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...
Haematopoietic cell transplantation (HCT) is a potentially curative treatment for leukaemia. Pre-transplant conditioning plays a central role in the successful outcome of allogeneic HCT and requires the administration of myeloablative drugs at a high dose. Busulfan is the backbone of such conditioning regimens in both adults and children. Busulfan is an alkylating agent with a narrow...
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...
Mechanistic pharmacokinetic/pharmacodynamic (PK/PD) modeling provides a quantitative framework for characterizing how drug dose and exposure relate to efficacy and toxicity over time \cite{bender2015pkpd_oncology, mould2015exposure_response}. In oncology, such models are increasingly used to inform dose selection, treatment evaluation and the interpretation of therapeutic response. However,...
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...
