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Thomas E. Yankeelov
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2020 – today
- 2024
- [j24]Chengyue Wu
, David A. Hormuth
Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma. Comput. Biol. Medicine 179: 108889 (2024) - [j23]Orhun O. Davarci
Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States. Eng. Comput. 40(2): 813-837 (2024) - [j22]Guillermo Lorenzo
A global sensitivity analysis of a mechanistic model of neoadjuvant chemotherapy for triple negative breast cancer constrained by in vitro and in vivo imaging data. Eng. Comput. 40(3): 1469-1499 (2024) - [j21]Reinhard C. Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L. Fonseca, James A. Glazier, Tomás Helikar, Marti Jett-Tilton, Denise Kirschner, Paul Macklin, Borna Mehrad, Beth Moore, Virginia Pasour, Ilya Shmulevich, Amber Smith, Isabel Voigt
Toward mechanistic medical digital twins: some use cases in immunology. Frontiers Digit. Health 6 (2024) - [j20]Chase Christenson, Chengyue Wu, David A. Hormuth, Casey E. Stowers, Megan LaMonica, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov:
Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition. J. Comput. Sci. 82: 102400 (2024) - 2023
- [j19]Anirban Chaudhuri, Graham Pash, David A. Hormuth, Guillermo Lorenzo, Michael G. Kapteyn, Chengyue Wu
Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas. Frontiers Artif. Intell. 6 (2023) - [j18]Caleb M. Phillips, Ernesto A. B. F. Lima
Assessing the identifiability of model selection frameworks for the prediction of patient outcomes in the clinical breast cancer setting. J. Comput. Sci. 69: 102006 (2023) - [j17]Caleb M. Phillips
Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis. PLoS Comput. Biol. 19(1) (2023) - [j16]Baoshan Liang
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth. IEEE Trans. Medical Imaging 42(10): 2865-2875 (2023) - [c11]Chengyue Wu
Optimized Patient-Specific Catheter Placement for Convection-Enhanced Nanoparticle Delivery in Recurrent Glioblastoma. SC Workshops 2023: 119-120 - [i7]Anirban Chaudhuri, Graham Pash, David A. Hormuth II, Guillermo Lorenzo, Michael G. Kapteyn, Chengyue Wu
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas. CoRR abs/2308.12429 (2023) - [i6]Guillermo Lorenzo, Syed Rakin Ahmed
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data. CoRR abs/2308.14925 (2023) - [i5]Guillermo Lorenzo, Jon S. Heiselman, Michael A. Liss, Michael I. Miga, Héctor Gómez
Patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model. CoRR abs/2310.00060 (2023) - 2022
- [j15]Grant R. Howard
Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules. PLoS Comput. Biol. 18(3) (2022) - [j14]Chengyue Wu
Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics. IEEE Trans. Biomed. Eng. 69(11): 3334-3344 (2022) - [i4]Baoshan Liang, Jingye Tan, Luke Lozenski, David A. Hormuth II, Thomas E. Yankeelov, Umberto Villa, Danial Faghihi:
Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth. CoRR abs/2209.12089 (2022) - [i3]Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Vito Quaranta, Darren R. Tyson, Thomas E. Yankeelov:
Identifying mechanisms driving the early response of triple negative breast cancer patients to neoadjuvant chemotherapy using a mechanistic model integrating in vitro and in vivo imaging data. CoRR abs/2212.04270 (2022) - 2021
- [j13]Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas J. R. Hughes, Alessia Patton, Alessandro Reali, Thomas E. Yankeelov, Alessandro Veneziani:
Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion. Appl. Math. Lett. 111: 106617 (2021) - [j12]Mirae Kim
Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics. Bioinform. 38(1): 243-249 (2021) - [j11]Chengyue Wu
An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom. Medical Image Anal. 73: 102186 (2021) - [j10]Ernesto A. B. F. Lima
Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth. PLoS Comput. Biol. 17(11) (2021) - [j9]Birkan Tunç
