Radiomics in Cancer Imaging: Theory and Applications in Solid Tumours
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 454
Special Issue Editors
Interests: artificial intelligence; computational imaging; computer vision; image processing; medical image analysis; radiomics
Special Issues, Collections and Topics in MDPI journals
Interests: nuclear medicine; image-based diagnostics; artificial intelligence; PET/CT; SPECT; SPECT/CT; radiomics; oncology; neurodegenerative disorders
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Radiomics is an approach to medical imaging that is aimed at extracting quantitative features that would otherwise go unnoticed to the human eye. By leveraging artificial intelligence and machine learning algorithms, radiomics can generate prediction models capable of assisting medical professionals in clinical decision-making. In this context, radiomics has attracted increasing worldwide interest as a potential tool for diagnosis, risk stratification, and treatment planning. Yet the translation of radiomics into clinical practise still faces some major hurdles, such as standardisation and reproducibility problems, lack of data for training the models, man-machine interaction issues (e.g., interpretability and willingness to accept the results of an algorithm), as well as legal and ethical issues. Radiomics is also a strongly multidisciplinary discipline, and its success depends a great deal on the cooperation of experts from different fields, including physicians, biologists, mathematicians, statisticians, computer scientists, and engineers.
This Special Issue wants to provide a forum to discuss challenges, discoveries, and opportunities of radiomics in the field of solid tumours. We welcome both methodological and application-oriented contributions. We encourage the submission of original research articles, reviews, and comparative evaluations.
Dr. Francesco Bianconi
Dr. Barbara Palumbo
Guest Editors
Manuscript Submission Information
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Keywords
- radiomics for the diagnosis, prognostication and treatment planning of solid tumours
- methods, translational research and clinical applications
- conventional and deep learning radiomics
- interpretability of radiomics features and prediction models
- image processing (including acquisition, segmentation and feature extraction)
- imaging methods (including CT, MRI, PET, SPECT and US)
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