Suchergebnisse - "Hormuth II, David A."

Investigating the Limits of Predictability of Magnetic Resonance Imaging-Based Mathematical Models of Tumor Growth.

Autoren: LaMonica, Megan F. Yankeelov, Thomas E. Hormuth II, David A.

Quelle: Cancers; Oct2025, Vol. 17 Issue 20, p3361, 18p

Schlagwörter: MATHEMATICS, GLIOMAS, RESEARCH funding, CELL proliferation, MAGNETIC resonance imaging, DESCRIPTIVE statistics, RATS, MATHEMATICAL models, ANIMAL experimentation, ANALYSIS of variance, THEORY, CARCINOGENESIS

Author Correction: Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.

Autoren: Slavkova, Kalina P. Patel, Sahil H. Cacini, Zachary et al.

Quelle: Scientific Reports; 5/22/2023, Vol. 13 Issue 1, p1-1, 1p

Schlagwörter: CONTINUOUS time models, GLIOMAS, MATHEMATICAL models, TUMORS, INTERNET publishing

Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin.

Autoren: Christenson, Chase Wu, Chengyue Hormuth II, David A. et al.

Quelle: NPJ Systems Biology & Applications; 5/23/2025, Vol. 11 Issue 1, p1-16, 16p

Schlagwörter: TRIPLE-negative breast cancer, OPTIMAL control theory, DIGITAL twin, MAGNETIC resonance imaging, NEOADJUVANT chemotherapy

Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.

Autoren: Hiremath, Khushi C. Atakishi, Kenan Lima, Ernesto A. B. F. et al.

Quelle: Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences; 4/1/2025, Vol. 383 Issue 2293, p1-22, 22p

Schlagwörter: SIMULATED patients, PARAMETER estimation, MATHEMATICAL models, BIOLOGICAL systems, STATISTICAL correlation

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.

Autoren: Alkim, Emel Dowst, Heidi DiCarlo, Julie et al.

Quelle: Tomography: A Journal for Imaging Research; Apr2023, Vol. 9 Issue 2, p810-828, 19p

Schlagwörter: GENE expression, MAGNETIC resonance imaging, IMAGE analysis, BREAST cancer, TRIPLE-negative breast cancer, CONTRAST-enhanced magnetic resonance imaging

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Autoren: Lorenzo, Guillermo Ahmed, Syed Rakin Hormuth II, David A. et al.

Quelle: Annual Review of Biomedical Engineering; Jun2024, Vol. 26 Issue 1, p529-560, 25p

Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer.

Autoren: Kazerouni, Anum S. Hormuth II, David A. Davis, Tessa et al.

Quelle: Cancers; Apr2022, Vol. 14 Issue 7, p1837, 18p

Schlagwörter: ANALYSIS of variance, ANIMAL experimentation, EPIDERMAL growth factor, IMMUNOHISTOCHEMISTRY, FLUOROIMMUNOASSAY, MAGNETIC resonance imaging, DESCRIPTIVE statistics, CELL lines, HORMONE receptor positive breast cancer, MICE, PHENOTYPES

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin.

Autoren: Miniere, Hugo J. M. Lima, Ernesto A. B. F. Lorenzo, Guillermo et al.

Quelle: Cancer Biology & Therapy; 2024, Vol. 25 Issue 1, p1-16, 16p

Schlagwörter: DATA assimilation, BREAST cancer, CANCER cells, DOXORUBICIN, IN vivo studies

Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation.

Autoren: Yang, Jianchen Virostko, Jack Liu, Junyan et al.

Quelle: Scientific Reports; 6/27/2023, Vol. 13 Issue 1, p1-18, 18p

Schlagwörter: MACHINE learning, K-nearest neighbor classification, CELL proliferation, DECISION trees, GLUCOSE transporters, RANDOM forest algorithms, CELL growth

Mathematical modelling of the dynamics of image-informed tumor habitats in a murine model of glioma.

Autoren: Slavkova, Kalina P. Patel, Sahil H. Cacini, Zachary et al.

Quelle: Scientific Reports; 5/22/2023, Vol. 13 Issue 1, p1-14, 14p

Schlagwörter: CONTINUOUS time models, DIFFUSION magnetic resonance imaging, GLIOMAS, CONTRAST-enhanced magnetic resonance imaging, ORDINARY differential equations, MAGNETIC resonance imaging

A data assimilation framework to predict the response of glioma cells to radiation.

Autoren: Junyan Liu Hormuth II, David A. Jianchen Yang et al.

Quelle: Mathematical Biosciences & Engineering; 2023, Vol. 20 Issue 1, p318-336, 19p

A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data.

Autoren: Liu, Junyan Hormuth II, David A. Yang, Jianchen et al.

Quelle: Frontiers in Oncology; 2/4/2022, Vol. 12, p1-14, 14p

Schlagwörter: GLIOMAS, RADIOTHERAPY, PEARSON correlation (Statistics), MICROSCOPY, RADIOBIOLOGY

An experimental-mathematical approach to predict tumor cell growth as a function of glucose availability in breast cancer cell lines.

Autoren: Yang, Jianchen Virostko, Jack Hormuth II, David A. et al.

Quelle: PLoS ONE; 7/13/2021, Vol. 16 Issue 7, p1-24, 24p

Schlagwörter: BREAST cancer, CELL growth, TUMOR growth, CELL physiology, CELL lines, CANCER cells, GLYCOLYSIS

Patient specific, imaging-informed modeling of rhenium-186 nanoliposome delivery via convection-enhanced delivery in glioblastoma multiforme.

Autoren: Woodall, Ryan T Hormuth II, David A Wu, Chengyue et al.

Quelle: Biomedical Physics & Engineering Express; Jul2021, Vol. 7 Issue 4, p1-15, 15p

Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation.

Autoren: Hormuth II, David A. Al Feghali, Karine A. Elliott, Andrew M. et al.

Quelle: Scientific Reports; 4/19/2021, Vol. 11 Issue 1, p1-14, 14p

Schlagwörter: GLIOMAS, MAGNETIC resonance imaging, TUMOR growth, CELL proliferation, CHEMORADIOTHERAPY

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

Autoren: Hormuth II, David A. Jarrett, Angela M. Yankeelov, Thomas E. et al.

Quelle: Radiation Oncology; 1/2/2020, Vol. 15 Issue 1, p1-14, 14p

Schlagwörter: MATHEMATICAL models, RADIOTHERAPY, MAGNETIC resonance imaging, BLOOD volume, AKAIKE information criterion, RADIATION injuries

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

Autoren: Hormuth II, David A. Jarrett, Angela M. Feng, Xinzeng et al.

Quelle: Annals of Biomedical Engineering; Jul2019, Vol. 47 Issue 7, p1539-1551, 13p, 2 Color Photographs, 1 Diagram, 3 Charts, 1 Graph

The effects of intravoxel contrast agent diffusion on the analysis of DCE‐MRI data in realistic tissue domains.

Autoren: Woodall, Ryan T. Barnes, Stephanie L. Hormuth, II, David A. et al.

Quelle: Magnetic Resonance in Medicine; Jul2018, Vol. 80 Issue 1, p330-340, 11p

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.

Autoren: Wu, Chengyue Hormuth II, David A. Easley, Ty et al.

Quelle: Journal of Medical Imaging; Mar/Apr2024, Vol. 11 Issue 2, p24002-24002, 1p

Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.

Autoren: Hormuth II, David A. Phillips, Caleb M. Wu, Chengyue et al.

Quelle: Cancers; Jun2021, Vol. 13 Issue 12, p3008, 1p

Schlagwörter: PATHOLOGIC neovascularization, STATISTICAL models