About this Research Topic
The current oncological practice is based on a "one-size-fits-all" approach through standards of care based on general population averages. Personalized oncology is based on the concept that managing patients’ health should be based on individual patient-specific characteristics. Similarly, personalized radiotherapy has the potential to maximize therapy outcomes while minimizing toxicity by adjusting treatment intensity according to the patients’ response or molecular characteristics, and yet limiting the use of aggressive approaches for non-responders. This approach could be accomplished through tailored target definition, dose prescription, and toxicity management.
The aim of the Research Topic is to spread the principles and practice of a personalized approach in radiation oncology. As the modality of radiation therapy has changed significantly over the past decade, it is crucial for advanced tools supporting physicians when selecting either conventional therapy or a protocol-based treatment or for innovative trial designs based on the patient’s multifactorial profile. The development of predictive models can require either retrospective or prospective data collection with the appropriate validation. Clinical studies mining data about the patient-specific response to treatment are needed to develop prediction modeling and to design prospective trials.
We welcome Original Research as well as Case Report, Clinical Trial, Hypothesis and Theory, Methods, Mini Review, Opinion, Perspective, Review, and Technology and Code articles focused but not limited to the following topics:
1. Personalized interventions (including biological profile-based treatments; image-guided radiotherapy (IGRT); off-, online adaptive treatments; innovative clinical and technological approaches; patient’s characteristics driven treatment selection);
2. Radiation oncological predictive modeling;
3. Radiation oncological therapies (including treatment intensification; modified radiotherapy schedule fractionation/sensibilization; innovative technological approaches; outcome/toxicity prediction for treatment);
4. Radiation Oncological multimodal integration (through personalization of innovative multimodal integrations).
This Research Topic is part one of a two-part series - please also see the collection "Personalization in Modern Radiation Oncology: Predictions, Prognosis and Survival"
The aim of the Research Topic is to spread the principles and practice of a personalized approach in radiation oncology. As the modality of radiation therapy has changed significantly over the past decade, it is crucial for advanced tools supporting physicians when selecting either conventional therapy or a protocol-based treatment or for innovative trial designs based on the patient’s multifactorial profile. The development of predictive models can require either retrospective or prospective data collection with the appropriate validation. Clinical studies mining data about the patient-specific response to treatment are needed to develop prediction modeling and to design prospective trials.
We welcome Original Research as well as Case Report, Clinical Trial, Hypothesis and Theory, Methods, Mini Review, Opinion, Perspective, Review, and Technology and Code articles focused but not limited to the following topics:
1. Personalized interventions (including biological profile-based treatments; image-guided radiotherapy (IGRT); off-, online adaptive treatments; innovative clinical and technological approaches; patient’s characteristics driven treatment selection);
2. Radiation oncological predictive modeling;
3. Radiation oncological therapies (including treatment intensification; modified radiotherapy schedule fractionation/sensibilization; innovative technological approaches; outcome/toxicity prediction for treatment);
4. Radiation Oncological multimodal integration (through personalization of innovative multimodal integrations).
This Research Topic is part one of a two-part series - please also see the collection "Personalization in Modern Radiation Oncology: Predictions, Prognosis and Survival"
Keywords: Personalized radiation oncology, predictive models, toxicity, treatment selection, radiation therapy intensification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
