Evaluation of IAUGC indices and two DCE-MRI pharmacokinetic parameters assessed by two different theoretical algorithms in patients with brain tumors

The diagnostic efficiency of the perfusion-related parameters in assessing the vascular disrupting agent (CA4P) response in a rabbit VX2 liver tumor model

BACKGROUND

There are inconsistencies when concomitantly using dynamic contrast enhancement (DCE) and intravoxel incoherent motion (IVIM) to evaluate diagnostic efficiency.

PURPOSE

To evaluate the diagnostic efficiency of perfusion-related parameters in assessing the effect of Combretastatin-A4-phosphate (CA4P) in a rabbit VX2 liver tumor model using DCE and IVIM.

MATERIAL AND METHODS

Twenty rabbits implanted with VX2 tumors were included in the study. The perfusion-parameters of DCE (K^trans and iAUC60) and IVIM (f and D*) were measured at baseline and 4 h after administration of CA4P. Subsequently, the rabbits were euthanized. Pre- and post-treatment perfusion parameters were analyzed using paired t-test. Correlation between the various perfusion parameters and correlation of perfusion parameters with microvascular density (MVD) were assessed using Pearson correlation analysis. The diagnostic efficiency was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

All perfusion parameters (K^trans, iAUC60, f and D*) showed significant decrease after 4 h of CA4P administration (all P < 0.001). Post-treatment perfusion parameters showed a moderate correlation with MVD (r = 0.663, r = 0.567, r = 0.685, r = 0.618, respectively; all P < 0.05). At baseline and after treatment, K^trans values and iAUC60 showed correlation with f and D* (all P < 0.05). Concomitant use of perfusion parameters of DCE and IVIM showed the best diagnostic performance, which was slightly greater than that observed with individual application of DCE or IVIM (AUC = 0.915, 0.880, and 0.895, respectively).

CONCLUSION

Although concomitant application of DCE and IVIM can slightly improve the diagnostic value in assessing the effect of CA4P, the values were relatively small.

An extended reference region model for DCE-MRI that accounts for plasma volume

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated K^trans by over 10% at v_p  = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible v_p where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

Hybrid Invasive Weed Optimization Algorithm for Parameter Estimation of Pharmacokinetic Model

Given that the traditional methods of estimating pharmacokinetic parameters are constrained by the sensitivity of their initial value and incapability of evolutionary algorithm to determine search range, this paper proposes a hybrid invasive weed optimization (HIWO) algorithm by combining the Hooke–Jeeves (HJ) and invasive weed optimization (IWO) algorithm. Using the HIWO algorithm for the parameter optimization by the experiment of extravascular administration two-compartment model, we can see that the proposed method is not only better than traditional feathering method (FM) in terms of numerical stability, but also better than HJ and IWO in terms of error minimization. The experimental results show that HIWO algorithm is a feasible method to optimize the pharmacokinetic parameters, which has higher precision and stronger robustness than the other techniques.

Comparison of Cerebral Blood Volume and Plasma Volume in Untreated Intracranial Tumors

Purpose

Plasma volume and blood volume are imaging-derived parameters that are often used to evaluation intracranial tumors. Physiologically, these parameters are directly related, but their two different methods of measurements, T1-dynamic contrast enhanced (DCE)- and T2-dynamic susceptibility contrast (DSC)-MR utilize different model assumptions and approaches. This poses the question of whether the interchangeable use of T1-DCE-MRI derived fractionated plasma volume (vp) and relative cerebral blood volume (rCBV) assessed using DSC-MRI, particularly in glioblastoma, is reliable, and if this relationship can be generalized to other types of brain tumors. Our goal was to examine the hypothetical correlation between these parameters in three most common intracranial tumor types.

Methods

Twenty-four newly diagnosed, treatment naïve brain tumor patients, who had undergone DCE- and DSC-MRI, were classified in three histologically proven groups: glioblastoma (n = 7), meningioma (n = 9), and intraparenchymal metastases (n = 8). The rCBV was obtained from DSC after normalization with the normal-appearing anatomically symmetrical contralateral white matter. Correlations between these parameters were evaluated using Pearson (r), Spearman's (ρ) and Kendall’s tau-b (τ_B) rank correlation coefficient.

Results

The Pearson, Spearman and Kendall’s correlation between vp with rCBV were r = 0.193, ρ = 0.253 and τ_B = 0.33 (p-_Pearson = 0.326, p-_Spearman= 0.814 and p-_Kendall= 0.823) in glioblastoma, r = -0.007, ρ = 0.051 and τ_B = 0.135 (p-_Pearson = 0.970, p-_Spearman= 0.765 and p-_Kendall= 0.358) in meningiomas, and r = 0.289, ρ = 0.228 and τ_B = 0.239 (p-_Pearson = 0.109, p-_Spearman= 0.210 and p-_Kendall= 0.095) in metastasis.

Conclusion

Results indicate that no correlation exists between vp with rCBV in glioblastomas, meningiomas and intraparenchymal metastatic lesions. Consequently, these parameters, as calculated in this study, should not be used interchangeably in either research or clinical practice.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.