A Kinetic Model for Human Platelet Survival with Long-Lasting Time-Activity Curves

Abstract Purpose: Even though the semi quantitative dynamic analysis of 18F-FDOPA PET effectively and non-invasively predicts isocitrate dehydrogenase (IDH) mutations in newly-diagnosed gliomas, the underlying kinetic model of 18F-FDOPA is complex. Our current study addresses whether a semi quantitative analysis indeed captures all the clinically relevant predictive features of the more sophisticated graphical and compartmental models.Methods: Thirty-seven tumour time-activity curves from 18F-FDOPA PET dynamic acquisitions of newly-diagnosed gliomas were analysed using a semi quantitative model with (Ref SQ) or without reference region (SQ), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model validated for 18F-FDOPA PET imaging in gliomas (2TCM). The overall predictive performance of each model for predicting IDH mutations was assessed, by an area under the curve (AUC) comparison of multivariate analyses of all parameters included in the model.Results: SQ model with an AUC of 0.733 showed comparable performances to other models with AUCs of 0.814, 0.693, 0.786, 0.863, respectively corresponding to Ref SQ, Logan, Ref Logan and 2 TCM (p≥0.11 for the pairwise comparisons with other models). SQ time-to-peak parameter had the best diagnostic performance relative to all individual parameters with an accuracy of 75.7%.Conclusions: The SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performances compared to other models most notably the compartmental model for predicting IDH mutations. This validates the application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in routine clinical practice for newly-diagnosed gliomas.

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Evaluation of the 11CO2 Positron Emission Tomographic Method for Measuring Brain pH. I. pH Changes Measured in States of Altered PCO2

Journal of Cerebral Blood Flow & Metabolism ◽

10.1038/jcbfm.1987.125 ◽

1987 ◽

Vol 7 (6) ◽

pp. 709-719 ◽

Cited By ~ 26

Author(s):

Richard B. Buxton ◽

Nathaniel M. Alpert ◽

Viken Babikian ◽

Steven Weise ◽

John A. Correia ◽

...

Keyword(s):

Kinetic Model ◽

Model Fit ◽

Log P ◽

Dissolved Gas ◽

Time Activity ◽

Bicarbonate Ions ◽

Positron Emission ◽

Tomographic Method ◽

Brain Ph

The 11CO2 method for measuring local brain pH with positron emission tomography (PET) has been experimentally evaluated, testing the adequacy of the kinetic model and the ability of the method to measure changes in brain pH. Plasma and tissue time/activity curves measured during and following continuous inhalation of 11CO2 were fit with a kinetic model that includes effects of tissue pH, blood flow, and fixation of CO2 into compounds other than dissolved gas and bicarbonate ions. For each of ten dogs, brain pH was measured with PET at two values of PaCO2 (range 21–67 mm Hg). The kinetic model fit the data well during both inhalation and washout of the label, with residual root mean square (RMS) deviations of the model from the measurements consistent with the statistical quality of the PET data. Brain pH calculated from the PET data shows a linear variation with log(PaCO2). These results were in good agreement with previously reported measurements of brain pH, both in absolute value and in variation with PCO2. The interpretation of these pH values in normal and pathological states is discussed.

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Voxel-Based Estimation of Kinetic Model Parameters of the l-[1-11C]Leucine PET Method for Determination of Regional Rates of Cerebral Protein Synthesis: Validation and Comparison with Region-of-Interest-Based Methods

Journal of Cerebral Blood Flow & Metabolism ◽

10.1038/jcbfm.2009.52 ◽

2009 ◽

Vol 29 (7) ◽

pp. 1317-1331 ◽

Cited By ~ 20

Author(s):

Giampaolo Tomasi ◽

Alessandra Bertoldo ◽

Shrinivas Bishu ◽

Aaron Unterman ◽

Carolyn Beebe Smith ◽

...

Keyword(s):

Protein Synthesis ◽

Kinetic Model ◽

Region Of Interest ◽

Nonlinear Least Squares ◽

Measured Data ◽

Parameter Estimates ◽

Model Parameters ◽

Time Activity ◽

Positron Emission ◽

Cerebral Protein Synthesis

We adapted and validated a basis function method (BFM) to estimate at the voxel level parameters of the kinetic model of the l-[1-11C]leucine positron emission tomography (PET) method and regional rates of cerebral protein synthesis (rCPS). In simulation at noise levels typical of voxel data, BFM yielded low-bias estimates of rCPS; in measured data, BFM and nonlinear least-squares parameter estimates were in good agreement. We also examined whether there are advantages to using voxel-level estimates averaged over regions of interest (ROIs) in place of estimates obtained by directly fitting ROI time-activity curves (TACs). In both simulated and measured data, fits of ROI TACs were poor, likely because of tissue heterogeneity not taken into account in the kinetic model. In simulation, rCPS determined from fitting ROI TACs was substantially overestimated and BFM-estimated rCPS averaged over all voxels in an ROI was slightly underestimated. In measured data, rCPS determined by regional averaging of voxel estimates was lower than rCPS determined from ROI TACs, consistent with simulation. In both simulated and measured data, intersubject variability of BFM-estimated rCPS averaged over all voxels in a ROI was low. We conclude that voxelwise estimation is preferable to fitting ROI TACs using a homogeneous tissue model.

