Integrating Radiomics and Radiotherapy in Cancer Care: Quantitative Imaging and Efficacy Assessment
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 30 May 2026 | Viewed by 2
Special Issue Editor
Special Issue Information
Dear Colleagues,
Medical imaging remains a cornerstone in the diagnosis, staging, and management of cancer, particularly in radiation oncology, where imaging plays a central role in treatment planning, guidance, and response evaluation. In recent years, radiomics—the extraction of large numbers of quantitative features from standard-of-care medical images—has emerged as a powerful approach to decode tumor biology, heterogeneity, and microenvironmental complexity beyond what can be visually perceived. These image-derived biomarkers hold great potential to enhance diagnosis, prognostication, and prediction of treatment response, thereby contributing to the advancement of precision radiotherapy.
This Special Issue of Cancers aims to provide a comprehensive overview of recent advances in radiomics and its applications in radiation oncology, from methodological developments to clinical translation and efficacy assessment. We welcome original research articles, systematic reviews, and high-impact perspectives that explore the integration of radiomics into the radiotherapy workflow—including treatment planning, adaptive radiotherapy, dose optimization, and outcome prediction.
Topics of interest include (but are not limited to) the following:
- Development and validation of quantitative imaging biomarkers in radiotherapy;
- Radiomics for tumor characterization, treatment planning, and adaptive workflows;
- Integration of radiomics with genomics, pathology, and clinical data (radiogenomics and multi-omics);
- AI and machine learning methods for radiomic feature analysis and model development.
Through this Special Issue, Cancers seeks to bridge quantitative imaging research and clinical radiotherapy, fostering a multidisciplinary dialogue among radiologists, radiation oncologists, medical physicists, and data scientists. Our goal is to highlight the clinical potential of radiomics as a data-driven tool to advance individualized cancer care. We warmly invite your contributions to this timely and impactful Special Issue.
Prof. Dr. Savino Cilla
Guest Editor
Manuscript Submission Information
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Keywords
- radiomics
- radiogenomics
- radiation oncology
- imaging biomarkers
- predictive models
- machine learning
- precision radiotherapy
- quantitative imaging
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