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Sharon E. Ungersma - One of the best experts on this subject based on the ideXlab platform.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, H. Toni Jun, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K ^trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo , significant reductions in K ^trans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K ^trans consistent with published data in xenograft models. Conclusion The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K ^trans results consistent with literature values and suitable for compound studies.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, H. Toni Jun, Angela Coxon, Sharon E. UngersmaAbstract:Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and Ktrans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures: In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results: Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo, significant reductions in Ktrans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded Ktrans consistent with published data in xenograft models. Conclusion: The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield Ktrans results consistent with literature values and suitable for compound studies. © 2017, World Molecular Imaging Society.
Matthew D Silva - One of the best experts on this subject based on the ideXlab platform.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, H. Toni Jun, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K ^trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo , significant reductions in K ^trans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K ^trans consistent with published data in xenograft models. Conclusion The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K ^trans results consistent with literature values and suitable for compound studies.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, H. Toni Jun, Angela Coxon, Sharon E. UngersmaAbstract:Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and Ktrans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures: In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results: Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo, significant reductions in Ktrans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded Ktrans consistent with published data in xenograft models. Conclusion: The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield Ktrans results consistent with literature values and suitable for compound studies. © 2017, World Molecular Imaging Society.
Gabriël J. E. Rinkel - One of the best experts on this subject based on the ideXlab platform.
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Long-term outcome after aneurysmal subarachnoid hemorrhage—risks of Vascular events, death from Cancer and all-cause death
Journal of Neurology, 2014Co-Authors: Dennis J. Nieuwkamp, Arno Wilde, Marieke J. H. Wermer, Ale Algra, Gabriël J. E. RinkelAbstract:Smoking and hypertension are risk factors for aneurysmal subarachnoid hemorrhage (aSAH), but also for other cardioVascular diseases and Cancer. Few prospective data are available on the very long term risks of Vascular diseases and Vascular, Cancer-related and overall death after aSAH. We determined Vascular events and survival status in 1,765 patients with aSAH admitted to our center from 1985 to 2010. Cumulative risks were estimated with survival analysis. We compared risks of Vascular, Cancer-related and all-cause death with the general population with standardized mortality ratios (SMRs). Incidences of Vascular events and death were compared with those after TIA/minor stroke. Conditional on surviving 3 months after aSAH, the risk of death was 8.7 % (95 % CI 7.3–10.1) within 5 years, 17.9 % (16.1–19.9) within 10 years, 29.5 % (27.3–31.8) within 15 years, and 43.6 % (41.2–46.1) within 20 years after SAH. The SMR for all-cause death was 1.8 (1.6–2.1), for Vascular death 2.0 (95 % CI 1.6–2.5) and for Cancer-related death 1.2 (0.9–1.5; sensitivity analysis 1.4; 95 % CI 1.1–1.8). The increased SMR for all-cause death persevered up to 20 years after aSAH. Compared with TIA/minor stroke patients, the age- and sex-adjusted cumulative incidence on Vascular events was lower for aSAH patients [hazard ratio (HR) 0.48; 95 % CI 0.40–0.57); the HR for all-cause death was 0.96 (95 % CI 0.84–1.10). After aSAH, risks of Vascular events and death, and probably also that of Cancer-related death, are higher than in the general population. Although the long-term risk of Vascular events was lower in aSAH patients than in TIA/minor stroke patients, the risk of death was similar.
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Long-term outcome after aneurysmal subarachnoid hemorrhage-risks of Vascular events, death from Cancer and all-cause death.
