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.
At the basis of such an innovative perspective is the personalization of modern radiation oncology to gain useful insights about prognosis associated with different clinical presentations. Although grouped by staging and some additional features, many characteristics that can result in subgroups associated with very different prognoses are unclear. Moreover, predictive algorithms can be built by recruiting information from many different sources (including, clinical features, radiomic analysis, treatment planning data, etc), helping to address the prognosis associated with a specific presentation, in order to define the optimal workflow for each patient. Finally, the use of reliable tools for outcome prediction could possibly lead to an improved survival outcome for our patients.
We welcome a range of article types focused on but not limited to the following topics:
1. Radiation oncological predictive modeling;
2. Survival Analysis;
3. Radiation oncological therapy by predictive perspective analysis;
4. Radiation oncological multimodal integration (through personalization of innovative multimodal integrations);
5. Innovative radiological predictive analyses applied to radiation oncology
This Research Topic is part two of a two-part series - please also see the collection "Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls"
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Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
At the basis of such an innovative perspective is the personalization of modern radiation oncology to gain useful insights about prognosis associated with different clinical presentations. Although grouped by staging and some additional features, many characteristics that can result in subgroups associated with very different prognoses are unclear. Moreover, predictive algorithms can be built by recruiting information from many different sources (including, clinical features, radiomic analysis, treatment planning data, etc), helping to address the prognosis associated with a specific presentation, in order to define the optimal workflow for each patient. Finally, the use of reliable tools for outcome prediction could possibly lead to an improved survival outcome for our patients.
We welcome a range of article types focused on but not limited to the following topics:
1. Radiation oncological predictive modeling;
2. Survival Analysis;
3. Radiation oncological therapy by predictive perspective analysis;
4. Radiation oncological multimodal integration (through personalization of innovative multimodal integrations);
5. Innovative radiological predictive analyses applied to radiation oncology
This Research Topic is part two of a two-part series - please also see the collection "Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls"
--
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
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.
