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Showing 1 to 12 of 25 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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Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Translational oncology

Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE.
PMID: 24772203
Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb.

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve...

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Li X, Kang H, Arlinghaus LR, et al. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014;7(1):14-22doi: 10.1593/tlo.13748.
Li, X., Kang, H., Arlinghaus, L. R., Abramson, R. G., Chakravarthy, A. B., Abramson, V. G., Farley, J., Sanders, M., & Yankeelov, T. E. (2014). Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational oncology, 7(1), 14-22. https://doi.org/10.1593/tlo.13748
Li, Xia, et al. "Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer." Translational oncology vol. 7,1 (2014): 14-22. doi: https://doi.org/10.1593/tlo.13748
Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb. PMID: 24772203; PMCID: PMC3998687.
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Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting.

Journal of magnetic resonance imaging : JMRI

Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J.
PMID: 29570895
J Magn Reson Imaging. 2018 Mar 23; doi: 10.1002/jmri.26011. Epub 2018 Mar 23.

BACKGROUND: Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.PURPOSE/HYPOTHESIS: To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and TSTUDY...

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Sorace AG, Wu C, Barnes SL, et al. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging. 2018;doi: 10.1002/jmri.26011.
Sorace, A. G., Wu, C., Barnes, S. L., Jarrett, A. M., Avery, S., Patt, D., Goodgame, B., Luci, J. J., Kang, H., Abramson, R. G., Yankeelov, T. E., & Virostko, J. (2018). Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.26011
Sorace, Anna G, et al. "Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting." Journal of magnetic resonance imaging : JMRI vol. (2018). doi: https://doi.org/10.1002/jmri.26011
Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging. 2018 Mar 23; doi: 10.1002/jmri.26011. Epub 2018 Mar 23. PMID: 29570895; PMCID: PMC6151298.
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Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer.

Translational oncology

McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE.
PMID: 29674173
Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16.

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those...

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McKenna MT, Weis JA, Brock A, et al. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018;11(3):732-742doi: 10.1016/j.tranon.2018.03.009.
McKenna, M. T., Weis, J. A., Brock, A., Quaranta, V., & Yankeelov, T. E. (2018). Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Translational oncology, 11(3), 732-742. https://doi.org/10.1016/j.tranon.2018.03.009
McKenna, Matthew T, et al. "Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer." Translational oncology vol. 11,3 (2018): 732-742. doi: https://doi.org/10.1016/j.tranon.2018.03.009
McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16. PMID: 29674173; PMCID: PMC6056758.
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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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Math, magnets, and medicine: enabling personalized oncology.

Expert review of precision medicine and drug development

Hormuth DA, Jarrett AM, Lorenzo G, Lima EABF, Wu C, Chung C, Patt D, Yankeelov TE.
PMID: 34027102
Expert Rev Precis Med Drug Dev. 2021;6(2):79-81. doi: 10.1080/23808993.2021.1878023. Epub 2021 Jan 27.

No abstract available.

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Hormuth DA, Jarrett AM, Lorenzo G, et al. Math, magnets, and medicine: enabling personalized oncology. Expert Rev Precis Med Drug Dev. 2021;6(2):79-81doi: 10.1080/23808993.2021.1878023.
Hormuth, D. A., Jarrett, A. M., Lorenzo, G., Lima, E. A. B. F., Wu, C., Chung, C., Patt, D., & Yankeelov, T. E. (2021). Math, magnets, and medicine: enabling personalized oncology. Expert review of precision medicine and drug development, 6(2), 79-81. https://doi.org/10.1080/23808993.2021.1878023
Hormuth, David A, et al. "Math, magnets, and medicine: enabling personalized oncology." Expert review of precision medicine and drug development vol. 6,2 (2021): 79-81. doi: https://doi.org/10.1080/23808993.2021.1878023
Hormuth DA, Jarrett AM, Lorenzo G, Lima EABF, Wu C, Chung C, Patt D, Yankeelov TE. Math, magnets, and medicine: enabling personalized oncology. Expert Rev Precis Med Drug Dev. 2021;6(2):79-81. doi: 10.1080/23808993.2021.1878023. Epub 2021 Jan 27. PMID: 34027102; PMCID: PMC8133535.
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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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A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH.

