Artificial Intelligence-Assisted Radiomics in Cancer

Dear Colleagues,

Medical images contain a massive amount of information that no human can fully appreciate and quantify. The images are formed by computing complex biological signals into visible shapes and forms, discarding potentially rich biological information that is not visible. Radiomics aims to develop the next-generation quantitative decision support system by extracting new quantitative information from multidimensional imaging data by applying high-order statistics and artificial intelligence (AI). The recent explosive growth of artificial intelligence research and development for computer-aided detection (CADe) and diagnosis (CADx) is based on three-dimensional pattern recognition; thus, perhaps we have just “scratched the surface of imaging AI”. Radiomics research will help us explore precise cancer diagnosis and the progression of disease at a deeper individual biological level. Much progress has been made in radiomics, and the rate of advances is accelerating. Undoubtedly, various AI tools and concepts in data science will play significant roles in accelerating advances in radiomics to reach the goal of precision medicine. 

This Special Issue presents a unique opportunity to bring together diverse perspectives from academics, industry professionals, and policymakers. We are open to a wide range of contributions, including original research articles, comprehensive reviews, and other related publications. By facilitating the timely communication of multidisciplinary research results, we aim to help form a global community of experts in AI radiomics. The topical areas may include (but are not limited to) the following:

• Artificial intelligence tools and deep learning;
• Generative artificial intelligence;
• Segmentation;
• Image labeling;
• Image denoising;
• Image normalization;
• Feature extraction;
• Strategy for standardization;
• High-order statistics;
• Integration of EHR data; 
• Integration with proteomics and genomics;
• Clinical adoption strategy and workflow optimization;
• Training and education.

I look forward to receiving your contributions.

Prof. Dr. Seong K. Mun
Guest Editor

Manuscript Submission Information

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• radiomics
• learning tools
• data normalization
• segmentation
• artificial intelligence
• quantitative imaging
• multi-dimensional feature analysis
• statistical models
• precision medicine