Multicompartment analysis of gadolinium chelate kinetics: Blood-tissue exchange in mammary tumors as monitored by dynamic MR imaging

Background:The semi-quantitative analysis of the time–intensity curves in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a limited specificity due to overlapping enhancement patterns after gadolinium administration. With the advances in technology and faster sequences, imaging of the entire breast can be done in a few seconds, which allows measuring the transit of contrast (transfer constant: Ktrans) through the vascular bed at capillary level that reflects quantitative measure of porosity/permeability of tumor vessels.Aim:Our study aims to evaluate the pharmacokinetic parameter Ktrans for enhancing breast lesions and correlate it with histopathology, and assess accuracy, sensitivity, and specificity of this parameter in discriminating benign and malignant breast lesions.Materials and Methods:One hundred and fifty-one women with 216 histologically proved enhancing breast lesions underwent high temporal resolution DCE-MRI for the early dynamic analysis for calculation of pharmacokinetic parameters (Ktrans) using standard two compartment model. The calculated values of Ktrans were correlated with histopathology to calculate the sensitivity, specificity, and accuracy.Results:Receiver operating characteristic (ROC) curve analysis revealed a mean Ktrans value of 0.56, which reliably distinguished benign and malignant breast lesions with a sensitivity of 91.1% and specificity of 90.3% with an overall accuracy of 89.3%. The area under curve (AUC) was 0.907.Conclusion:Ktrans is a reliable quantitative parameter for characterizing benign and malignant lesions in routine DCE-MRI of breasts.

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“…The pharmacokinetic analysis of DCE-MRI in the assessment of the microcirulatory properties of breast cancer has been reported. [67]…”

Section: Discussionmentioning

confidence: 99%

Role of quantitative pharmacokinetic parameter (transfer constant: Ktrans) in the characterization of breast lesions on MRI

Jena

Taneja

Mehta

2013

Indian J Radiol Imaging

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“…We should note that a bi-compartmental model does not consider the complex biology of the tumor. Although multi-compartment models have been proposed [40], the open bi-compartmental model has been able to fit DCE-DRI data surprisingly well and is therefore widely accepted by the research community. Time-course changes in tissue CA concentration are modeled as a result of first-order exchange of the CA molecules between compartments.…”

Section: Methodsmentioning

confidence: 99%

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

et al. 2013

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BackgroundDCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.ResultsPixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains.ConclusionsA user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/.

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“…In particular, the LM has been applied to study multiple sclerosis, 113 assess heart diseases, 4,134,223-227 quantify regional myocardial perfusion in healthy humans, 228,229 and diagnose breast cancer. 230,231 The BM, which is particularly attractive since the AIF need not be known a priori, 232 has been used to study the brain 114,160,233 and breast tumors, 7,135,160,[233][234][235][236][237][238] as well as to analyze contrast uptake patterns in other applications, including intracranial meningiomas, 239 malignant pleural mesothelioma, 240 cervical cancer [241][242][243] and its chemoradiotherapy outcome, 232,244 colorectal 245 and liver tumors, 246 prostate diseases, 12,247,248 and bone perfusion. 185 The most straightforward PK models to interpret are the TK and ETK models, which have been extensively applied to characterize the brain, 3,115,175,249-255 lung, 256,257 breast, 200,[258][259][260][261][262][263][264][265]…”

Section: E Clinical Applications Of Parametric Modelsmentioning

confidence: 99%

Models and methods for analyzing DCE‐MRI: A review

Khalifa¹,

Soliman²,

El-Baz³

et al. 2014

Medical Physics

261

230

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Multicompartment analysis of gadolinium chelate kinetics: Blood-tissue exchange in mammary tumors as monitored by dynamic MR imaging

Cited by 134 publications

References 35 publications

Role of quantitative pharmacokinetic parameter (transfer constant: Ktrans) in the characterization of breast lesions on MRI

Role of quantitative pharmacokinetic parameter (transfer constant: Ktrans) in the characterization of breast lesions on MRI

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

Models and methods for analyzing DCE‐MRI: A review

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