Thomas E Yankeelov

发表

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics

Guillermo Lorenzo, Todd A. Oliver, Chengyue Wu, 2020, IEEE Transactions on Medical Imaging.

Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details.

David A Hormuth, Thomas E Yankeelov, Michael I Miga, 2018, Methods in molecular biology.

Three-dimensional Image-based Mechanical Modeling for Predicting the Response of Breast Cancer to Neoadjuvant Therapy.

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2017, Computer methods in applied mechanics and engineering.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Xia Li, Thomas E Yankeelov, Hakmook Kang, 2017, Journal of medical imaging.

Quantitative effects of using compressed sensing in dynamic contrast enhanced MRI

Xia Li, David S Smith, Thomas E Yankeelov, 2011, Physics in medicine and biology.

Modeling tumor growth and treatment response based on quantitative imaging data.

Thomas E Yankeelov, John C Gore, Nkiruka C Atuegwu, 2010, Integrative biology : quantitative biosciences from nano to macro.

Automatic nonrigid registration of whole body CT mice images.

Xia Li, Benoit M Dawant, Thomas E Yankeelov, 2008, Medical physics.

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

Xia Li, Jack Virostko, Wei Huang, 2018, Journal of medical imaging.

Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data.

Angela M. Jarrett, David A Hormuth, Thomas E Yankeelov, 2019, JCO clinical cancer informatics.

A fully coupled space-time multiscale modeling framework for predicting tumor growth.

Yusheng Feng, Thomas E Yankeelov, Mohammad Mamunur Rahman, 2017, Computer methods in applied mechanics and engineering.

The Quantitative Imaging Network: A Decade of Achievement

Thomas E Yankeelov, 2019, Tomography.

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials

Wei Huang, Thomas E Yankeelov, Bachir Taouli, 2016, Magnetic resonance in medicine.

Statistical comparison of dynamic contrast‐enhanced MRI pharmacokinetic models in human breast cancer

Xia Li, Thomas E Yankeelov, John C Gore, 2012, Magnetic resonance in medicine.

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis

Sarah Avery, Thomas E Yankeelov, Hakmook Kang, 2017, Journal of medical imaging.

Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer.

Thomas E Yankeelov, T. Yankeelov, 2012, ISRN biomathematics.

The integration of quantitative multi-modality imaging data into mathematical models of tumors

Thomas E Yankeelov, John C Gore, Nkiruka C Atuegwu, 2010, Physics in medicine and biology.

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2015, Journal of medical imaging.

Incorporating the effects of transcytolemmal water exchange in a reference region model for DCE‐MRI analysis: Theory, simulations, and experimental results

Thomas E Yankeelov, John C Gore, Jeffrey J Luci, 2008, Magnetic resonance in medicine.

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

Xia Li, Thomas E Yankeelov, Hakmook Kang, 2014, Translational oncology.

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.

Thomas E Yankeelov, Lori R Arlinghaus, Vandana G Abramson, 2013, Translational oncology.

A mechanically coupled reaction–diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2013, Physics in medicine and biology.

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response.

Benoit M Dawant, Thomas E Yankeelov, John C Gore, 2009, Magnetic resonance imaging.

Clinically Relevant Modeling of Tumor Growth and Treatment Response

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2013, Science Translational Medicine.

Magnetic resonance in the era of molecular imaging of cancer.

Thomas E Yankeelov, John C Gore, H Charles Manning, 2011, Magnetic resonance imaging.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.

Ron Kikinis, Xia Li, Andriy Fedorov, 2014, Translational oncology.

Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

David A Hormuth, Thomas E Yankeelov, Xinzeng Feng, 2018, Physics in medicine and biology.

Comparison of dual-echo DSC-MRI- and DCE-MRI-derived contrast agent kinetic parameters.

Thomas E Yankeelov, John C Gore, C Chad Quarles, 2012, Magnetic resonance imaging.

Clinical utility of quantitative imaging.

Thomas E Yankeelov, Richard G Abramson, Leon Lenchik, 2015, Academic radiology.

The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer.

Thomas E Yankeelov, John C Gore, Lori R Arlinghaus, 2011, Seminars in oncology.

Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer.

