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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

Model-averaged importance of the statistically significant parameters.
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Related In: Results  -  Collection


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f5-cin-suppl.4-2015-041: Model-averaged importance of the statistically significant parameters.

Mentions: Following an extensive screening of all possible model combinations in the identification of the optimal model for predicting the therapeutic outcome, the information criterion profile of all models is graphically depicted in Figure 4. According to this profile, the model with the lowest AICc is composed of the parameters “GCTT_tc_0_95”, “GCTT_PS_4_5”, and “GCTT_Vp_1_30” with AICc = 7.4395. To facilitate comparison among the different parameters through the wrapper model selection, Figure 5 highlights the estimated importance of each parameter. The nonzero coefficients from the Lasso model, related to the parameters from the best fitted model, resulted in parameters “GCTT_tc_0_95”, “GCTT_PS_4_5”, “GCTT_Vp_1_30” (same parameters from the selected model using AICc), “GCTT_Vp_1_70” (fourth top-ranked parameter as depicted in Fig. 5), “GCTT_ Ktrans_3_Std”, and “GCTT_Ktrans_4_5”. The parameters “GCTT_Ktrans_3_Std” and “GCTT_Ktrans_4_5” were rejected at the preprocessing phase of the analysis using AICc because the corresponding P-values were higher than 1%.


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)

Model-averaged importance of the statistically significant parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5-cin-suppl.4-2015-041: Model-averaged importance of the statistically significant parameters.
Mentions: Following an extensive screening of all possible model combinations in the identification of the optimal model for predicting the therapeutic outcome, the information criterion profile of all models is graphically depicted in Figure 4. According to this profile, the model with the lowest AICc is composed of the parameters “GCTT_tc_0_95”, “GCTT_PS_4_5”, and “GCTT_Vp_1_30” with AICc = 7.4395. To facilitate comparison among the different parameters through the wrapper model selection, Figure 5 highlights the estimated importance of each parameter. The nonzero coefficients from the Lasso model, related to the parameters from the best fitted model, resulted in parameters “GCTT_tc_0_95”, “GCTT_PS_4_5”, “GCTT_Vp_1_30” (same parameters from the selected model using AICc), “GCTT_Vp_1_70” (fourth top-ranked parameter as depicted in Fig. 5), “GCTT_ Ktrans_3_Std”, and “GCTT_Ktrans_4_5”. The parameters “GCTT_Ktrans_3_Std” and “GCTT_Ktrans_4_5” were rejected at the preprocessing phase of the analysis using AICc because the corresponding P-values were higher than 1%.

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