About this Research Topic
Innovative use of imaging information on oncologic intervention such as radiation therapy is crucial for better treatment outcomes in the population and to increase the treatment efficacy at the individual patient level. There are recent significant advances in molecular imaging tools, some of which became available for routine clinical applications. These tools can partially offer information necessary for precision medicine. However, most standard clinical imaging tools do not provide health professionals with sufficient biological and medical data to confidently perform precision oncology. Advanced mathematical and computational techniques used to process imaging data and discover underlying biology hidden in the images are valuable analytical tools to achieve the goal or get closer to it. Examples of such tools include radiomics with mathematical imaging biomarkers and radiogenomics which might offer clues on the genomics of subjects. Additionally, machine learning tools can be applied to find otherwise unknown relationships between biology and its images.
Accurate, robust, and reliable mathematical techniques that relate images to the hidden biology and the effects of radiation at the individual level are needed to advance precision radiotherapy. One of the vital image analysis tools is radiomics. Although there were many publications on the applications of this tool in radiation oncology, the connection between the radiomics features and biology is unclear because most features were introduced and optimized in a field unrelated to medicine and biology, for example, for earth surveillance. Hence, there is an urgent need for new radiomics features specifically created for the biology of tumors considering the hallmarks of cancer, such as the sustaining proliferation, angiogenesis, and invasion/metastasis ability. The radiomics-based biomarkers should also be relevant to the immunology of cancers and indicate the tumor microenvironment accurately.
Furthermore, there is little experimental evidence showing the relationship between radiomics features and underlying biology. We need hard evidence with reliable data on this topic to make further progress. The same can be said about radiogenomics. We need more reliable biomarkers/features with a stronger connection to genomic expression specific to tumor types and an individual patient. Once new mathematical features are validated for better association with biology, the next step is to apply those to personalize the radiation therapy, in particular, dosage and treatment schedule. This requires adequate mathematical models which can model the dynamics of tumor growth and the radiation response. Additionally, the biomarkers and features can be used with less clearly defined but powerful computational tools developed in machine learning.
The proposed article collection will provide the current state of research aimed at discovering biology-specific radiomics features for precision radiotherapy and applications of such tools in radiation oncology. The researchers who work in the radiomics of various cancer types with a strong emphasis on patient-specific biology are encouraged to contribute to this article collection.
Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases that are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this Research Topic.
Accurate, robust, and reliable mathematical techniques that relate images to the hidden biology and the effects of radiation at the individual level are needed to advance precision radiotherapy. One of the vital image analysis tools is radiomics. Although there were many publications on the applications of this tool in radiation oncology, the connection between the radiomics features and biology is unclear because most features were introduced and optimized in a field unrelated to medicine and biology, for example, for earth surveillance. Hence, there is an urgent need for new radiomics features specifically created for the biology of tumors considering the hallmarks of cancer, such as the sustaining proliferation, angiogenesis, and invasion/metastasis ability. The radiomics-based biomarkers should also be relevant to the immunology of cancers and indicate the tumor microenvironment accurately.
Furthermore, there is little experimental evidence showing the relationship between radiomics features and underlying biology. We need hard evidence with reliable data on this topic to make further progress. The same can be said about radiogenomics. We need more reliable biomarkers/features with a stronger connection to genomic expression specific to tumor types and an individual patient. Once new mathematical features are validated for better association with biology, the next step is to apply those to personalize the radiation therapy, in particular, dosage and treatment schedule. This requires adequate mathematical models which can model the dynamics of tumor growth and the radiation response. Additionally, the biomarkers and features can be used with less clearly defined but powerful computational tools developed in machine learning.
The proposed article collection will provide the current state of research aimed at discovering biology-specific radiomics features for precision radiotherapy and applications of such tools in radiation oncology. The researchers who work in the radiomics of various cancer types with a strong emphasis on patient-specific biology are encouraged to contribute to this article collection.
Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases that are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this Research Topic.
Keywords: Mathematics, model, computation, biology, radiotherapy, Precision medicine
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