Advanced Radiomics in Precision Oncology
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 31
Special Issue Editor
2. Department of Radiology, Paracelsus Medical University Salzburg, Salzburg, Austria
Interests: radiomics; radiology; medical and biomedical image processing; medical image analysis; nuclear medicine; oncology imaging; neuroendocrine tumors
Special Issue Information
Dear Colleagues,
In an era of exponentially growing digitalization and data-driven medicine, radiology is experiencing a paradigm shift. The future of medical imaging AI will be driven by artificial intelligence, image analytics and integration with big data serve at all levels. In this changing context, radiomics—and, more recently, deep radiomics—has been identified as one of the most promising methods for unleashing the quantitative potential inherent in routine medical imaging.
Traditional radiomics involves the systematic extraction of predefined quantitative features from imaging data, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography. Such features mathematically represent the tissue intensity and texture, shape and pattern heterogeneity within the images, converting the images into high-dimensional data. Deep radiomics adopts this philosophy and instead utilizes deep learning concepts to automatically generate complex image representations directly from raw imaging data, avoiding the need for handcrafted feature engineering and, in turn, supporting highly abstract visual patterns.
The significance of radiomics and deep radiomics is increasingly related to the paradigm shift to precision medicine. As non-invasive, reproducible whole-lesion methods, they are not affected by limitations related to invasive tissue sampling and interobserver variability. Along with machine learning, multi-omics data and clinical characteristics have been integrated into radiomics-based models for providing comprehensive tumor characterization, risk stratification, treatment response evaluation, and patient outcome prediction in various medical domains.
This Special Issue will identify current developments in radiomics and deep radiomics, covering technical developments, clinical applications and validation techniques, as well as integration with the field of artificial intelligence. Through considering interdisciplinary views, the current status and future direction of quantitative imaging in modern medicine are summarized.
Prof. Dr. Shahin Zandieh
Guest Editor
Manuscript Submission Information
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Keywords
- radiomics
- deep radiomics
- medical imaging
- artificial intelligence (AI)
- precision medicine
- quantitative imaging
- deep learning
- feature extraction
- image analytics
- big data
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