Much of the emphasis in discussions about personalized medicine has been focused on the molecular characterization of tissue samples using microarray technology. However, as genetic differ between and within tumors and are quite heterogeneous, these techniques are limited.
Imaging is noninvasive and is often used in routine clinical practice for disease diagnosis, treatment, and prognosis. Imaging is useful to guide disease therapy by providing a more comprehensive view of the entire lesion and it can be used on an ongoing basis to monitor lesion growth and progression or its response to treatment. The imaging includes but not limited to ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET).
Radiomics refers to the conversion of images to high dimensional data and the subsequent mining for 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 to the genomic level.
With advanced artificial intelligence methods, especially deep learning, data processing, feature extraction and data integration have been greatly improved. The topic is about artificial intelligence methods in biomedical images and genomics data for disease diagnosis, treatment, and prognosis, as listed here:
• Biomarker identification from biomedical images to predict disease diagnosis, treatment, and prognosis
• Radiogenomics/image-omics in identifying imaging biomarkers associated with molecular characteristics of the disease.
• Machine learning/deep learning methods in biomedical imaging or genomics for disease detection and precision medicine.
• Prediction of histological characteristics of disease based on biomedical imaging.
• Integration of radiomics and genomics features for disease diagnosis, prognosis, and prediction medicine
• Multimodality images or multi-omics data integration methods