Journal of Neurology, 2013Co-Authors: Dennis J. Nieuwkamp, Arno Wilde, Marieke J. H. Wermer, Ale Algra, Gabriël J. E. RinkelAbstract:Smoking and hypertension are risk factors for aneurysmal subarachnoid hemorrhage (aSAH), but also for other cardioVascular diseases and Cancer. Few prospective data are available on the very long term risks of Vascular diseases and Vascular, Cancer-related and overall death after aSAH. We determined Vascular events and survival status in 1,765 patients with aSAH admitted to our center from 1985 to 2010. Cumulative risks were estimated with survival analysis. We compared risks of Vascular, Cancer-related and all-cause death with the general population with standardized mortality ratios (SMRs). Incidences of Vascular events and death were compared with those after TIA/minor stroke. Conditional on surviving 3 months after aSAH, the risk of death was 8.7 % (95 % CI 7.3–10.1) within 5 years, 17.9 % (16.1–19.9) within 10 years, 29.5 % (27.3–31.8) within 15 years, and 43.6 % (41.2–46.1) within 20 years after SAH. The SMR for all-cause death was 1.8 (1.6–2.1), for Vascular death 2.0 (95 % CI 1.6–2.5) and for Cancer-related death 1.2 (0.9–1.5; sensitivity analysis 1.4; 95 % CI 1.1–1.8). The increased SMR for all-cause death persevered up to 20 years after aSAH. Compared with TIA/minor stroke patients, the age- and sex-adjusted cumulative incidence on Vascular events was lower for aSAH patients [hazard ratio (HR) 0.48; 95 % CI 0.40–0.57); the HR for all-cause death was 0.96 (95 % CI 0.84–1.10). After aSAH, risks of Vascular events and death, and probably also that of Cancer-related death, are higher than in the general population. Although the long-term risk of Vascular events was lower in aSAH patients than in TIA/minor stroke patients, the risk of death was similar.
Brittany Yerby - One of the best experts on this subject based on the ideXlab platform.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, H. Toni Jun, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K ^trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo , significant reductions in K ^trans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K ^trans consistent with published data in xenograft models. Conclusion The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K ^trans results consistent with literature values and suitable for compound studies.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, H. Toni Jun, Angela Coxon, Sharon E. UngersmaAbstract:Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and Ktrans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures: In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results: Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo, significant reductions in Ktrans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded Ktrans consistent with published data in xenograft models. Conclusion: The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield Ktrans results consistent with literature values and suitable for compound studies. © 2017, World Molecular Imaging Society.
Albert Gomez - One of the best experts on this subject based on the ideXlab platform.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, H. Toni Jun, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K ^trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo , significant reductions in K ^trans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded K ^trans consistent with published data in xenograft models. Conclusion The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield K ^trans results consistent with literature values and suitable for compound studies.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, Angela Coxon, Sharon E. UngersmaAbstract:Purpose Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and K trans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system.
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Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model
Molecular Imaging and Biology, 2017Co-Authors: Matthew D Silva, Brittany Yerby, Jodi Moriguchi, Albert Gomez, H. Toni Jun, Angela Coxon, Sharon E. UngersmaAbstract:Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is an accepted method to evaluate tumor perfusion and permeability and anti-Vascular Cancer therapies. However, there is no consensus on the Vascular input function estimation method, which is critical to kinetic modeling and Ktrans estimation. This work proposes a response-derived input function (RDIF) estimated from the response of the tumor, modeled as a linear, time-invariant (LTI) system. Procedures: In an LTI system, an unknown input can be estimated from the system response. If applied to DCE MRI, this method would eliminate need of distal image-derived inputs, model inputs, or reference regions. The RDIF method first determines each tumor pixel’s best-fit input function, and then combines the individual fits into a single input function for the entire tumor. The method was tested with simulations and a xenograft study with anti-Vascular drug treatment. Results: Simulations showed successful estimation of input function expected values and good performance in the presence of noise. In vivo, significant reductions in Ktrans and AUC occurred 2 days following anti-delta-like ligand 4 treatment. The in vivo study results yielded Ktrans consistent with published data in xenograft models. Conclusion: The RDIF method for DCE analysis offers an alternative, easy-to-implement method for estimating the input function in tumors. The method assumes that during the DCE experiment, the changes observed by MRI result solely from Vascular perfusion and permeability kinetics, and that information can be used to model the input function. Importantly, the method is demonstrated in a murine xenograft study to yield Ktrans results consistent with literature values and suitable for compound studies. © 2017, World Molecular Imaging Society.
