P < 0.001 ), E ( P < 0.001 ), and Ve ( P = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in Ktrans ( P = 0.013) and Ve ( P < 0.001 ) for IDH-mutant gliomas. No significant difference was observed in Kep ( P = 0.525). The AUCs of Ktrans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, P < 0.001 ). Ktrans showed a weak correlation with F (r < 0.3, P > 0.16) and a very weak correlation with PS (r < 0.06, P > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment.

Multicenter DSC–MRI-Based Radiomics Predict IDH Mutation in Gliomas

“…This observation is in accordance with recent studies highlighting the importance of the initial components of the signal time-intensity curve for IDH status determination [34]. Signal deviation in these regions correlates to predictable vascularity phenotypes of IDH-mutant and IDH-wildtype tumors [35], as determined by IDH-specific vascular gene signatures [36]. Specifically, Choi et al [34] demonstrated that the curve components between T_0 and T_max performed best for the identification of IDH-mutant versus IDH-wildtype gliomas by applying an explainable recurrent neural network to attribute attention weights to specific parts of the T2* signal time-intensity curve.…”

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

“…DCE studies assessing the permeability parameters also showed potential for distinguishing between the two molecular entities. IDHmut gliomas were found to exhibit decreased CBF, volume transfer constant between blood plasma and extravascular extracellular space (Ktrans), blood plasma fractional volume (Vp), extravascular extracellular volume fraction (Ve), and area under the curve (AUC) [ 11 , 41 , 42 ]. With respect of ASL-CBF, some authors like Brendle et al and Yoo et al reported it to be useful to distinguish between these two subgroups [ 43 , 44 ], while others found only a moderate correlation [ 12 ].…”

Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors

“…Classical TK models such as EXTOFTS have been used to distinguish between benign and malignant STTs [ 13 – 15 ]. However, these two models are limited by using only a single constant transfer parameter (K trans ) to model transport, and thereby cannot distinguish between the transport of tracer molecules in blood vessels and the exchange process of tracer molecules between blood vessels and tissues [ 16 ].…”

“…Advanced pharmacokinetic models, such as CC, ATH, and DP, have been proposed, providing a more accurate explanation of tracer transport in the tissue microenvironment, and rendering derived parameters that better describe the tumor tissue microenvironment [ 9 , 11 , 16 , 17 ]. These three models are concerned with two transport processes: intravascular perfusion of tracer molecules, and osmotic exchange inside and outside the vessel through the vessel wall.…”

“…These three models are concerned with two transport processes: intravascular perfusion of tracer molecules, and osmotic exchange inside and outside the vessel through the vessel wall. By using individual parameters for blood flow (F) and permeability-surface area product (PS), they successfully describe these processes [ 16 ]. There are differences in the three models: CC posits that tracer concentrations are evenly distributed between the inner and outer vascular spaces, whereas the ATH suggests that tracer concentrations fluctuate over time and space within the intravascular space [ 10 , 11 , 17 ].…”