Radiotherapy (RT) now represents one of the pillars of cancer treatment and is part of the therapeutic path for a large fraction of cancer patients. Technological advancements in RT have been always directed towards delivering a highly selective dose distribution, whilst allowing an effective tumor treatment, and sparing organs at risk. Modern RT is based on advanced photon techniques (e.g. VMAT etc) as well as on charged particle therapy (i.e. protons and carbon ions). A better understanding of the radiobiological aspects related to RT is also needed to further improve treatment effectiveness. Great advances have been possible in recent years thanks to combined radio- and immuno-therapy treatments. Thus, it is clear that a combined optimization of the physical and radiobiological variables involved in a RT treatment is required to move toward the personalization of effective treatments, which could also contribute to the transition toward precision medicine in this field.
In this context, a crucial role is represented by modeling studies. On one hand, radiobiological modeling at different scales of complexity allows a better understanding of the mechanisms behind radiation effects. On the other hand, the application of mathematical models to the description of RT outcomes (both tumor control and toxicity) is needed to set up effective treatments. Simultaneously, such models are also important to establish efficient patient-selection criteria when comparing different RT modalities (e.g. protons vs photons).
Given the importance of accurate modeling of the effect of radiation on biological tissue, recently there has been a clear and growing interest in developing artificial Machine Learning-based models (ML). ML has proved to be an extremely flexible tool for constructing accurate and robust predictive models from data. Following the disruptive effect that ML has had in various fields of application, ML has recently started to be applied in the RT context, for example in AI-driven adaptive RT.
In this Research Topic we aim to highlight the recent efforts in the radiobiological modeling community at different levels. We welcome multiscale mechanistic studies on the mechanisms underlying radiation effects. In this context, micro- and nano-dosimetry contributions are encouraged. We are also eager to receive manuscripts dedicated to model development in a more clinical context, such as those focusing on TCP and NTCP models. We recognize the growing interest in the application of machine learning techniques also in this research field, and we will consider related contributions. Similarly, we strongly support modeling efforts focused on the understanding of the FLASH effect and on considerations for its clinical application.
We invite submissions on radiobiological modelization covering all aspects of oncology application, from advanced TCP and NTCP modeling to recent rising interest in data-driven ML models and application to FLASH RT. Potential topics include but are not limited to:
1. Micro-and nanodosimetry based radiobiological mathematical modeling;
2. Machine learning-based radiobiological models;
3. Modeling of hypoxia and others radiation modifiers;
4. Modeling of combined and multi-ion treatments;
5. Modeling of combined chemo-/immunotherapy treatments;
6. Voxel-based TCP and NTCP models;
7. Machine learning-based radiotherapy outcome modeling;
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Radiotherapy (RT) now represents one of the pillars of cancer treatment and is part of the therapeutic path for a large fraction of cancer patients. Technological advancements in RT have been always directed towards delivering a highly selective dose distribution, whilst allowing an effective tumor treatment, and sparing organs at risk. Modern RT is based on advanced photon techniques (e.g. VMAT etc) as well as on charged particle therapy (i.e. protons and carbon ions). A better understanding of the radiobiological aspects related to RT is also needed to further improve treatment effectiveness. Great advances have been possible in recent years thanks to combined radio- and immuno-therapy treatments. Thus, it is clear that a combined optimization of the physical and radiobiological variables involved in a RT treatment is required to move toward the personalization of effective treatments, which could also contribute to the transition toward precision medicine in this field.
In this context, a crucial role is represented by modeling studies. On one hand, radiobiological modeling at different scales of complexity allows a better understanding of the mechanisms behind radiation effects. On the other hand, the application of mathematical models to the description of RT outcomes (both tumor control and toxicity) is needed to set up effective treatments. Simultaneously, such models are also important to establish efficient patient-selection criteria when comparing different RT modalities (e.g. protons vs photons).
Given the importance of accurate modeling of the effect of radiation on biological tissue, recently there has been a clear and growing interest in developing artificial Machine Learning-based models (ML). ML has proved to be an extremely flexible tool for constructing accurate and robust predictive models from data. Following the disruptive effect that ML has had in various fields of application, ML has recently started to be applied in the RT context, for example in AI-driven adaptive RT.
In this Research Topic we aim to highlight the recent efforts in the radiobiological modeling community at different levels. We welcome multiscale mechanistic studies on the mechanisms underlying radiation effects. In this context, micro- and nano-dosimetry contributions are encouraged. We are also eager to receive manuscripts dedicated to model development in a more clinical context, such as those focusing on TCP and NTCP models. We recognize the growing interest in the application of machine learning techniques also in this research field, and we will consider related contributions. Similarly, we strongly support modeling efforts focused on the understanding of the FLASH effect and on considerations for its clinical application.
We invite submissions on radiobiological modelization covering all aspects of oncology application, from advanced TCP and NTCP modeling to recent rising interest in data-driven ML models and application to FLASH RT. Potential topics include but are not limited to:
1. Micro-and nanodosimetry based radiobiological mathematical modeling;
2. Machine learning-based radiobiological models;
3. Modeling of hypoxia and others radiation modifiers;
4. Modeling of combined and multi-ion treatments;
5. Modeling of combined chemo-/immunotherapy treatments;
6. Voxel-based TCP and NTCP models;
7. Machine learning-based radiotherapy outcome modeling;
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