Thomas E Yankeelov, Gregory D Ayers, Mary E Loveless, 2014, Magnetic resonance imaging.

New insights into tumor microstructure using temporal diffusion spectroscopy.

Thomas E Yankeelov, John C Gore, Chad Quarles, 2008, Cancer research.

Assessing the reproducibility of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer

Thomas E Yankeelov, Gregory D Ayers, Mary E Loveless, 2013, Magnetic resonance in medicine.

Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms.

Benoit M Dawant, Thomas E Yankeelov, John C Gore, 2010, Medical physics.

Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms.

Benoit M Dawant, Thomas E Yankeelov, John C Gore, 2010, Medical physics.

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning.

Thomas E Yankeelov, Subramani Mani, Yukun Chen, 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

A Predictive Mathematical Modeling Approach for the Study of Doxorubicin Treatment in Triple Negative Breast Cancer

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2017, Scientific Reports.

A hybrid model of tumor growth and angiogenesis: In silico experiments

Amy Brock, Thomas E Yankeelov, Ryan T. Woodall, 2020, PloS one.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer

Thomas E Yankeelov, John C Gore, Lori R Arlinghaus, 2011, Physics in medicine and biology.

Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling

Angela M. Jarrett, David A Hormuth, Thomas E Yankeelov, 2020, Radiation Oncology.

Incorporating contrast agent diffusion into the analysis of DCE‐MRI data

Thomas E Yankeelov, T. Yankeelov, M. Lepage, 2007, Magnetic resonance in medicine.

Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer.

David A Hormuth, Thomas E Yankeelov, Michael I Miga, 2017, International journal of radiation oncology, biology, physics.

Mathematical models of tumor cell proliferation: A review of the literature

Angela M. Jarrett, Amy Brock, David A Hormuth, 2018, Expert review of anticancer therapy.

Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI

Angela M. Jarrett, David A Hormuth, Thomas E Yankeelov, 2019, Annals of Biomedical Engineering.

Variation of the relaxographic “shutter‐speed” for transcytolemmal water exchange affects the CR bolus‐tracking curve shape

Thomas E Yankeelov, Xin Li, William D Rooney, 2003, Magnetic resonance in medicine.

Evidence of multiexponential T2 in rat glioblastoma

Thomas E Yankeelov, John C Gore, Mark D Does, 2009, NMR in biomedicine.

Multiparametric Magnetic Resonance Imaging for Predicting Pathological Response After the First Cycle of Neoadjuvant Chemotherapy in Breast Cancer

Thomas E Yankeelov, Hakmook Kang, Lori R Arlinghaus, 2015, Investigative radiology.

Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer.

Angela M. Jarrett, Thomas E Yankeelov, Anna G Sorace, 2018, Mathematical medicine and biology : a journal of the IMA.

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation.

Andriy Fedorov, Thomas E Yankeelov, Gregory D Ayers, 2014, Magnetic resonance imaging.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE‐MRI: Results from a multicenter phantom study

Wei Huang, Thomas E Yankeelov, Jayashree Kalpathy-Cramer, 2018, Magnetic resonance in medicine.

Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model.

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2015, Cancer research.

A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats.

David A Hormuth, Thomas E Yankeelov, Mark D Does, 2014, Magnetic resonance imaging.

Comparisons of the efficacy of a Jak1/2 inhibitor (AZD1480) with a VEGF signaling inhibitor (cediranib) and sham treatments in mouse tumors using DCE-MRI, DW-MRI, and histology.

Thomas E Yankeelov, John C Gore, Mary E Loveless, 2012, Neoplasia.

Co-registration of multi-modality imaging allows for comprehensive analysis of tumor-induced bone disease.

Thomas E Yankeelov, John C Gore, Richard M Caprioli, 2014, Bone.

Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

Thomas E Yankeelov, Anna G Sorace, Stephanie L Barnes, 2020, Cancers.

The 2019 mathematical oncology roadmap

Michael Hinczewski, Peter Jeavons, George Biros, 2019, Physical biology.

Quantitative analysis of vascular properties derived from ultrafast DCE‐MRI to discriminate malignant and benign breast tumors

Chengyue Wu, David A Hormuth, Federico Pineda, 2018, Magnetic resonance in medicine.

