Despite being one of the less commonly diagnosed cancers, bone cancers still carry unique risks of mortality and malignancy alike. Imaging is a mandatory step for diagnosis, classification and mapping of bone cancers and requires the continuous attention of further research. There are currently a number of commonly implemented imaging techniques used to identify and monitor cancers of the bones, each bringing their own advantages and disadvantages. Further research will uncover new avenues for optimization of these techniques, and demonstrate how technological advancement offers radiologists opportunities to refine and improve diagnostic and prognostic criteria which helps in the management of malignant bone neoplasms.
In clinical practice, musculoskeletal radiologists have access to a number of bone imaging techniques which range from conventional radiology (which represents the mainstay of bone tumors assessment) to well more advanced techniques like computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and bone scans that uses small doses of radioactive materials like gallium or technetium. A recent technological advancement becoming more prominent in radiology is the incorporation of artificial intelligence (AI) or deep learning software to further characterize images acquired with the above-mentioned imaging techniques. As computing technology becomes more and more advanced, its ability to learn and identify patterns or trends in the same manner as a human open up new opportunities to increase the speed of identifying cancerous tissues from imaging data of the bones automatically.
This Research Topic aims to attract manuscript submissions which detail how aforementioned imagining techniques (i.e. x-ray, CT, MRI, PET) may be updated to improve the abilities of radiologists in the identification, classification, and/or staging of cancers of the bone. Manuscripts demonstrating how AI or deep learning neural networks can support radiologists in identifying cancers of the bones are also welcome. We also invite submissions demonstrating how novel imaging methodologies can improve diagnosis of bone cancers.
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Despite being one of the less commonly diagnosed cancers, bone cancers still carry unique risks of mortality and malignancy alike. Imaging is a mandatory step for diagnosis, classification and mapping of bone cancers and requires the continuous attention of further research. There are currently a number of commonly implemented imaging techniques used to identify and monitor cancers of the bones, each bringing their own advantages and disadvantages. Further research will uncover new avenues for optimization of these techniques, and demonstrate how technological advancement offers radiologists opportunities to refine and improve diagnostic and prognostic criteria which helps in the management of malignant bone neoplasms.
In clinical practice, musculoskeletal radiologists have access to a number of bone imaging techniques which range from conventional radiology (which represents the mainstay of bone tumors assessment) to well more advanced techniques like computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and bone scans that uses small doses of radioactive materials like gallium or technetium. A recent technological advancement becoming more prominent in radiology is the incorporation of artificial intelligence (AI) or deep learning software to further characterize images acquired with the above-mentioned imaging techniques. As computing technology becomes more and more advanced, its ability to learn and identify patterns or trends in the same manner as a human open up new opportunities to increase the speed of identifying cancerous tissues from imaging data of the bones automatically.
This Research Topic aims to attract manuscript submissions which detail how aforementioned imagining techniques (i.e. x-ray, CT, MRI, PET) may be updated to improve the abilities of radiologists in the identification, classification, and/or staging of cancers of the bone. Manuscripts demonstrating how AI or deep learning neural networks can support radiologists in identifying cancers of the bones are also welcome. We also invite submissions demonstrating how novel imaging methodologies can improve diagnosis of bone cancers.
Important Note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.