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Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data.

Kontopodis E, Kanli G, Manikis GC, Van Cauter S, Marias K - Cancer Inform (2015)

Bottom Line: Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue.In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature.The results indicate that GCTT model's PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

View Article: PubMed Central - PubMed

Affiliation: Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece.

ABSTRACT
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of contrast leakage from the vascular tissue by using pharmacokinetic (PK) models. Such quantitative analysis of DCE-MRI data provides physiological parameters that are able to provide information of tumor pathophysiology and therapeutic outcome. Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue. In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature. To enhance the GCTT imaging biomarkers, we introduce a novel method for segmenting the tumor area into subregions according to their vascular heterogeneity characteristics. A cohort of 11 patients diagnosed with glioblastoma multiforme with known therapeutic outcome was used to assess the predictive value of both models in terms of correctly classifying responders and nonresponders based on only one DCE-MRI examination. The results indicate that GCTT model's PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

No MeSH data available.


Related in: MedlinePlus

Schematic diagram for converting signal intensity into concentrations. The rectangle shows the start point, the trapeziums show the parameters, and the circle includes the equations to be solved.
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Related In: Results  -  Collection


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f1-cin-suppl.4-2015-041: Schematic diagram for converting signal intensity into concentrations. The rectangle shows the start point, the trapeziums show the parameters, and the circle includes the equations to be solved.

Mentions: The overall procedure is depicted in Figure 1, where using the VFAs data, vector [S0, T10] is estimated using Equation (2). Afterward, using DCE-MRI data, the time course of the longitudinal relaxation time (T1(t)) is calculated by Equation (3). Substituting the pre-contrast relaxation time (T10) and the time course of the longitudinal relaxation time (T1(t)) in Equation (1); the CA concentration is calculated.


Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data.

Kontopodis E, Kanli G, Manikis GC, Van Cauter S, Marias K - Cancer Inform (2015)

Schematic diagram for converting signal intensity into concentrations. The rectangle shows the start point, the trapeziums show the parameters, and the circle includes the equations to be solved.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4536783&req=5

f1-cin-suppl.4-2015-041: Schematic diagram for converting signal intensity into concentrations. The rectangle shows the start point, the trapeziums show the parameters, and the circle includes the equations to be solved.
Mentions: The overall procedure is depicted in Figure 1, where using the VFAs data, vector [S0, T10] is estimated using Equation (2). Afterward, using DCE-MRI data, the time course of the longitudinal relaxation time (T1(t)) is calculated by Equation (3). Substituting the pre-contrast relaxation time (T10) and the time course of the longitudinal relaxation time (T1(t)) in Equation (1); the CA concentration is calculated.

Bottom Line: Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue.In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature.The results indicate that GCTT model's PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

View Article: PubMed Central - PubMed

Affiliation: Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational BioMedicine Lab, Heraklion, Greece.

ABSTRACT
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of contrast leakage from the vascular tissue by using pharmacokinetic (PK) models. Such quantitative analysis of DCE-MRI data provides physiological parameters that are able to provide information of tumor pathophysiology and therapeutic outcome. Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue. In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature. To enhance the GCTT imaging biomarkers, we introduce a novel method for segmenting the tumor area into subregions according to their vascular heterogeneity characteristics. A cohort of 11 patients diagnosed with glioblastoma multiforme with known therapeutic outcome was used to assess the predictive value of both models in terms of correctly classifying responders and nonresponders based on only one DCE-MRI examination. The results indicate that GCTT model's PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

No MeSH data available.


Related in: MedlinePlus