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Showing 1 to 12 of 13 entries
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Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer.

Journal of medical imaging (Bellingham, Wash.)

Weis JA, Flint KM, Sanchez V, Yankeelov TE, Miga MI.
PMID: 26158120
J Med Imaging (Bellingham). 2015 Jul;2(3):036001. doi: 10.1117/1.JMI.2.3.036001. Epub 2015 Jul 02.

Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography...

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Weis JA, Flint KM, Sanchez V, et al. Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. J Med Imaging (Bellingham). 2015;2(3):036001doi: 10.1117/1.JMI.2.3.036001.
Weis, J. A., Flint, K. M., Sanchez, V., Yankeelov, T. E., & Miga, M. I. (2015). Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. Journal of medical imaging (Bellingham, Wash.), 2(3), 036001. https://doi.org/10.1117/1.JMI.2.3.036001
Weis, Jared A, et al. "Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer." Journal of medical imaging (Bellingham, Wash.) vol. 2,3 (2015): 036001. doi: https://doi.org/10.1117/1.JMI.2.3.036001
Weis JA, Flint KM, Sanchez V, Yankeelov TE, Miga MI. Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. J Med Imaging (Bellingham). 2015 Jul;2(3):036001. doi: 10.1117/1.JMI.2.3.036001. Epub 2015 Jul 02. PMID: 26158120; PMCID: PMC4490672.
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Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

International journal of biomedical imaging

Smith DS, Gore JC, Yankeelov TE, Welch EB.
PMID: 22481908
Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01.

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI...

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Smith DS, Gore JC, Yankeelov TE, et al. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827doi: 10.1155/2012/864827.
Smith, D. S., Gore, J. C., Yankeelov, T. E., & Welch, E. B. (2012). Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. International journal of biomedical imaging, 2012864827. https://doi.org/10.1155/2012/864827
Smith, David S, et al. "Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods." International journal of biomedical imaging vol. 2012 (2012): 864827. doi: https://doi.org/10.1155/2012/864827
Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01. PMID: 22481908; PMCID: PMC3296267.
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Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.

Frontiers in physiology

McKenna MT, Weis JA, Quaranta V, Yankeelov TE.
PMID: 31178753
Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019.

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug...

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McKenna MT, Weis JA, Quaranta V, et al. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019;10:616doi: 10.3389/fphys.2019.00616.
McKenna, M. T., Weis, J. A., Quaranta, V., & Yankeelov, T. E. (2019). Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Frontiers in physiology, 10616. https://doi.org/10.3389/fphys.2019.00616
McKenna, Matthew T, et al. "Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric." Frontiers in physiology vol. 10 (2019): 616. doi: https://doi.org/10.3389/fphys.2019.00616
McKenna MT, Weis JA, Quaranta V, Yankeelov TE. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019. PMID: 31178753; PMCID: PMC6538812.
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Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data.

Computer methods in applied mechanics and engineering

Lima EABF, Oden JT, Wohlmuth B, Shahmoradi A, Hormuth DA, Yankeelov TE, Scarabosio L, Horger T.
PMID: 29269963
Comput Methods Appl Mech Eng. 2017 Dec 01;327:277-305. doi: 10.1016/j.cma.2017.08.009. Epub 2017 Aug 18.

The use of mathematical and computational models for reliable predictions of tumor growth and decline in living organisms is one of the foremost challenges in modern predictive science, as it must cope with uncertainties in observational data, model selection,...

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Lima EABF, Oden JT, Wohlmuth B, et al. Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data. Comput Methods Appl Mech Eng. 2017;327:277-305doi: 10.1016/j.cma.2017.08.009.
Lima, E. A. B. F., Oden, J. T., Wohlmuth, B., Shahmoradi, A., Hormuth, D. A., Yankeelov, T. E., Scarabosio, L., & Horger, T. (2017). Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data. Computer methods in applied mechanics and engineering, 327277-305. https://doi.org/10.1016/j.cma.2017.08.009
Lima, E A B F, et al. "Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data." Computer methods in applied mechanics and engineering vol. 327 (2017): 277-305. doi: https://doi.org/10.1016/j.cma.2017.08.009
Lima EABF, Oden JT, Wohlmuth B, Shahmoradi A, Hormuth DA, Yankeelov TE, Scarabosio L, Horger T. Selection and Validation of Predictive Models of Radiation Effects on Tumor Growth Based on Noninvasive Imaging Data. Comput Methods Appl Mech Eng. 2017 Dec 01;327:277-305. doi: 10.1016/j.cma.2017.08.009. Epub 2017 Aug 18. PMID: 29269963; PMCID: PMC5734134.
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Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer.

ISRN biomathematics

Yankeelov TE.
PMID: 23914302
ISRN Biomath. 2012;2012. doi: 10.5402/2012/287394.