Correlation of tumor characteristics derived from DCE‐MRI and DW‐MRI with histology in murine models of breast cancer

Thomas E Yankeelov, Mary E Loveless, Anna G Sorace, 2015, NMR in biomedicine.

MR Imaging Biomarkers in Oncology Clinical Trials.

Thomas E Yankeelov, Jared A Weis, Lori R Arlinghaus, 2016, Magnetic resonance imaging clinics of North America.

Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy.

Thomas E Yankeelov, Michael I Miga, Jared A Weis, 2012, Breast cancer.

A Method for Assessing the Microvasculature in a Murine Tumor Model Using Contrast‐Enhanced Ultrasonography

Thomas E Yankeelov, John C Gore, Mary E Loveless, 2008, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples.

Thomas E Yankeelov, John C Gore, J. Gore, 2007, Current medical imaging reviews.

Simultaneous measurement of arterial input function and tumor pharmacokinetics in mice by dynamic contrast enhanced imaging: Effects of transcytolemmal water exchange

Rong Zhou, Thomas E Yankeelov, Stephen Pickup, 2004, Magnetic resonance in medicine.

Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging

Benoit M Dawant, Thomas E Yankeelov, Tuhin K Sinha, 2008, Nature Methods.

Shutter‐speed analysis of contrast reagent bolus‐tracking data: Preliminary observations in benign and malignant breast disease

Wei Huang, Thomas E Yankeelov, Xin Li, 2005, Magnetic resonance in medicine.

Integration of diffusion‐weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy

Thomas E Yankeelov, John C Gore, Lori R Arlinghaus, 2011, Magnetic resonance in medicine.

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results

Thomas E Yankeelov, Mary E Loveless, Stephanie L Barnes, 2013, Physics in medicine and biology.

Motion correction in diffusion‐weighted MRI of the breast at 3T

Thomas E Yankeelov, John C Gore, Lori R Arlinghaus, 2011, Journal of magnetic resonance imaging : JMRI.

Methods and challenges in quantitative imaging biomarker development.

Thomas E Yankeelov, Richard G Abramson, Leon Lenchik, 2015, Academic radiology.

Amide proton transfer imaging of the breast at 3 T: Establishing reproducibility and possible feasibility assessing chemotherapy response

Thomas E Yankeelov, Lori R Arlinghaus, Gregory D Ayers, 2013, Magnetic resonance in medicine.

DCE‐MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings

Xia Li, Thomas E Yankeelov, Lori R Arlinghaus, 2014, Magnetic resonance in medicine.

Letter to cancer center directors: Progress in quantitative imaging as a means to predict and/or measure tumor response in cancer therapy trials.

Paul E Kinahan, Thomas E Yankeelov, John M Buatti, 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.

Thomas E Yankeelov, Pei-Fang Su, Lori R Arlinghaus, 2013, Magnetic resonance imaging.

A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth

David A Hormuth, Thomas E Yankeelov, Michael I Miga, 2017, Journal of The Royal Society Interface.

DCE‐ and DW‐MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer

Thomas E Yankeelov, Hakmook Kang, Anna G Sorace, 2017, NMR in biomedicine.

Simultaneous PET-MRI in oncology: a solution looking for a problem?

Ciprian Catana, Thomas E Yankeelov, John C Gore, 2012, Magnetic resonance imaging.

A multi-state model of chemoresistance to characterize phenotypic dynamics in breast cancer

Kaitlyn E. Johnson, Amy Brock, Thomas E Yankeelov, 2017, Scientific Reports.

Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results.

Thomas E Yankeelov, John C Gore, Ingrid Meszoely, 2007, Magnetic resonance imaging.

Toward a science of tumor forecasting for clinical oncology.

Thomas E Yankeelov, Vito Quaranta, T. Yankeelov, 2015, Cancer research.

Temporal sampling requirements for reference region modeling of DCE‐MRI data in human breast cancer

Thomas E Yankeelov, John C Gore, E Brian Welch, 2009, Journal of magnetic resonance imaging : JMRI.

Comparison of a reference region model with direct measurement of an AIF in the analysis of DCE‐MRI data

Thomas E Yankeelov, John C Gore, Ruth C Wilkins, 2007, Magnetic resonance in medicine.