A physiologically based pharmacokinetic model of vascular–extravascular exchanges during liver carcinogenesis: application to MRI contrast agents

Mescam, Muriel; Éliat, Pierre-Antoine; Fauvel, Claire; Certaines, J. de; Bézy-Wendling, Johanne

doi:10.1002/cmmi.147

Cited by 9 publications

(10 citation statements)

References 41 publications

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“…Thus, significant correlations between tumor size and v 0 or IFP were not found, and the correlations between v 0 and IFP found for Gd-DTPA were similar to those found for gadomelitol in both tumor lines. The plasma clearance rate of gadomelitol is less than half of that of Gd-DTPA (39), and gadomelitol has a diffusion coefficient in water that is only 20% to 25% of that of Gd-DTPA (40). However, gadomelitol is not superior to Gd-DTPA for assessment of v 0 because of the lower vessel wall permeability to gadomelitol.…”

Section: Discussionmentioning

confidence: 99%

Interstitial Fluid Pressure and Associated Lymph Node Metastasis Revealed in Tumors by Dynamic Contrast-Enhanced MRI

et al. 2012

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Elevated interstitial fluid pressure (IFP) in tumors can cause metastatic dissemination and treatment resistance, but its study poses a challenge because of a paucity of noninvasive imaging strategies. In this study, we address this issue by reporting the development of a noninvasive tool to assess tumor IFP and interstitial hypertension-induced lymph node metastasis. Using mouse xenograft models of several types of human cancer, we used gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA) as a contrast agent for dynamic contrastenhanced MRI (DCE-MRI). Immediately after Gd-DTPA administration, a high-signal-intensity rim was observed in the tumor periphery, which moved outward with time. Assuming the velocity of Gd-DTPA to be equal to the fluid flow velocity, we used a simple model of peritumoral interstitial fluid flow to calculate the fluid flow velocity at the tumor surface (v 0 ) based on the rim movement. Significant positive correlations were found between v 0 and IFP in all tumor xenografts. Moreover, the primary tumors of metastasis-positive mice displayed higher IFP and v 0 than the primary tumors of metastasis-negative mice. Findings were confirmed in cervical cancer patients with pelvic lymph node metastases, where we found v 0 to be higher compared with patients without lymph node involvement (P < 0.00001). Together, these findings establish that Gd-DTPA-based DCE-MRI can noninvasively visualize tumor IFP, and they reveal the potential for v 0 determined by this method to serve as a novel general biomarker of tumor aggressiveness. Cancer Res; 72(19); 4899-908. Ó2012 AACR.

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Section: Discussionmentioning

confidence: 99%

Interstitial Fluid Pressure and Associated Lymph Node Metastasis Revealed in Tumors by Dynamic Contrast-Enhanced MRI

et al. 2012

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“…The computational model described in [13] is able to simulate the liver vascular trees [15] in normal or some pathological situations [18]. This model allows the user to simulate the growth of the hepatic arteries, the portal vein and the hepatic veins, after an initialization phase where the patient specific main vessels can be introduced.…”

Section: Methodsmentioning

confidence: 99%

Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol

Simoncini

Jurczuk

Reska

et al. 2017

Med Biol Eng Comput

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Selective internal radiation therapy (SIRT) using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of liver cancer. A personalized intervention can lead to high concentration dose in the tumor, while sparing the surrounding parenchyma. We propose a computational model for patient-specific simulation of entire hepatic arterial tree, based on liver, tumors, and arteries segmentation on patient's tomography. Segmentation of hepatic arteries down to a diameter of 0.5 mm is semi-automatically performed on 3D cone-beam CT angiography. The liver and tumors are extracted from CT-scan at portal phase by an active surface method. Once the images are registered through an automatic multimodal registration, extracted data are used to initialize a numerical model simulating liver vascular network. The model creates successive bifurcations from given principal vessels, observing Poiseuille's and matter conservation laws. Simulations provide a coherent reconstruction of global hepatic arterial tree until vessel diameter of 0.05 mm. Microspheres distribution under simple hypotheses is also quantified, depending on injection site. The patient-specific character of this model may allow a personalized numerical approximation of microspheres final distribution, opening the way to clinical optimization of catheter placement for tumor targeting.

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“…For the simulation analysis performed in this study, the same model was used to generate the idealized contrast agent enhancement curves as was used to assess its performance using both the curve fitting and dictionary matching approaches. This approach will not demonstrate the ability of each method to characterize a more physiologically accurate model, such as those described in 25,26 . Future simulation studies should consider using a more detailed and complex pharmacokinetic model to generate idealized contrast agent enhancement curves.…”

Section: Discussionmentioning

confidence: 98%

Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach

Ghodasara

Chen

Pahwa

et al. 2020

Sci Rep

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perfusion properties can be estimated from pharmacokinetic models applied to Dce-MRi data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo Dce-MRi datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4-1.0% for arterial fraction, 0.5-1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1-2.1% for arterial fraction, 0.5-1.3% for distribution volume, and 0.2-1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in Dce-MRi. Dynamic contrast-enhanced (DCE) MRI data can be used with a variety of pharmacokinetic models to estimate perfusion properties through either an ROI-based or voxel-based analysis. However, many models are complex, and thus determination of fitted variables can be challenging, particularly when employing voxel-wise analysis as several thousand voxels must be evaluated to estimate many model parameters simultaneously, which poses significant computational burdens 1-5. Curve fitting algorithms are used to estimate properties of interest 6-8 , but this approach has many potential drawbacks. For instance, these algorithms can be extremely computationally expensive and require many hours to process just one dataset, which has led to the exploration of alternatives such as the linear least squares method 9-11. Curve fitting algorithms often also have numerous configuration options including initial property guesses, property bounds, algorithm choice, tolerances, and cost functions. The large number of available configuration options and underreporting of precise configurations causes difficulties in replicating perfusion modeling results across institutions. Lastly, curve fitting algorithms are vulnerable to converging on local minima 11. When this occurs, the best fit to the model is not optimally identified, resulting in an inaccurate estimate of perfusion properties. Furthermore, these occurrences may be difficult to identify. These drawbacks have significant consequences. Heye, et al. 12 have shown that there is considerable variation in perfusion properties quantified across analysis platforms such as Tissue4D (Siemens,

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A physiologically based pharmacokinetic model of vascular–extravascular exchanges during liver carcinogenesis: application to MRI contrast agents

Cited by 9 publications

References 41 publications

Interstitial Fluid Pressure and Associated Lymph Node Metastasis Revealed in Tumors by Dynamic Contrast-Enhanced MRI

Interstitial Fluid Pressure and Associated Lymph Node Metastasis Revealed in Tumors by Dynamic Contrast-Enhanced MRI

Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol

Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach

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