Radiotherapy (RT) is an effective localized therapy used to treat ~75% of head and neck cancer (HNC) patients. However, delivery to surrounding normal tissues induce toxicities that exacerbate patient symptoms. Motivated by a published dataset of longitudinal patient reported outcomes (PROs) in HNC patients treated with RT, we developed a mathematical model to capture both on-target tumor...
Brain necrosis after brain and head & neck radiotherapy presents a fundamental inference problem since by the time a lesion is visible on MRI, it has already expanded, remodeled, and erased the evidence of where and why it began. Behind this expansion lies a spatially dynamic process governed by brain architecture and patient-specific biology, which are not captured in clinical dose...
Over 75% of cancer patients receive chemo- and/or radiation therapy, but treatment schedules are not optimized for the individual patient. Optimized schedules may lead to improved outcomes. We aim to (1) collect confluence time courses for glioma cells treated over a wide range of chemo- and radiation therapy schedules, (2) calibrate a biology-based math model to the data, and (3) apply...
Adaptive cancer therapy is a new paradigm of treatment for non-curative disease that aims to prolong emergence of resistance, and thus treatment failure. Here we use a mathematical model to explore how incorporating treatment toxicity into the protocol of adaptive therapy can be beneficial by both extending time to treatment failure and improving the quality of life for the patient...
Anti-cancer drug treatment can induce tumor cells to adopt reversible drug-tolerant phenotypic states, setting the stage for the evolution of resistance and eventual treatment failure. This challenges conventional maximum tolerated dose (MTD) regimens by creating a trade-off between short-term tumor reduction and long-term tumor control. Here, we show how mathematical model-informed treatment...
Deciphering intratumor spatial configuration of cell communities is fundamental for mechanistically understanding how heterogeneity in tumor phenotypes impacts the effectiveness of treatments. Such dynamic interplay in the tumor microenvironment determines a continuum of transition stages, having different levels of compliance to therapy \cite{prunella2025pharmacometric}. Scheduling and...
The integration of mechanistic models with machine learning is becoming increasingly important for predicting treatment response and optimizing dose schedules in cancer therapy. Mechanistic models based on differential equations capture biological processes such as tumor growth and drug dynamics, while machine learning provides flexible tools for learning unknown components from data. However,...
RT for HPV+ oropharyngeal cancer has high cure rates, but this is often associated with significant toxicity. Despite broad interest in de-intensifying RT in this context, there isnโt a reliable biomarker to identify individual patients for safe de-escalation without sacrificing cure. We address this by creating a virtual cohort of head and neck cancer. The virtual cohort is based on two...
Recent efforts to improve cancer treatment focus on identifying new therapeutic targets, enhancing delivery methods, and optimizing treatment combinations and sequencing to address the pronounced spatial and temporal heterogeneity of tumors. Mathematical models of cancer progression and therapeutic response have emerged as powerful tools for personalized tumor forecasting, adaptive treatment...
