Development of a Virtual Preclinical CT Platform for Advanced Imaging and Theranostics in Head and Neck Cancer Research

Abstract Effective treatment of aggressive and treatment-resistant cancers, such as head and neck squamous cell carcinoma (HNSCC), demands innovative methodologies that accurately capture the complexities of the tumor microenvironment and predict therapeutic responses. However, traditional imaging techniques alone may not fully capture the complex and dynamic processes of tumor growth, angiogenesis, and therapeutic responses, limiting our ability to develop and optimize effective treatments. This project aims to develop a Virtual Preclinical CT (VPCT) platform to serve as a "digital twin" for studying preclinical cancer models of HNSCC. By integrating advanced photon-counting CT (PCCT) imaging with sophisticated computational models, the VPCT platform will simulate tumor behavior, optimize imaging protocols, and evaluate therapeutic interventions in a virtual setting. The VPCT platform will combine high-resolution tumor and vascular models with PCCT simulations to accurately replicate the anatomical and physiological complexity of mouse cancer models, thereby enabling precise simulations of therapeutic outcomes. Our approach is organized into three specific aims: Aim 1 focuses on developing the VPCT imaging platform by enhancing digital mouse phantoms with detailed vascular and tumor models derived from high-resolution PCCT data and integrating multiscale tumor models using computational platforms like CompuCell3D. Aim 2 will validate the VPCT platform by comparing simulated imaging data with empirical data from custom-designed phantoms and in vivo PCCT imaging of HNSCC tumors, ensuring the platform’s accuracy in material separation and vascular mapping. Aim 3 will leverage the validated VPCT platform to optimize and evaluate radiation therapy (RT) and combination treatments for HNSCC. Specifically, we will identify the optimal timing for injecting high atomic number (Z) barium nanoparticles (VivoVist™) in relation to RT to maximize vascular disruption and enhance the enhanced permeability and retention (EPR) effect. Additionally, we will explore the synergistic potential of combining VivoVist™ nanoparticles with RT and the chemotherapy agent Doxil (liposomal doxorubicin). By integrating advanced radiomics, machine learning, and computational pathology, we aim to understand therapeutic strategies and significantly improve the accuracy of treatment outcome predictions. The VPCT platform is anticipated to significantly improve tumor modeling and optimize PCCT protocols for assessing the effects of RT on tumor vasculature and evaluating combination therapies. By focusing on vascular changes and the synergistic effects of combined treatments, the platform will offer critical insights for refining RT and chemotherapeutic strategies in cancer treatment. Additionally, integrating advanced computational techniques will enhance predictive accuracy, enabling preclinical findings to directly inform clinical applications and advance personalized cancer therapies.