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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

Proposed methodology for separating the tumor image area into four subregions based on the vascular heterogeneity characteristics (α−1). This way, in each subregion as well as in the whole tumor region, the value of all PK GCTT parameters can be assessed separately.
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Related In: Results  -  Collection


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f2-cin-suppl.4-2015-041: Proposed methodology for separating the tumor image area into four subregions based on the vascular heterogeneity characteristics (α−1). This way, in each subregion as well as in the whole tumor region, the value of all PK GCTT parameters can be assessed separately.

Mentions: In order to enhance the application of the GCTT model in real clinical data, a preprocessing step is proposed by exploiting the α−1 parameter. For this purpose, the MR image of the tumor was segmented based on each voxel’s α−1 value, in order to separate tumor into subregions of similar vascular heterogeneity characteristics. After extensive experimentation and observation of the α−1 histogram characteristics in the tumor ROIs, a four-subregion segmentation scheme was proposed, as shown in Figure 2. It was observed that, in most of the cases, the histogram distributions of the α−1 parameter include three characteristic peaks and a plateau in the same value intervals. The α−1 boundary values were determined empirically based on the average histogram profile. The first subregion consists of the first peak, the second subregion consists of the linear histogram part, the third subregion consists of the second peak, and the fourth subregion consists of the third peak. Provided that the average profile is similar in future studies, it is possible to use the same values and test by other researchers.


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)

Proposed methodology for separating the tumor image area into four subregions based on the vascular heterogeneity characteristics (α−1). This way, in each subregion as well as in the whole tumor region, the value of all PK GCTT parameters can be assessed separately.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.4-2015-041: Proposed methodology for separating the tumor image area into four subregions based on the vascular heterogeneity characteristics (α−1). This way, in each subregion as well as in the whole tumor region, the value of all PK GCTT parameters can be assessed separately.
Mentions: In order to enhance the application of the GCTT model in real clinical data, a preprocessing step is proposed by exploiting the α−1 parameter. For this purpose, the MR image of the tumor was segmented based on each voxel’s α−1 value, in order to separate tumor into subregions of similar vascular heterogeneity characteristics. After extensive experimentation and observation of the α−1 histogram characteristics in the tumor ROIs, a four-subregion segmentation scheme was proposed, as shown in Figure 2. It was observed that, in most of the cases, the histogram distributions of the α−1 parameter include three characteristic peaks and a plateau in the same value intervals. The α−1 boundary values were determined empirically based on the average histogram profile. The first subregion consists of the first peak, the second subregion consists of the linear histogram part, the third subregion consists of the second peak, and the fourth subregion consists of the third peak. Provided that the average profile is similar in future studies, it is possible to use the same values and test by other researchers.

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