While there is a mature literature on biomathematical and biophysical modeling in cancer, many of the existing approaches are not of clinical utility, as they require input data that are extremely difficult to obtain in an intact organism, and/or...

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Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomath. 2012;2012doi: 10.5402/2012/287394.
Yankeelov, T. E. (2012). Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN biomathematics, 2012. https://doi.org/10.5402/2012/287394
Yankeelov, Thomas E. "Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer." ISRN biomathematics vol. 2012 (2012). doi: https://doi.org/10.5402/2012/287394
Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomath. 2012;2012. doi: 10.5402/2012/287394. PMID: 23914302; PMCID: PMC3729405.
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Toward a science of tumor forecasting for clinical oncology.

Cancer research

Yankeelov TE, Quaranta V, Evans KJ, Rericha EC.
PMID: 25592148
Cancer Res. 2015 Mar 15;75(6):918-23. doi: 10.1158/0008-5472.CAN-14-2233. Epub 2015 Jan 15.

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response...

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Yankeelov TE, Quaranta V, Evans KJ, et al. Toward a science of tumor forecasting for clinical oncology. Cancer Res. 2015;75(6):918-23doi: 10.1158/0008-5472.CAN-14-2233.
Yankeelov, T. E., Quaranta, V., Evans, K. J., & Rericha, E. C. (2015). Toward a science of tumor forecasting for clinical oncology. Cancer research, 75(6), 918-23. https://doi.org/10.1158/0008-5472.CAN-14-2233
Yankeelov, Thomas E, et al. "Toward a science of tumor forecasting for clinical oncology." Cancer research vol. 75,6 (2015): 918-23. doi: https://doi.org/10.1158/0008-5472.CAN-14-2233
Yankeelov TE, Quaranta V, Evans KJ, Rericha EC. Toward a science of tumor forecasting for clinical oncology. Cancer Res. 2015 Mar 15;75(6):918-23. doi: 10.1158/0008-5472.CAN-14-2233. Epub 2015 Jan 15. PMID: 25592148; PMCID: PMC4359948.
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Selection, calibration, and validation of models of tumor growth.

Mathematical models & methods in applied sciences : M3AS

Lima EABF, Oden JT, Hormuth DA, Yankeelov TE, Almeida RC.
PMID: 28827890
Math Models Methods Appl Sci. 2016 Nov;26(12):2341-2368. doi: 10.1142/S021820251650055X. Epub 2016 Oct 03.

This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in...

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Lima EABF, Oden JT, Hormuth DA, et al. Selection, calibration, and validation of models of tumor growth. Math Models Methods Appl Sci. 2016;26(12):2341-2368doi: 10.1142/S021820251650055X.
Lima, E. A. B. F., Oden, J. T., Hormuth, D. A., Yankeelov, T. E., & Almeida, R. C. (2016). Selection, calibration, and validation of models of tumor growth. Mathematical models & methods in applied sciences : M3AS, 26(12), 2341-2368. https://doi.org/10.1142/S021820251650055X
Lima, E A B F, et al. "Selection, calibration, and validation of models of tumor growth." Mathematical models & methods in applied sciences : M3AS vol. 26,12 (2016): 2341-2368. doi: https://doi.org/10.1142/S021820251650055X
Lima EABF, Oden JT, Hormuth DA, Yankeelov TE, Almeida RC. Selection, calibration, and validation of models of tumor growth. Math Models Methods Appl Sci. 2016 Nov;26(12):2341-2368. doi: 10.1142/S021820251650055X. Epub 2016 Oct 03. PMID: 28827890; PMCID: PMC5560997.
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Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations.

International journal of biomedical imaging

Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE.
PMID: 24222761
Int J Biomed Imaging. 2013;2013:576470. doi: 10.1155/2013/576470. Epub 2013 Oct 03.

We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue...

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Fluckiger JU, Li X, Whisenant JG, et al. Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. Int J Biomed Imaging. 2013;2013:576470doi: 10.1155/2013/576470.
Fluckiger, J. U., Li, X., Whisenant, J. G., Peterson, T. E., Gore, J. C., & Yankeelov, T. E. (2013). Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. International journal of biomedical imaging, 2013576470. https://doi.org/10.1155/2013/576470
Fluckiger, Jacob U, et al. "Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations." International journal of biomedical imaging vol. 2013 (2013): 576470. doi: https://doi.org/10.1155/2013/576470
Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE. Using dynamic contrast-enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. Int J Biomed Imaging. 2013;2013:576470. doi: 10.1155/2013/576470. Epub 2013 Oct 03. PMID: 24222761; PMCID: PMC3814089.
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Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging.

