Integration of Bioinformatics and Medical Imaging for Cancer Biology

The integration of bioinformatics and medical imaging, often referred to as radiogenomics, has emerged as a powerful and transformative approach in cancer biology. This interdisciplinary field seeks to bridge the gap between the molecular landscape of tumors and their observable imaging phenotypes, linking micro-level genomic and biological alterations with macro-level clinical and radiological characteristics. By combining high-throughput multi-omics data—including genomics, transcriptomics, proteomics, miRNA profiling, and epigenetic information—with quantitative imaging features derived from MRI, CT, PET, and other imaging modalities, researchers can gain deeper insight into tumor heterogeneity, progression, and biological behavior.This integrated approach has strong potential to improve tumor classification, biomarker discovery, treatment response prediction, and the development of personalized therapeutic strategies. It also supports advances in related areas such as drug discovery, biomedicine and pharmacology, aging research, and precision oncology. As cancers are highly complex and dynamic diseases, the fusion of imaging and molecular data provides a more comprehensive framework for understanding disease mechanisms and guiding clinical decision-making.Recent progress in artificial intelligence, machine learning, deep learning, and explainable AI has further accelerated research in this area. In addition, contributions from optimization methods, applied mathematics, advanced data structures, and quantum computing are opening new possibilities for modeling, integrating, and interpreting complex biomedical datasets. These computational tools are essential for developing robust, scalable, and clinically relevant methods that can translate radiogenomic insights into real-world cancer care.This Research Topic welcomes original research articles, review papers, and case studies that explore the integration of bioinformatics and medical imaging in cancer biology. Submissions may focus on AI-driven radiogenomics, multi-omics and imaging data fusion, predictive modeling, interpretable and explainable frameworks, optimization-based methods, and other innovative computational strategies that advance cancer diagnosis, prognosis, and therapy.Possible Themes for Submissiono Radiogenomics and imaging-genomics correlations in cancero Multi-omics and medical imaging data integrationo AI, machine learning, and deep learning in cancer imaging biologyo Explainable AI for cancer diagnosis and prognosiso Imaging biomarkers for treatment response and survival predictiono Computational models for tumor heterogeneity and evolutiono Precision oncology and personalized cancer therapeuticso Applications in drug discovery, pharmacology, and aging-related cancerso Optimization, mathematical modeling, and quantum approaches in biomedical data analysiso Clinical case studies using integrated bioinformatics and imaging workflows Topic editors: Aimin Li, Tapas Si Source: https://www.frontiersin.org/research-topics/79779/integration-of-bioinformatics-and-medical-imaging-for-cancer-biology