Radiomics in Cancer
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".
Deadline for manuscript submissions: 1 August 2025 | Viewed by 150
Special Issue Editor
Special Issue Information
Dear Colleagues,
Radiomics is an innovative and rapidly expanding field that combines medical imaging, computer science, and data analysis to improve cancer care. By extracting detailed quantitative data from medical images, radiomics provides unique insights that can guide personalized treatment plans. This approach requires collaboration among radiologists, imaging scientists, and data scientists, each bringing a specialized expertise to navigate the complex workflow. The radiomics process typically follows a structured sequence: tumor segmentation, where the tumor is outlined; image preprocessing, which standardizes images for analysis; feature extraction, which identifies relevant characteristics; and finally, model development and validation, ensuring that results are reliable. Each step is essential to produce accurate, actionable insights.
One of the most promising aspects of radiomics is its potential to transform the diagnosis, staging, and management of cancer. Through advanced imaging analysis, radiomics can help in diagnosing and precisely staging different cancer types, enabling clinicians to make well-informed decisions. It can also predict the likelihood of metastasis, allowing doctors to consider more targeted treatments, and estimate patient survival, which can help with treatment planning and patient counseling. Additionally, radiomics offers a way to evaluate the effectiveness of therapies over time, potentially providing early indications of treatment response. These capabilities have significant implications for improving patient outcomes and tailoring interventions to individual patients’ needs.
Despite the enthusiasm surrounding radiomics, there are challenges and limitations that must be addressed to fully integrate it into routine clinical practice. Physicians and researchers need to be aware of potential pitfalls, such as variability in imaging techniques, data quality, and the reproducibility of results, which can impact the reliability of radiomic models. There is also the need for rigorous validation across diverse patient populations to ensure that models are generalizable. This Special Issue aims to explore these current limitations in radiomics for cancer, assess the state of the art, and discuss future directions for advancing the field, with the ultimate goal of refining and expanding radiomics to enhance cancer therapies and patient care.
Dr. Isabelle M. Germano
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- radiomic
- medical imaging
- personalized treatment
- diagnosis
- staging
- management
- targeted treatment
- patient outcome
- clinical practice
- cancer therapies
- patient care
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