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Author profile
Jack B Stevens
Duke University
A Data-Calibrated Gompertz Tumor Growth Model for Predicting Chemoradiation Response In Murine Tumors.
Mechanistic tumor growth models provide a framework for linking observed tumor kinetics to underlying biological processes; however, their application to real preclinical datasets is limited by incomplete longitudinal measurements and uncertainty in model par...
Poster Program · Therapy Physics
A Multiscale Topological Framework for Phenotypic Profiling of the Oropharyngeal Carcinoma Microenvironment
Standardizing the assessment of the Tumor Microenvironment (TME) is critical for personalizing therapy in Head and Neck Squamous Cell Carcinoma (HNSCC). As part of a larger multiomics effort to discover multiscale biomarkers, we developed a mathematical frame...
Poster Program · Therapy Physics
Investigating Intra-Tumoral Heterogeneity Driven By Nutrient Dynamics and Radiotherapy Using an In-Silico Avascular Tumor Growth Model
To develop an in-silico tumor model that incorporates nutrient-driven growth and radiotherapy response to generate spatio-temporal proliferating (P), quiescent (Q), and necrotic (N) cell maps for radiomics-based heterogeneity analysis.
Poster Program · Therapy Physics
Quantum-Inspired Potential Mapping for Multi-Parametric MRI Radiomics–Based Post-Resection Glioblastoma Survival Prediction
To develop a multi-parametric MRI (mp-MRI) radiomics framework for predicting post-resection glioblastoma (GBM) survival by integrating conventional MR modalities with a quantum mechanics–inspired imaging representation.
Poster Program · Diagnostic and Interventional Radiology Physics
In silico Clinical Trials Via Mathematical Modeling Predict Response to Chemoradiation Therapy for Head and Neck Cancer
To develop an in silico clinical trial framework based on mathematical modeling of tumor response to chemoradiation therapy as a prognostic indication of patient outcomes.
Proffered Program · Therapy Physics