Editorial on the Research Topic Biomedical image or genomic data characterization and radiogenomic/ image-omics Precision medicine has emerged as a practical solution for disease care thanks to advances in high-throughput data generation and analysis. Much of the emphasis in discussions about precision medicine or personalized medicine has been focused on the molecular characterization of tissues. However, as genetics differ between and within tumors and are quite heterogeneous, molecular

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... terizations are limited. Furthermore, there is no easy methodology yet to unravel why tumors with similar characteristics respond differently to a targeted therapy. Imaging is relatively noninvasive and is often used in routine clinical practice for disease diagnosis, treatment, and prognosis. Medical imaging can provide a comprehensive view of entire tumor lesions; it is commonly used in clinical practice to monitor the progress of the cancer during treatment. The imaging includes but is not limited to ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Radiomics refers to the conversion of images to high-dimensional data and subsequent mining for the characterization of biology and ultimately to improve disease management for patients. Radiogenomics investigates relationships between imaging features and genomics, which represents the correlation between the anatomical-histological level and the genomic level. With advanced artificial intelligence methods, especially deep learning, data processing, feature extraction and data integration, medical image-or genomic databased precision medicine has been greatly improved. There are 15 papers in this Research Topic: "Biomedical image or genomic data characterization and radiogenomic/imageomics." The articles focus on machine learning methods-based biomarker identification