IEEE transactions on bio-medical engineering

Tunc B, Hormuth D, Biros G, Yankeelov TE.
PMID: 34061731
IEEE Trans Biomed Eng. 2021 Dec;68(12):3713-3724. doi: 10.1109/TBME.2021.3085523. Epub 2021 Nov 19.

It is well-known that expanding glioblastomas typically induce significant deformations of the surrounding parenchyma (i.e., the so-called "mass effect"). In this study, we evaluate the performance of three mathematical models of tumor growth: 1) a reaction-diffusion-advection model which accounts...

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Tunc B, Hormuth D, Biros G, et al. Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging. IEEE Trans Biomed Eng. 2021;68(12):3713-3724doi: 10.1109/TBME.2021.3085523.
Tunc, B., Hormuth, D., Biros, G., & Yankeelov, T. E. (2021). Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging. IEEE transactions on bio-medical engineering, 68(12), 3713-3724. https://doi.org/10.1109/TBME.2021.3085523
Tunc, Birkan, et al. "Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging." IEEE transactions on bio-medical engineering vol. 68,12 (2021): 3713-3724. doi: https://doi.org/10.1109/TBME.2021.3085523
Tunc B, Hormuth D, Biros G, Yankeelov TE. Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging. IEEE Trans Biomed Eng. 2021 Dec;68(12):3713-3724. doi: 10.1109/TBME.2021.3085523. Epub 2021 Nov 19. PMID: 34061731; PMCID: PMC8604780.
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Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts.

Translational oncology

Whisenant JG, Sorace AG, McIntyre JO, Kang H, Sánchez V, Loveless ME, Yankeelov TE.
PMID: 25500087
Transl Oncol. 2014 Dec;7(6):768-79. doi: 10.1016/j.tranon.2014.09.011.

We report longitudinal diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast enhanced (DCE)-MRI (7 T) studies designed to identify functional changes, prior to volume changes, in trastuzumab-sensitive and resistant HER2+ breast cancer xenografts. Athymic mice (N = 33) were...

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Whisenant JG, Sorace AG, McIntyre JO, et al. Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts. Transl Oncol. 2014;7(6):768-79doi: 10.1016/j.tranon.2014.09.011.
Whisenant, J. G., Sorace, A. G., McIntyre, J. O., Kang, H., Sánchez, V., Loveless, M. E., & Yankeelov, T. E. (2014). Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts. Translational oncology, 7(6), 768-79. https://doi.org/10.1016/j.tranon.2014.09.011
Whisenant, Jennifer G, et al. "Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts." Translational oncology vol. 7,6 (2014): 768-79. doi: https://doi.org/10.1016/j.tranon.2014.09.011
Whisenant JG, Sorace AG, McIntyre JO, Kang H, Sánchez V, Loveless ME, Yankeelov TE. Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts. Transl Oncol. 2014 Dec;7(6):768-79. doi: 10.1016/j.tranon.2014.09.011. PMID: 25500087; PMCID: PMC4311041.
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Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

International journal of biomedical imaging

Smith DS, Gore JC, Yankeelov TE, Welch EB.
PMID: 22481908
Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01.

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI...

Cite
Smith DS, Gore JC, Yankeelov TE, et al. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827doi: 10.1155/2012/864827.
Smith, D. S., Gore, J. C., Yankeelov, T. E., & Welch, E. B. (2012). Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. International journal of biomedical imaging, 2012864827. https://doi.org/10.1155/2012/864827
Smith, David S, et al. "Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods." International journal of biomedical imaging vol. 2012 (2012): 864827. doi: https://doi.org/10.1155/2012/864827
Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01. PMID: 22481908; PMCID: PMC3296267.
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Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation.

Pharmaceutics

Barnes SL, Whisenant JG, Loveless ME, Yankeelov TE.
PMID: 23105959
Pharmaceutics. 2012;4(3):442-78. doi: 10.3390/pharmaceutics4030442.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and...

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Barnes SL, Whisenant JG, Loveless ME, et al. Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics. 2012;4(3):442-78doi: 10.3390/pharmaceutics4030442.
Barnes, S. L., Whisenant, J. G., Loveless, M. E., & Yankeelov, T. E. (2012). Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics, 4(3), 442-78. https://doi.org/10.3390/pharmaceutics4030442
Barnes, Stephanie L, et al. "Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation." Pharmaceutics vol. 4,3 (2012): 442-78. doi: https://doi.org/10.3390/pharmaceutics4030442
Barnes SL, Whisenant JG, Loveless ME, Yankeelov TE. Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics. 2012;4(3):442-78. doi: 10.3390/pharmaceutics4030442. PMID: 23105959; PMCID: PMC3480221.
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Showing 1 to 12 of 13 entries