Special Issue: Mathematical modeling in radiotherapy and immunotherapy

Guest Editors

Dr. David A. Hormuth II
The University of Texas at Austin, Austin, USA
Email: david.hormuth@utexas.edu

Dr. Guillermo Lorenzo
The University of Texas at Austin, Austin, USA & University of Pavia, Italy
Email: guillermo.lorenzo@unipv.it

Dr. Joseph D. Butner
MD Anderson Cancer Center, USA
Email: JDButner@mdanderson.org

Dr. Chengyue Wu
The University of Texas at Austin, Austin, USA
Email: cw35926@utexas.edu

Dr. Mohammad U. Zahid
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, USA
Email: mohammad.zahid@moffitt.org

Manuscript Topics

For over a century, radiotherapy has been leveraged through many modalities to treat cancer, especially in settings where complete surgical resection may be challenging or impossible to perform. More recently, immunotherapy has also become an important pillar in the treatment of many cancers, and it can be administered alone or in combination with other treatments including radiotherapy. Mathematical modeling of the response of healthy and malignant tissue to radiotherapy has been providing insight into the complex radiobiological phenomena underlying this cancer treatment. Additionally, mathematical modeling has also enabled personalized tumor forecasting, which sets the groundwork for potential optimization of radiation therapy schedule and dose for each individual patient. Likewise, mathematical models have also contributed to the understanding of the intricate interplay between tumor cells and cells of the immune system after the delivery of novel targeted therapies (e.g., CAR-T cells, immune checkpoint inhibitors), thereby providing a basis to predict and design immunotherapy regimens for each patient. Furthermore, mathematical models will be critical for understanding the complex, non-linear interactions of radiation with the immune system and immunotherapies.  Given the promising results achieved by mathematical models of radiotherapy and immunotherapy thus far, there is a critical need for further development of these models in order to accelerate biomedical research and facilitate the personalized and optimal delivery of radio- and immunotherapies either alone or in combination. In particular, it is clear that the identification of an optimal patient-specific treatment schedule that maximizes the therapeutic outcomes while controlling for toxicities is a central challenge. Nevertheless, mathematical models provide a robust and rigorous framework to investigate the vast therapeutic design space (e.g., dosage, scheduling, treatment type), which is impractical or even unfeasible to explore experimentally. In this special issue, we aim to assemble a collection of original and review papers presenting mathematical modeling techniques or theories describing the response of cancers to radiotherapy and/or immunotherapy to address open challenges in the understanding, delivery, and mathematical representation of these treatments. Potential contributions include but are not limited to the following topics:

• Spatial and/or time-resolved mechanistic models of treatment response
• Multi-scale modeling of tumor response dynamics
• Modeling of treatment resistance and tumor evolution
• Forecasting treatment response
• Linking mathematical models with clinical or experimental data
• Optimal delivery of therapy
• Hybrid models combining mechanistic and machine learning approaches

Keywords: Mathematical oncology; computational oncology; computational biology; biomechanistic modeling; data-informed modeling; optimized therapy; tumor forecasting; digital twins; synergistic therapy; radiotherapy; radiobiology; immunotherapy

Instructions for authors
Please submit your manuscript to online submission system

Paper Submission

All manuscripts will be peer-reviewed before their acceptance for publication. The deadline for manuscript submission is 31 March 2024

Published Papers({{count}})

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