About this Research Topic
Recent progress in artificial intelligence (AI) and advanced imaging techniques have brought about exciting diagnostic and therapeutic opportunities in oncology. More and more state-of-the-art CT, PET, and MRI imaging techniques are being applied to tumor imaging, and new interdisciplinary field such as MR-LINAC is booming. We feel there's an urgent need to organize a Research Topic to highlight the application of AI and imaging for various oncology branches such as radiation therapy, as we are witnessing an accelerated integration of imaging and therapy to improve the care of cancer patients.
Imaging has also been the backbone of the oncology world. It is used for diagnosis as well as planning. MRI has a superior soft tissue contrast but lacks the electron density information for the dose calculation, alternative approaches use synthetic CT for dose calculation. The development of the MRI-guided system and online adaptation can be considered as the bridge between many specialties, starting from learning about MRI safety by the therapy physicist to the use of AI for robust planning. Active research is done to improve the images (advanced MRI techniques, Nuclear medicine and PET-MRI, synthetic images, CBCT for dose calculation) for clinical workflows. However, all these topics have a lot of room for exploration.
In this Research Topic, we welcome research articles, and case reports focusing, but not limited to:
- AI applications in imaging improvement, robust planning, image improvement, or clinical outcome prediction.
- improving the clinical workflow
- clinical trials (eg: adaptive planning, segmentation, synthetic CT, etc.)
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which 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 journal.
Imaging has also been the backbone of the oncology world. It is used for diagnosis as well as planning. MRI has a superior soft tissue contrast but lacks the electron density information for the dose calculation, alternative approaches use synthetic CT for dose calculation. The development of the MRI-guided system and online adaptation can be considered as the bridge between many specialties, starting from learning about MRI safety by the therapy physicist to the use of AI for robust planning. Active research is done to improve the images (advanced MRI techniques, Nuclear medicine and PET-MRI, synthetic images, CBCT for dose calculation) for clinical workflows. However, all these topics have a lot of room for exploration.
In this Research Topic, we welcome research articles, and case reports focusing, but not limited to:
- AI applications in imaging improvement, robust planning, image improvement, or clinical outcome prediction.
- improving the clinical workflow
- clinical trials (eg: adaptive planning, segmentation, synthetic CT, etc.)
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which 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 journal.
Keywords: AI, MRI, CT, PET, Segmentation, oncology, clinical outcomes
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.
