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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

ktrans PK parameter computed in the whole tumor region of interest also offers a good separability while the 70% percentile was the best metric.
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f6-cin-suppl.4-2015-041: ktrans PK parameter computed in the whole tumor region of interest also offers a good separability while the 70% percentile was the best metric.

Mentions: In our study, the GCTT model’s PK parameters performed better than Tofts’, while the segmentation of the tumor ROIs based on vascular heterogeneity further enhanced the discriminatory power of the GCTT model. The results indicate that the GCTT model may be more efficient in characterizing the chaotic vascular structure of tumor and subsequent pathophysiological characteristics such as the delayed extraction of the CA in the EES. Also, in search for the best model according to the optimal multivariate linear regression presented, the GCCT PK parameters outperformed the Tofts ones. ktrans computed from ETM performed well in our study, and in particular the 70% percentile. Figure 6 shows the histogram profiles for ktrans for all the patients. The ROC results showed, however, that the overall best PK parameter is the extraction fraction E (70% percentile) of the GCCT model but only when computed in the “Heterogeneous” subregion (in tables of results referred to as region 3). This is further highlighted in Figure 7, where it can be observed that the histograms of this parameter between responders and nonresponders become morphologically more separable in the “Heterogeneous” subregion than in the whole tumor ROI as annotated in the MRI data. If this result is confirmed in future studies, it has the potential to enhance the robustness of PK imaging biomarkers from DCE-MRI and widen their clinical adoption for aiding the therapy monitoring process. However, being aware of the limited cohort group of this study and the restriction to only GBM tumor, we suggest more extensive studies in a broader range of patients and tumor types in order to establish which model is better for the early prediction of response in cancer patients.


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)

ktrans PK parameter computed in the whole tumor region of interest also offers a good separability while the 70% percentile was the best metric.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f6-cin-suppl.4-2015-041: ktrans PK parameter computed in the whole tumor region of interest also offers a good separability while the 70% percentile was the best metric.
Mentions: In our study, the GCTT model’s PK parameters performed better than Tofts’, while the segmentation of the tumor ROIs based on vascular heterogeneity further enhanced the discriminatory power of the GCTT model. The results indicate that the GCTT model may be more efficient in characterizing the chaotic vascular structure of tumor and subsequent pathophysiological characteristics such as the delayed extraction of the CA in the EES. Also, in search for the best model according to the optimal multivariate linear regression presented, the GCCT PK parameters outperformed the Tofts ones. ktrans computed from ETM performed well in our study, and in particular the 70% percentile. Figure 6 shows the histogram profiles for ktrans for all the patients. The ROC results showed, however, that the overall best PK parameter is the extraction fraction E (70% percentile) of the GCCT model but only when computed in the “Heterogeneous” subregion (in tables of results referred to as region 3). This is further highlighted in Figure 7, where it can be observed that the histograms of this parameter between responders and nonresponders become morphologically more separable in the “Heterogeneous” subregion than in the whole tumor ROI as annotated in the MRI data. If this result is confirmed in future studies, it has the potential to enhance the robustness of PK imaging biomarkers from DCE-MRI and widen their clinical adoption for aiding the therapy monitoring process. However, being aware of the limited cohort group of this study and the restriction to only GBM tumor, we suggest more extensive studies in a broader range of patients and tumor types in order to establish which model is better for the early prediction of response in cancer patients.

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