About this Research Topic
Oncological imaging has undergone rapid transformation over the past decade, evolving from traditional diagnostic techniques into sophisticated tools that can offer far more than just anatomical visualization. This evolution is pivotal in enhancing early cancer detection, treatment planning, and predicting patient outcomes. With the advent of advanced imaging techniques, such as radiomics, MR relaxometry, radiogenomics, and other specialized modalities, oncologists now have a wealth of data at their disposal that can provide deep insights into tumor biology, heterogeneity, and therapeutic responses.
Radiomics stands at the forefront of this transformation. This technique involves the extraction of quantitative features from medical images that go beyond visual interpretation. By converting images into high-dimensional data, radiomics allows for a detailed analysis of tumor characteristics, including texture, shape, and intensity. This data, when combined with clinical and genomic information, can enhance decision-making in precision oncology. Manuscripts addressing novel radiomic approaches, feature extraction algorithms, and their clinical applications in various cancer types are highly encouraged.
Radiogenomics bridges the gap between imaging and genomics, offering a unique opportunity to correlate imaging phenotypes with genomic data. This integration is essential for understanding tumor behavior at a molecular level and can lead to the identification of new biomarkers for personalized cancer treatment. Research manuscripts that present innovative radiogenomic studies, including image-based prediction of genetic mutations or associations between imaging features and gene expression patterns, are of particular interest.
In addition to these key areas, we are also seeking submissions on other specialized imaging techniques that are pushing the boundaries of oncological imaging. These may include but are not limited to, artificial intelligence (AI)-driven imaging analysis, positron emission tomography (PET) and computed tomography (CT) innovations, optical imaging techniques, and hybrid imaging modalities. Manuscripts highlighting novel technologies, their integration into clinical workflows, and their impact on patient outcomes are crucial for advancing the field.
Overall, this topic aims to compile cutting-edge research and reviews that highlight the latest advancements in oncological imaging. We invite submissions that contribute to the growing body of knowledge in this dynamic field, with the goal of improving cancer diagnosis, prognosis, and treatment. Contributions that emphasize translational research, potential clinical applications, and interdisciplinary collaborations are particularly encouraged.
Radiomics stands at the forefront of this transformation. This technique involves the extraction of quantitative features from medical images that go beyond visual interpretation. By converting images into high-dimensional data, radiomics allows for a detailed analysis of tumor characteristics, including texture, shape, and intensity. This data, when combined with clinical and genomic information, can enhance decision-making in precision oncology. Manuscripts addressing novel radiomic approaches, feature extraction algorithms, and their clinical applications in various cancer types are highly encouraged.
Radiogenomics bridges the gap between imaging and genomics, offering a unique opportunity to correlate imaging phenotypes with genomic data. This integration is essential for understanding tumor behavior at a molecular level and can lead to the identification of new biomarkers for personalized cancer treatment. Research manuscripts that present innovative radiogenomic studies, including image-based prediction of genetic mutations or associations between imaging features and gene expression patterns, are of particular interest.
In addition to these key areas, we are also seeking submissions on other specialized imaging techniques that are pushing the boundaries of oncological imaging. These may include but are not limited to, artificial intelligence (AI)-driven imaging analysis, positron emission tomography (PET) and computed tomography (CT) innovations, optical imaging techniques, and hybrid imaging modalities. Manuscripts highlighting novel technologies, their integration into clinical workflows, and their impact on patient outcomes are crucial for advancing the field.
Overall, this topic aims to compile cutting-edge research and reviews that highlight the latest advancements in oncological imaging. We invite submissions that contribute to the growing body of knowledge in this dynamic field, with the goal of improving cancer diagnosis, prognosis, and treatment. Contributions that emphasize translational research, potential clinical applications, and interdisciplinary collaborations are particularly encouraged.
Keywords: Radiomics, Oncological, Imaging, radiogenomics, specialized modalities
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.