Modeling of Glioma Growth With Mass Effect by Longitudinal Magnetic Resonance Imaging. IEEE Trans. Biomed. Eng. 68(12): 3713-3724 (2021) - [i2]Guillermo Lorenzo, David A. Hormuth II, Angela M. Jarrett, Ernesto A. B. F. Lima, Shashank Subramanian, George Biros, J. Tinsley Oden, Thomas J. R. Hughes, Thomas E. Yankeelov:
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization. CoRR abs/2102.12602 (2021) - 2020
- [j8]Chengyue Wu
Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics. IEEE Trans. Medical Imaging 39(9): 2760-2771 (2020) - [i1]Alex Viguerie, Guillermo Lorenzo, Ferdinando Auricchio, Davide Baroli, Thomas J. R. Hughes, Alessia Patton, Alessandro Reali, Thomas E. Yankeelov, Alessandro Veneziani:
Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion. CoRR abs/2005.05320 (2020)
2010 – 2019
- 2017
- [j7]Dong Wang
Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. Int. J. Biomed. Imaging 2017: 7835749:1-7835749:11 (2017) - 2016
- [c10]Joseph B. DeGrandchamp
Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data. Biomedical Applications in Molecular, Structural, and Functional Imaging 2016: 978811 - 2015
- [j6]Rebekah H. Conley
Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI. Int. J. Comput. Assist. Radiol. Surg. 10(12): 1985-1996 (2015) - [c9]Jared A. Weis
Development of a diaphragmatic motion-based elastography framework for assessment of liver stiffness. Biomedical Applications in Molecular, Structural, and Functional Imaging 2015: 941703 - [c8]Katelyn M. Flint, Jared A. Weis
Voxel-level reproducibility assessment of modality independent elastography in a pre-clinical murine model. Biomedical Applications in Molecular, Structural, and Functional Imaging 2015: 941704 - 2014
- [c7]Stephanie L. Barnes, Jennifer G. Whisenant, Xia Li, Thomas E. Yankeelov
Techniques and applications of dynamic contrast enhanced magnetic resonance imaging in cancer. EMBC 2014: 4264-4267 - [c6]Rebekah H. Conley, Ingrid M. Meszoely, Thomas S. Pheiffer, Jared A. Weis
Image to physical space registration of supine breast MRI for image guided breast surgery. Image-Guided Procedures 2014: 90362N - 2013
- [j5]Jacob U. Fluckiger, Xia Li, Jennifer G. Whisenant, Todd E. Peterson
Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Data to Constrain a Positron Emission Tomography Kinetic Model: Theory and Simulations. Int. J. Biomed. Imaging 2013: 576470:1-576470:9 (2013) - [j4]Subramani Mani, Yukun Chen, Xia Li, Lori R. Arlinghaus
Research and applications: Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. J. Am. Medical Informatics Assoc. 20(4): 688-695 (2013) - [c5]David S. Smith
Curvelets as a sparse basis for compressed sensing magnetic resonance imaging. Image Processing 2013: 866929 - 2012
- [j3]David S. Smith
Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int. J. Biomed. Imaging 2012: 864827:1-864827:6 (2012) - [j2]David S. Smith
Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Undersampling Patterns on Derived Parameters for DCE- and DSC-MRI. IEEE Trans. Medical Imaging 31(2): 504-511 (2012) - [c4]Xia Li, Lori R. Arlinghaus
Early DCE-MRI Changes after Longitudinal Registration May Predict Breast Cancer Response to Neoadjuvant Chemotherapy. WBIR 2012: 229-235
2000 – 2009
- 2009
- [j1]Robert E. Lee, E. Brian Welch
Implementation of a Semi-automated Post-processing System for Parametric MRI Mapping of Human Breast Cancer. J. Digit. Imaging 22(4): 424-436 (2009) - [c3]Xia Li, Benoit M. Dawant, E. Brian Welch, A. Bapsi Chakravarthy, Darla Freehardt, Ingrid A. Mayer, Mark C. Kelley, Ingrid M. Meszoely, John C. Gore, Thomas E. Yankeelov
Nonrigid registration algorithm for longitudinal breast MR images and the preliminary analysis of breast tumor response. Image Processing 2009: 72590Q - 2007
- [c2]Xia Li, Thomas E. Yankeelov
Constrained non-rigid registration for whole body image registration: method and validation. Image Processing 2007: 651202 - 2006
- [c1]Xia Li, Thomas E. Yankeelov
Multi-modal inter-subject registration of mouse brain images. Image Processing 2006: 61440T
Coauthor Index
aka: David A. Hormuth II
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