Table 3.

How mathematical models help the creation of digital twins in oncology. Mathematical models play a crucial role in the creation of digital twins in oncology by simulating and predicting the behavior of tumors and their interactions with therapies.

Field	Modeling Tumor Growth
Mathematical models, such as ordinary differential equations (ODEs) and partial differential equations (PDEs), are used to describe the following:	Cell proliferation and death	Tumor microenvironment	Spatial growth patterns
Field	Immune Response Dynamics
Mathematical models simulate the interaction between the tumor and the patient’s immune system in specific way:	Immune cell infiltration	Cytokine signaling	Immunotherapy response
Field	Drug Response and Therapy Optimization
Models of pharmacokinetics (PK) and pharmacodynamics (PD) are used to simulate how drugs are absorbed, distributed, metabolized, and eliminated in the body. These models help to predict the following:	The effectiveness of chemotherapy or targeted therapies based on drug concentration at the tumor site	Optimal dosing regimens to minimize side effects	Resistance mechanisms that may emerge during treatment
Field	Metastasis Simulation
Mathematical models can track how cancer cells migrate from the primary tumor to form metastases in other organs. These models simulate the following:	Cell migration	The process of tumor cells establishing new metastatic sites	Therapy resistance in metastases
Field	Predicting Outcomes and Clinical Decision Support
Digital twins built on mathematical models can predict future scenarios, such as the following:	The likelihood of tumor recurrence after surgery or chemotherapy	How long a treatment will remain effective before resistance develops	Which combination of therapies will yield the best outcomes based on the tumor’s specific characteristics