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DETERMINATION OF HUMAN PLATELET SURVIVAL UTILIZING C14-LABELED SEROTONIN*

Journal of Clinical Investigation ◽

10.1172/jci104439 ◽

1961 ◽

Vol 40 (12) ◽

pp. 2134-2142 ◽

Cited By ~ 38

Author(s):

Robert M. Heyssel

Keyword(s):

Human Platelet ◽

Platelet Survival

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A Methodological Investigation of Healthy Tissue, Hepatocellular Carcinoma and other Lesions with Dynamic 68Ga-FAPI-04 PET/CT Imaging

10.21203/rs.3.rs-92507/v1 ◽

2020 ◽

Author(s):

Barbara Katharina Geist ◽

Haiqun Xing ◽

Jingnan Wang ◽

Ximin Shi ◽

Haitao Zhao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Kinetic Model ◽

Kinetic Parameters ◽

Input Function ◽

Information Criteria ◽

Healthy Tissue ◽

Model Parameters ◽

Time Activity ◽

Tissue Compartment ◽

Pet Scans

Abstract Background: The study aimed to establish a 68Ga-FAPI-04 kinetic model in hepatic lesions, to determine the potential role of kinetic parameters in the differentiation of hepatocellular carcinoma (HCC) from non-HCC lesions.Material and Methods: Time activity curves (TACs) were extracted from seven HCC lesions and five non-HCC lesions obtained from 68Ga-FAPI-04 dynamic positron emission tomography (PET) scans of eight patients. Three kinetic models were applied to the TACs, using image derived hepatic artery and/or portal vein as input functions. For input functions and the lesions, the according voxel with the maximum standardized uptake value (SUVmax) was taken, for the healthy tissue mean SUV values. The optimum model was chosen after applying the Schwartz information criteria to the TACs, differences in model parameters between HCC, non-HCC lesions, and healthy tissue were evaluated with the ANOVA test. Results: A reversible two-tissue compartment model using both the arterial as well as venous input function was most preferred and showed significant differences in the kinetic parameters VND, VT and BPND between HCC, non-HCC lesions and healthy regions (p < 0.01). Conclusion: Several Model parameters derived from a two-tissue compartment kinetic model with two image-derived input function from vein and aorta and using SUVmax allow a differentiation between HCC and non-HCC lesions, obtained from dynamically performed PET scans using FAPI.

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A methodological investigation of healthy tissue, hepatocellular carcinoma, and other lesions with dynamic 68Ga-FAPI-04 PET/CT imaging

EJNMMI Physics ◽

10.1186/s40658-021-00353-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Barbara Katharina Geist ◽

Haiqun Xing ◽

Jingnan Wang ◽

Ximin Shi ◽

Haitao Zhao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Kinetic Model ◽

Kinetic Parameters ◽

Hepatic Artery ◽

Input Function ◽

Healthy Tissue ◽

Model Parameters ◽

Time Activity ◽

Tissue Compartment ◽

Pet Scans

Abstract Background The study aimed to establish a 68Ga-FAPI-04 kinetic model in hepatic lesions, to determine the potential role of kinetic parameters in the differentiation of hepatocellular carcinoma (HCC) from non-HCC lesions. Material and methods Time activity curves (TACs) were extracted from seven HCC lesions and five non-HCC lesions obtained from 68Ga-FAPI-04 dynamic positron emission tomography (PET) scans of eight patients. Three kinetic models were applied to the TACs, using image-derived hepatic artery and/or portal vein as input functions. The maximum standardized uptake value (SUVmax) was taken for the lesions, the hepatic artery, and for the portal veins—the mean SUV for all healthy regions. The optimum model was chosen after applying the Schwartz information criteria to the TACs, differences in model parameters between HCC, non-HCC lesions, and healthy tissue were evaluated with the ANOVA test. Results A reversible two-tissue compartment model using both the arterial as well as venous input function was most preferred and showed significant differences in the kinetic parameters VND, VT, and BPND between HCC, non-HCC lesions, and healthy regions (p < 0.01). Conclusion Several model parameters derived from a two-tissue compartment kinetic model with two image-derived input function from vein and aorta and using SUVmax allow a differentiation between HCC and non-HCC lesions, obtained from dynamically performed PET scans using FAPI.

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Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

Frontiers in Oncology ◽

10.3389/fonc.2021.735257 ◽

2021 ◽

Vol 11 ◽

Author(s):

Timothée Zaragori ◽

Matthieu Doyen ◽

Fabien Rech ◽

Marie Blonski ◽

Luc Taillandier ◽

...

Keyword(s):

Kinetic Model ◽

Dynamic Analysis ◽

Pet Imaging ◽

Compartmental Model ◽

Univariate Analysis ◽

The Other ◽

Newly Diagnosed ◽

Reference Region ◽

Time Activity ◽

Idh Mutations

PurposeDynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[18F]fluoro-l-DOPA (18F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time–activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models.MethodsThirty-seven tumour time–activity curves from 18F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3–79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for 18F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis.ResultsThe SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered.ConclusionsThe SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in newly diagnosed gliomas.

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