With the progress and improvement of computer science, artificial intelligence (AI) methods can now be applied to uncover novel findings in multiple disciplines, including clinical medicine. With the easy access to neuro-images via magnetic resonance imaging (MRI) and computed tomography (CT) techniques, the field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. AI methods have the potential to improve the precision of diagnostic and therapeutic methods in neuro-oncology. Currently, several applications of AI methods in neuro-oncology could be considered, including prediction of tumor grade, delineation of infiltrating margins of diffuse gliomas, monitoring tumor progression, prediction of survival and recurrence, prediction of molecular biomarkers changes, and prediction of drug response. Altogether, these AI-based radiomic and radiogenomic methods could be applied for patient stratification with more precise initial diagnostic approaches and therapeutic options in the management of brain tumor patients. It is now necessary to further develop the methodology and determine the full clinical utility of these novel approaches in neuro-oncology.
The main objective of this Research Topic is to report the latest research and advances of AI methods in neuro-oncology. Possible applications of AI methods in neuro-oncology include but are not limited to the diagnosis, treatment, and cancer biology of brain tumors. We invite contributions with high clinical translational significance, covering novel methods in computation algorithms, clinical evaluation of the application of AI methods in the management of brain tumor patients, or translational studies that uncover the molecular mechanisms for important radiological signs and appearances.
We welcome Original Research, Review and other relevant articles related to the latest advances in AI methods for neuro-oncology. Specific topics of particular interest include, but are not limited to:
- Prediction of tumor grade;
- Monitoring tumor progression;
- Prediction of survival and recurrence;
- Prediction of drug response;
- Early cancer diagnostics;
- Molecular mechanisms underlying the radiological signs and appearance of important clinical significance.
NOTE TO AUTHORS: Prospective Authors are kindly requested to observe the most appropriate submission path for their manuscript. While most submissions are expected to be submitted via Neuro-Oncology and Neurosurgical Oncology, those mainly focused on imaging methods for tumor grade prediction and response and survival monitoring should be submitted via the Cancer Imaging and Image-directed Interventions specialty of Frontiers in Oncology, while submissions highlighting the potentialities of Artificial Intelligence at this clinical interface must be submitted to the Medicine and Public Health specialty of Frontiers in Artificial Intelligence.
The Topic Editors and the Editorial Team reserve the right to redirect manuscripts to the most appropriate section as necessary.
With the progress and improvement of computer science, artificial intelligence (AI) methods can now be applied to uncover novel findings in multiple disciplines, including clinical medicine. With the easy access to neuro-images via magnetic resonance imaging (MRI) and computed tomography (CT) techniques, the field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. AI methods have the potential to improve the precision of diagnostic and therapeutic methods in neuro-oncology. Currently, several applications of AI methods in neuro-oncology could be considered, including prediction of tumor grade, delineation of infiltrating margins of diffuse gliomas, monitoring tumor progression, prediction of survival and recurrence, prediction of molecular biomarkers changes, and prediction of drug response. Altogether, these AI-based radiomic and radiogenomic methods could be applied for patient stratification with more precise initial diagnostic approaches and therapeutic options in the management of brain tumor patients. It is now necessary to further develop the methodology and determine the full clinical utility of these novel approaches in neuro-oncology.
The main objective of this Research Topic is to report the latest research and advances of AI methods in neuro-oncology. Possible applications of AI methods in neuro-oncology include but are not limited to the diagnosis, treatment, and cancer biology of brain tumors. We invite contributions with high clinical translational significance, covering novel methods in computation algorithms, clinical evaluation of the application of AI methods in the management of brain tumor patients, or translational studies that uncover the molecular mechanisms for important radiological signs and appearances.
We welcome Original Research, Review and other relevant articles related to the latest advances in AI methods for neuro-oncology. Specific topics of particular interest include, but are not limited to:
- Prediction of tumor grade;
- Monitoring tumor progression;
- Prediction of survival and recurrence;
- Prediction of drug response;
- Early cancer diagnostics;
- Molecular mechanisms underlying the radiological signs and appearance of important clinical significance.
NOTE TO AUTHORS: Prospective Authors are kindly requested to observe the most appropriate submission path for their manuscript. While most submissions are expected to be submitted via Neuro-Oncology and Neurosurgical Oncology, those mainly focused on imaging methods for tumor grade prediction and response and survival monitoring should be submitted via the Cancer Imaging and Image-directed Interventions specialty of Frontiers in Oncology, while submissions highlighting the potentialities of Artificial Intelligence at this clinical interface must be submitted to the Medicine and Public Health specialty of Frontiers in Artificial Intelligence.
The Topic Editors and the Editorial Team reserve the right to redirect manuscripts to the most appropriate section as necessary.
