A5045501081- Mathematical Biology Tumor Growth
- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Transplantation: Methods and Outcomes
- Advanced MRI Techniques and Applications
- Glioma Diagnosis and Treatment
- Mechanical Circulatory Support Devices
- Cancer Genomics and Diagnostics
- Cardiac Structural Anomalies and Repair
- Viral Infections and Immunology Research
- Nanoparticle-Based Drug Delivery
- Bioinformatics and Genomic Networks
- Cancer, Hypoxia, and Metabolism
- Cell Image Analysis Techniques
- Advanced Neuroimaging Techniques and Applications
- Advanced Electron Microscopy Techniques and Applications
- Organ Transplantation Techniques and Outcomes
- Vascular Procedures and Complications
- Radiopharmaceutical Chemistry and Applications
- Nanoplatforms for cancer theranostics
- Infective Endocarditis Diagnosis and Management
- HER2/EGFR in Cancer Research
- Aortic aneurysm repair treatments
- Gene Regulatory Network Analysis
The University of Texas at Austin
2016-2024
Livestrong Foundation
2018-2024
Marian University - Indiana
2020
Vanderbilt University
2013-2017
Indiana University Health
2003-2014
Indiana University – Purdue University Indianapolis
2014
HealthPartners
2003-2011
Methodist Hospital
1991-2010
Houston Methodist
1987-2010
Clariant (United States)
2008
Current mathematical models of tumor growth are limited in their clinical application because they require input data that nearly impossible to obtain with sufficient spatial resolution patients even at a single time point--for example, extent vascularization, immune infiltrate, ratio tumor-to-normal cells, or extracellular matrix status. Here we propose the use emerging, quantitative imaging methods initialize new generation predictive models. In near future, these could be able forecast...
This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes models tumor growth. First, process mathematical vascular tumors evolving complex, heterogeneous, macroenvironment living tissue; second, selection plausible among these classes, given relevant observational data; third, statistical calibration and validation finally, prediction key Quantities Interest (QOIs) to patient survival effect...
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. illustrate the as an enabler anticipatory personalized treatment that accounts uncertainties in underlying tumor biology high-grade gliomas, where heterogeneity response standard-of-care (SOC) radiotherapy contributes sub-optimal patient outcomes. The twin is initialized through prior distributions derived from population-level data literature mechanistic model's...
While gliomas have been extensively modelled with a reaction-diffusion (RD) type equation it is most likely an oversimplification. In this study, three mathematical models of glioma growth are developed and systematically investigated to establish framework for accurate prediction changes in tumour volume as well intra-tumoural heterogeneity. Tumour cell movement was described by coupling tissue stress, leading mechanically coupled (MC) RD model. Intra-tumour heterogeneity including...
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into biophysical model treatment of locally advanced breast cancer neoadjuvant therapy. Diffusion-weighted dynamic contrast-enhanced MRI is collected prior therapy, after 1 cycle at the completion first...
Abstract High-grade gliomas are an aggressive and invasive malignancy which susceptible to treatment resistance due heterogeneity in intratumoral properties such as cell proliferation density perfusion. Non-invasive imaging approaches can measure these properties, then be used calibrate patient-specific mathematical models of tumor growth response. We employed multiparametric magnetic resonance (MRI) identify extent (via contrast-enhanced T 1 - weighted, 2 -FLAIR) capture diffusion-weighted...
This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response targeted cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI dynamic contrast-enhanced measure tumor cellularity vascularity, respectively. Tumors stained for anti-CD31, anti-ɑSMA,...
We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels assist in the diagnosis suspicious breast lesions.Ultrafast, fast, high spatial resolution DCE-MRI data were acquired on 15 patients with lesions. Segmentation vasculature from surrounding tissue was performed by applying Hessian filter enhanced image generate map probability for each voxel belong vessel. Summary measures generated vascular morphology, as well inputs outputs...
The goal of this study is to experimentally and computationally investigate combination trastuzumab-paclitaxel therapies identify potential synergistic effects due sequencing the with in vitro imaging mathematical modeling. Longitudinal alterations cell confluence are reported for an model BT474 HER2+ breast cancer cells following various dosages timings paclitaxel trastuzumab regimens. Results drug regimens evaluated interaction relationships based on order, timing, quantity dose drugs....
Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential develop tools capable identifying and predicting intra- inter-tumor heterogeneities. Biology-inspired mathematical models are attacking this problem, but tumor often overlooked in
We have developed a family of biology-based mathematical models high-grade glioma (HGG), capturing the key features tumour growth and response to chemoradiation. now seek quantify accuracy parameter estimation determine, when given virtual patient cohort, which model was used generate tumours. In this way, we systematically test both identifiability. Virtual patients are generated from unique parameters whose dynamics determined by family. then assessed ability recover select tumour....
Reaction–diffusion models have been widely used to model glioma growth. However, it has not shown how accurately this can predict future tumor status using parameters (i.e., cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To end, we silico studies develop the methods needed estimate specific reaction–diffusion parameters, then tested accuracy with which these The analogous study was performed a murine of parameter estimation approach an 'grown' for ten...
Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in size. Our goal is predict the of breast tumors using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended previously established, mechanically coupled, reaction-diffusion predicting initialized with patient-specific diffusion weighted MRI (DW-MRI) by including effects chemotherapy drug delivery,...