Radiomics and Artificial Intelligence in Oncology: From Methodological Foundations to Personalized Clinical Decision-Making
A Special Issue of Journal of Personalized Medicine (ISSN 2075-4426) belonging to the section "Precision Oncology".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 208
Editors
Interests: computed tomography; magnetic resonance imaging; contrast media; oncologic imaging; cardiac imaging; imaging informatics
Special Issues, Collections and Topics in MDPI journals
Interests: thoracic imaging; lung cancer; interstitial lung disease; quantitative imaging; chest MRI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Radiology has consistently positioned itself at the forefront of innovation, not only through advances in imaging hardware but also through the early adoption and development of computational methodologies. In oncology, this dual trajectory has become particularly relevant, as the demand for precision medicine has exposed the limitations of purely qualitative image interpretation. Radiomics and artificial intelligence (AI) offer a complementary paradigm, enabling the extraction of quantitative imaging biomarkers and supporting more objective, reproducible and scalable clinical decision-making.
The integration of AI in radiology has followed a progressive and anticipatory path compared with other medical fields. From early computer-aided detection systems to radiomics, machine learning and deep learning to the recent emergence of large language models and foundation models, radiological research has consistently led methodological innovation. This evolution has now reached a phase of increasing maturity, with AI-based tools impacting virtually every step of the clinical workflow, from image acquisition and reconstruction to analysis and reporting. In oncology, this convergence is both timely and necessary, as it enables a shift toward personalized clinical decision-making while addressing key challenges such as variability, data heterogeneity and the need for integrative biomarkers.
This Special Issue aims to collect the most recent advances in radiomics and AI in oncologic imaging, spanning methodological developments and their translation into personalized clinical decision-making. We consider contributions addressing algorithm development, the validation and standardization of radiomic features, multimodal data integration, clinical implementation and the impact of AI-driven tools across the oncologic imaging workflow.
Topics of interest for publication include, but are not limited to, the following:
- radiomics and imaging biomarkers
- machine learning and deep learning in oncologic imaging
- foundation models and large language models in radiology
- multimodal and multi-omics integration
- AI for image acquisition, reconstruction and reporting
- clinical validation and translational studies
- standardization, reproducibility and regulatory aspects of AI in oncology.
Dr. Lorenzo Faggioni
Dr. Chiara Romei
Guest Editors
Manuscript Submission Information
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Keywords
- radiomics
- imaging biomarkers
- artificial intelligence
- machine learning
- oncologic imaging
- precision medicine
- deep learning
- large language models
- foundation models
- natural language processing
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