About this Research Topic
Gliomas are primary tumors of the central nervous system according to the WHO classification. They significantly impair the quality of life, and in higher malignancy grades, the life expectancy of affected patients. Despite recent advances in the pathological tumor differentiation by integrating their molecular characteristics and continuous development of new therapeutic approaches, the prognosis of higher WHO grade gliomas is still poor. Diagnostic imaging plays a pivotal role in glioma diagnosis, therapy planning, therapy evaluation, and detection of glioma recurrence. However, standard imaging methods have limited accuracy in the diagnosis and monitoring of gliomas.
Challenges for diagnostic imaging in glioma lie in differentiating residual vital tumors from therapy-associated changes, detecting recurrent disease, and identifying conversion of low-grade into high-grade tumors. Particularly, therapy with innovative agents results in equivocal imaging findings. Furthermore, there is a potential to improve the initial characterization of gliomas, the prediction of their prognosis, the planning of targeted therapies, and the assessment and prediction of patients’ functional outcome. New imaging approaches comprising advanced MRI techniques (including MR perfusion and spectroscopy, positron emission tomography, and functional MRI), artificial intelligence, and others may increase the significance and clinical benefit of diagnostic imaging in glioma.
This Research Topic thus welcomes authors to submit Original Research, Reviews, and Case Reports from clinical and preclinical standpoints that handle diagnostic imaging of gliomas from a neuroradiological or molecular imaging perspective. Manuscripts on artificial intelligence-based data mining and biomarker development for detection and prediction of glioma progression, and the use of advanced multimodal neuroimaging techniques, are also welcome.
This collection should focus on, but is not limited to, the following themes:
1. Diagnostic imaging in the evaluation of immune-modulating therapies of gliomas.
2. Assessment, accuracy, and clinical benefit of diagnostic imaging in the monitoring of glioma.
3. Radiomics and artificial intelligence imaging and image-guided planning of targeted surgery and radiation in glioma.
4. New imaging aspects in the assessment of tumor extent and the differentiation of therapy-associated changes from vital tumor.
5. Multimodal imaging in the assessment and prediction of glioma patients’ neurocognitive function.
6. Targeted molecular imaging of gliomas in preclinical models and reports showing gliomas mimicking other diseases.
Challenges for diagnostic imaging in glioma lie in differentiating residual vital tumors from therapy-associated changes, detecting recurrent disease, and identifying conversion of low-grade into high-grade tumors. Particularly, therapy with innovative agents results in equivocal imaging findings. Furthermore, there is a potential to improve the initial characterization of gliomas, the prediction of their prognosis, the planning of targeted therapies, and the assessment and prediction of patients’ functional outcome. New imaging approaches comprising advanced MRI techniques (including MR perfusion and spectroscopy, positron emission tomography, and functional MRI), artificial intelligence, and others may increase the significance and clinical benefit of diagnostic imaging in glioma.
This Research Topic thus welcomes authors to submit Original Research, Reviews, and Case Reports from clinical and preclinical standpoints that handle diagnostic imaging of gliomas from a neuroradiological or molecular imaging perspective. Manuscripts on artificial intelligence-based data mining and biomarker development for detection and prediction of glioma progression, and the use of advanced multimodal neuroimaging techniques, are also welcome.
This collection should focus on, but is not limited to, the following themes:
1. Diagnostic imaging in the evaluation of immune-modulating therapies of gliomas.
2. Assessment, accuracy, and clinical benefit of diagnostic imaging in the monitoring of glioma.
3. Radiomics and artificial intelligence imaging and image-guided planning of targeted surgery and radiation in glioma.
4. New imaging aspects in the assessment of tumor extent and the differentiation of therapy-associated changes from vital tumor.
5. Multimodal imaging in the assessment and prediction of glioma patients’ neurocognitive function.
6. Targeted molecular imaging of gliomas in preclinical models and reports showing gliomas mimicking other diseases.
Keywords: glioma, neuro-oncology, multimodal imaging, artificial intelligence, cognition
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.
