Clinical Diagnosis and Treatment in Oncology: Radiotherapy and Imaging Analysis
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Oncology".
Deadline for manuscript submissions: 20 July 2026 | Viewed by 12
Special Issue Editors
Interests: hypofractionation; stereotactic radiotherapy; oligometastases; prostate cancer; lung cancer; pe-diatric radiotherapy
Special Issues, Collections and Topics in MDPI journals
Interests: nuclear medicine; lung cancer; radiomics; metabolomics; imaging
Special Issues, Collections and Topics in MDPI journals
Interests: stereotactic radiotherapy; prostate cancer; lung cancer; central nervous system tumors; spine; oligometastases
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are delighted to announce a Special Issue dedicated to "Clinical Diagnosis and Treatment in Oncology: Radiotherapy and Imaging Analysis".
This issue aims to bring together cutting-edge research on the integration of advanced imaging technologies with radiotherapy to enhance precision oncology. Modern cancer care increasingly relies on personalized approaches, where imaging plays a pivotal role not only in diagnosis but also in treatment planning, response assessment, and long-term monitoring.
Particular emphasis will be placed on quantitative imaging methods, radiomics, metabolomics, and artificial intelligence applications that can extract clinically relevant information from imaging data. These approaches enable clinicians to better characterize tumor biology, predict treatment response, and optimize radiotherapy protocols to maximize efficacy while minimizing toxicity to healthy tissues.
We welcome submissions of original research, clinical studies, and comprehensive reviews focusing on topics such as imaging biomarkers, radiomics and machine learning, image-guided radiotherapy, adaptive treatment strategies, and multidisciplinary approaches to personalized cancer care. Contributions that advance the understanding of how imaging and radiotherapy can be combined to improve patient outcomes are especially encouraged.
This Special Issue provides an exciting opportunity to share innovative strategies and translational research that bridge imaging science and clinical oncology, ultimately supporting the development of more precise, effective, and patient-centered cancer treatments.
Dr. Giuseppe Ferrera
Dr. Pierpaolo Alongi
Dr. Francesco Cuccia
Guest Editors
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 short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
- radiomics
- metabolic imaging
- radiotherapy
- predictive factors
- response models
- PET-CT
- artificial intelligence
- biomarkers
- machine-learning
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