As neuro-oncology progresses, imaging has become a powerful tool in diagnosing, monitoring progression, and gauging therapeutic responses in various brain tumors. While qualitative assessments in imaging are common, the development and usage of quantitative methods offer more objectivity and precision. This Research Topic aims to delve into the advancement and application of quantitative methods in neuro-oncology imaging, highlighting their role in enhancing tumor detection, treatment, assessment, and patient management.
Researchers and professionals delving into neuro-oncology, radiology, biostatistics, data science, and computational neuroscience are invited to partake in this Research Topic. The focus lies on the exploration of quantitative neuro-oncology imaging methods, including the appraisal of novel algorithms, imaging biomarkers, machine learning techniques, and application of advanced statistical methods. In-depth reviews, original research, and discussions on regulatory and ethical considerations are encouraged.
Objectives:
1. Explore the development of innovative algorithms and quantitative measures: This Topic seeks research that brings innovative algorithms, new quantitative imaging measures, or enhancement of existing methods to the forefront. Studies presenting direct comparisons between these innovative methods and traditional techniques are particularly welcome.
2. Determine the utility of quantitative imaging biomarkers: With potential to provide valuable insights at a cellular and molecular level, imaging biomarkers present an exciting development in neuro-oncology. This Topic encourages research that investigates the usefulness of these biomarkers, their validation, and their incorporation into routine clinical decision-making processes.
3. Identify the role of machine learning and AI-based methods: Machine learning and AI-based approaches are reshaping the world of neuro-oncology imaging. This Topic calls for studies that explore the application of these technologies in the design of predictive models and their potential in improving accuracy and efficiency in imaging analysis.
4. Analyze Multi-Omics in Neuro-Oncology Imaging: The integration of multi-omics analysis with Quantitative Neuro-Oncology Imaging marks a significant stride towards precision medicine. By combining genomic, proteomic, and metabolomic insights with advanced imaging, researchers can better understand tumor biology and heterogeneity. This convergence, known as radiomics, allows for the identification of new biomarkers and therapeutic targets, enhancing the personalization of treatment strategies. Such multidimensional data integration is vital for advancing patient-specific prognosis and tailoring neuro-oncological interventions.
5. Discuss Regulatory and Ethical considerations: As quantitative methods in neuro-oncology imaging evolve, so does the need for increased scrutiny. This Topic rallies for discussions around the ethical, legal, and social implications of these advanced methods, and suggestions on how to navigate these challenges.
Quantitative methods in neuro-oncology imaging present an exciting frontier, with potential to revolutionize diagnosis and treatment. This Research Topic delves into various facets of this rapidly developing field and aims to accelerate the adoption of these methods in routine clinical practice. The amalgamation of neuroscience, computational science, and clinical acumen can bring about new dimensions in managing neuro-oncology, forging paths for innovative, efficient, and effective care for patients.
Keywords:
Multimodal imaging analysis, Machine learning algorithms, Biomarker quantification, PET/CT scans, Perfusion imaging, Image segmentation, Volumetric analysis, Radiomics
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.
As neuro-oncology progresses, imaging has become a powerful tool in diagnosing, monitoring progression, and gauging therapeutic responses in various brain tumors. While qualitative assessments in imaging are common, the development and usage of quantitative methods offer more objectivity and precision. This Research Topic aims to delve into the advancement and application of quantitative methods in neuro-oncology imaging, highlighting their role in enhancing tumor detection, treatment, assessment, and patient management.
Researchers and professionals delving into neuro-oncology, radiology, biostatistics, data science, and computational neuroscience are invited to partake in this Research Topic. The focus lies on the exploration of quantitative neuro-oncology imaging methods, including the appraisal of novel algorithms, imaging biomarkers, machine learning techniques, and application of advanced statistical methods. In-depth reviews, original research, and discussions on regulatory and ethical considerations are encouraged.
Objectives:
1. Explore the development of innovative algorithms and quantitative measures: This Topic seeks research that brings innovative algorithms, new quantitative imaging measures, or enhancement of existing methods to the forefront. Studies presenting direct comparisons between these innovative methods and traditional techniques are particularly welcome.
2. Determine the utility of quantitative imaging biomarkers: With potential to provide valuable insights at a cellular and molecular level, imaging biomarkers present an exciting development in neuro-oncology. This Topic encourages research that investigates the usefulness of these biomarkers, their validation, and their incorporation into routine clinical decision-making processes.
3. Identify the role of machine learning and AI-based methods: Machine learning and AI-based approaches are reshaping the world of neuro-oncology imaging. This Topic calls for studies that explore the application of these technologies in the design of predictive models and their potential in improving accuracy and efficiency in imaging analysis.
4. Analyze Multi-Omics in Neuro-Oncology Imaging: The integration of multi-omics analysis with Quantitative Neuro-Oncology Imaging marks a significant stride towards precision medicine. By combining genomic, proteomic, and metabolomic insights with advanced imaging, researchers can better understand tumor biology and heterogeneity. This convergence, known as radiomics, allows for the identification of new biomarkers and therapeutic targets, enhancing the personalization of treatment strategies. Such multidimensional data integration is vital for advancing patient-specific prognosis and tailoring neuro-oncological interventions.
5. Discuss Regulatory and Ethical considerations: As quantitative methods in neuro-oncology imaging evolve, so does the need for increased scrutiny. This Topic rallies for discussions around the ethical, legal, and social implications of these advanced methods, and suggestions on how to navigate these challenges.
Quantitative methods in neuro-oncology imaging present an exciting frontier, with potential to revolutionize diagnosis and treatment. This Research Topic delves into various facets of this rapidly developing field and aims to accelerate the adoption of these methods in routine clinical practice. The amalgamation of neuroscience, computational science, and clinical acumen can bring about new dimensions in managing neuro-oncology, forging paths for innovative, efficient, and effective care for patients.
Keywords:
Multimodal imaging analysis, Machine learning algorithms, Biomarker quantification, PET/CT scans, Perfusion imaging, Image segmentation, Volumetric analysis, Radiomics
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.