About this Research Topic
Since its first definition by Lambin in 2012, Radiomics saw an impressive raise and is currently one of the cutting-edge fields of imaging research. The possibility to mine quantitative data from medical imaging represents an invaluable occasion to improve our knowledge in several diseases and to offer patient-tailored treatments. The huge amount of data extracted from medical imaging by radiomic analysis requires adequate systems to elaborate them. Among those, artificial intelligence (AI) surely represents the most powerful and attractive option to translate radiomics analysis into predictive models that can have an impact on daily clinical practice. However, there are still challenges to be solved before the introduction of radiomics and AI into clinical practice, such as the lack of standardization and reproducibility.
This Research Topic aims to collect papers exploring the use of radiomics and artificial intelligence (both machine learning and deep learning) in the field of nuclear medicine and radiology, including all their possible applications for different imaging methods.
We are interested in collecting original articles or reviews focusing on radiomic analysis and artificial intelligence in oncological and non-oncological applications of medical imaging. Moreover, we are also interested in feasibility papers, aiming to improve the standardization and reproducibility of radiomic analysis.
This Research Topic aims to collect papers exploring the use of radiomics and artificial intelligence (both machine learning and deep learning) in the field of nuclear medicine and radiology, including all their possible applications for different imaging methods.
We are interested in collecting original articles or reviews focusing on radiomic analysis and artificial intelligence in oncological and non-oncological applications of medical imaging. Moreover, we are also interested in feasibility papers, aiming to improve the standardization and reproducibility of radiomic analysis.
Keywords: Radiomics, Artificial Intelligence, Medical Imaging, Radiomic Analysis, Machine Learning, Deep Learning.
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.
