Math meets the Clinic: Modeling Patient Specific HNSCC Radiation Response Dynamics

Authors

• Rohan Vijayvikram, University of DundeeFollow
• Pirmin Schlicke, The University of Texas MD Anderson Cancer CenterFollow
• Mohammad U. Zahid, The University of Texas MD Anderson Cancer CenterFollow
• Dina M. El-Habashy, The University of Texas MD Anderson Cancer CenterFollow
• Cem Dede, The University of Texas MD Anderson Cancer CenterFollow
• Clifton D. Fuller, The University of Texas MD Anderson Cancer CenterFollow
• Heiko Enderling, The University of Texas MD Anderson Cancer CenterFollow

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Description

Head and neck squamous cell carcinomas (HNSCCs) originate from the mucosal lining of the upper aerodigestive tract and radiation therapy has become a fundamental component in the standard care for these patients. However, the treatment's unique dynamics can lead to disparities in outcomes and biological responses in both tumor and normal tissues. A significant challenge remains in predicting individual patient responses to radiation, with variability often resulting in under or over-treatment and potentially adverse effects. The absence of reliable biomarkers highlights the need for predictive measures to guide clinical decisions in real-time. The integration of mathematical models in radiation therapy offers a promising solution, with models incorporating a global lambda demonstrating the ability to predict treatment responses beyond initial weeks. These models provide a framework to address patient response variability, potentially improving survival rates and quality of life.

Program Affiliation

Radiation Oncology Summer Experience Program

DOI

https://doi.org/10.52519/00145

Publication Date

Summer 8-1-2024

Recommended Citation

Vijayvikram, Rohan; Schlicke, Pirmin; Zahid, Mohammad U.; El-Habashy, Dina M.; Dede, Cem; Fuller, Clifton D.; and Enderling, Heiko, "Math meets the Clinic: Modeling Patient Specific HNSCC Radiation Response Dynamics" (2024). Radiation Oncology Medical Student Program 2024. 5.
doi:https://doi.org/10.52519/00145

Keywords

Digital Twin; Quantitative Personalized Oncology; Radiation Oncology; Mathematical Modeling in Radiation Oncology