Mathematical models & methods in applied sciences : M3AS

Rocha HL, Almeida RC, Lima EABF, Resende ACM, Oden JT, Yankeelov TE.
PMID: 29353950
Math Models Methods Appl Sci. 2018 Jan;28(1):61-93. doi: 10.1142/S0218202518500021. Epub 2017 Nov 24.

Cancer results from a complex interplay of different biological, chemical, and physical phenomena that span a wide range of time and length scales. Computational modeling may help to unfold the role of multiple evolving factors that exist and interact...

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Rocha HL, Almeida RC, Lima EABF, et al. A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH. Math Models Methods Appl Sci. 2017;28(1):61-93doi: 10.1142/S0218202518500021.
Rocha, H. L., Almeida, R. C., Lima, E. A. B. F., Resende, A. C. M., Oden, J. T., & Yankeelov, T. E. (2018). A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH. Mathematical models & methods in applied sciences : M3AS, 28(1), 61-93. https://doi.org/10.1142/S0218202518500021
Rocha, H L, et al. "A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH." Mathematical models & methods in applied sciences : M3AS vol. 28,1 (2018): 61-93. doi: https://doi.org/10.1142/S0218202518500021
Rocha HL, Almeida RC, Lima EABF, Resende ACM, Oden JT, Yankeelov TE. A HYBRID THREE-SCALE MODEL OF TUMOR GROWTH. Math Models Methods Appl Sci. 2018 Jan;28(1):61-93. doi: 10.1142/S0218202518500021. Epub 2017 Nov 24. PMID: 29353950; PMCID: PMC5773147.
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Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

Computer methods in applied mechanics and engineering

Weis JA, Miga MI, Yankeelov TE.
PMID: 28042181
Comput Methods Appl Mech Eng. 2017 Feb 01;314:494-512. doi: 10.1016/j.cma.2016.08.024. Epub 2016 Sep 01.

The use of quantitative medical imaging data to initialize and constrain mechanistic mathematical models of tumor growth has demonstrated a compelling strategy for predicting therapeutic response. More specifically, we have demonstrated a data-driven framework for prediction of residual tumor...

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Weis JA, Miga MI, Yankeelov TE. Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. Comput Methods Appl Mech Eng. 2016;314:494-512doi: 10.1016/j.cma.2016.08.024.
Weis, J. A., Miga, M. I., & Yankeelov, T. E. (2017). Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. Computer methods in applied mechanics and engineering, 314494-512. https://doi.org/10.1016/j.cma.2016.08.024
Weis, Jared A, et al. "Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy." Computer methods in applied mechanics and engineering vol. 314 (2017): 494-512. doi: https://doi.org/10.1016/j.cma.2016.08.024
Weis JA, Miga MI, Yankeelov TE. Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy. Comput Methods Appl Mech Eng. 2017 Feb 01;314:494-512. doi: 10.1016/j.cma.2016.08.024. Epub 2016 Sep 01. PMID: 28042181; PMCID: PMC5193147.
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Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy.

Translational oncology

Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE.
PMID: 23730404
Transl Oncol. 2013 Jun 01;6(3):256-64. doi: 10.1593/tlo.13130. Print 2013 Jun.

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) data of 28 patients were obtained pretreatment, after one cycle, and after completion of all cycles of neoadjuvant chemotherapy (NAC). For each patient at each time point, the tumor cell number...

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Atuegwu NC, Arlinghaus LR, Li X, et al. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy. Transl Oncol. 2013;6(3):256-64doi: 10.1593/tlo.13130.
Atuegwu, N. C., Arlinghaus, L. R., Li, X., Chakravarthy, A. B., Abramson, V. G., Sanders, M. E., & Yankeelov, T. E. (2013). Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy. Translational oncology, 6(3), 256-64. https://doi.org/10.1593/tlo.13130
Atuegwu, Nkiruka C, et al. "Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy." Translational oncology vol. 6,3 (2013): 256-64. doi: https://doi.org/10.1593/tlo.13130
Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy. Transl Oncol. 2013 Jun 01;6(3):256-64. doi: 10.1593/tlo.13130. Print 2013 Jun. PMID: 23730404; PMCID: PMC3660793.
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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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Showing 1 to 12 of 25 entries