Home WakeSpace Scholarship › Electronic Theses and Dissertations

DEVELOPMENT OF AN IMAGE-BASED MATHEMATICAL MODELING TOOL FOR MULTI-SCALE CHARACTERIZATION OF BREAST CANCER RESPONSE TO NEOADJUVANT THERAPY

Electronic Theses and Dissertations

Item Files

Item Details

title
DEVELOPMENT OF AN IMAGE-BASED MATHEMATICAL MODELING TOOL FOR MULTI-SCALE CHARACTERIZATION OF BREAST CANCER RESPONSE TO NEOADJUVANT THERAPY
author
Bowers, Haley Johnson
abstract
Breast cancer therapeutic regimens are currently administered in a structured and pre-determined manner with drug and dosing strategies determined based on broad molecular subtypes. Personalized medicine efforts continue to identify new biomarkers and druggable targets to support further enhancements to individualizing neoadjuvant therapy (NAT) regimens. Most precision medicine efforts take a genetic-centric approach, which has led to major advancements in breast cancer care, but overlooks other important patient-specific phenotypic measures that influence response to therapy. Development of new methods to characterize these phenotypic metrics of response may offer opportunity to complement current genomic strategies to advance personalized medicine. Cancer is a highly dynamic disease, both spatially and temporally, but current NAT paradigms are static with no current tools to dynamically adapt to changes in therapeutic response. New technologies to characterize the dynamics of cancer are needed to guide the optimization of drug dosing schedules to support advanced adaptive therapeutic regimens. A barrier to this approach is the lack of phenotypic assessment methods for therapy selection and monitoring settings. This work focuses on the integration of imaging data, biophysics, and mathematics to establish multi-scale computational modeling methods for characterization of breast cancer response to therapy. The key innovation of this work is the deployment of harmonized modeling strategies to characterize response both in vitro and in vivo using observational imaging data. Within breast cancer, there are a number of effective therapeutic regimens, and even more still being developed, but heterogeneous response to these therapies presents a major challenge. To address this, we developed a coupled experimental-computational framework to estimate multicellular tumor spheroid growth and invasion parameters to quantitatively investigate the dynamics of cancer growth and therapeutic response in vitro. This motivated deploying a similar image data-driven mathematical modeling approach for dynamic characterization of clinical breast cancer response to NAT. This work provided methods for characterizing changes in magnetic resonance (MR) imaging data using spatial estimates of tumor proliferation to enable accurate predictions of the residual tumor burden. This multi-scale integration of computational modeling with both microscopy and clinical imaging data has the potential to personalize dynamic NAT regimens based on patient-specific response assessment to improve treatment outcomes and reduce treatment-related adverse events.
subject
Biophysical
Breast
Cancer
Imaging
Mathematical
Modeling
contributor
Weis, Jared A (committee chair)
Thomas, Alexandra (committee member)
Zhao, Dawen (committee member)
Robertson, John (committee member)
Levi, Nicole (committee member)
date
2022-09-17T08:35:50Z (accessioned)
2023-09-16T08:30:07Z (available)
2022 (issued)
degree
Biomedical Engineering (discipline)
embargo
2023-09-16 (terms)
identifier
http://hdl.handle.net/10339/101262 (uri)
language
en (iso)
publisher
Wake Forest University
type
Dissertation

Usage Statistics