AI-Driven Predictive Modeling in Radiation Therapy: Toward Precision and Personalized Clinical Applications
A special issue of Cancers (ISSN 2072-6694).
Deadline for manuscript submissions: 20 February 2026 | Viewed by 50
Special Issue Editor
Interests: radiotherapy in brain tumor; liver tumor; breast cancer; lymphoma; stereotactic brain radiosurgery; SRS; stereotactic body radiation therapy; SBRT
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue highlights the transformative role of artificial intelligence (AI) and predictive modeling in modern radiation therapy, with a particular emphasis on clinical relevance and realworld application. As oncology moves toward more precise and patient-centered treatment paradigms, AI-driven tools—such as machine learning, radiomics, and dosiomics—are revolutionizing how clinicians plan, adapt, and evaluate radiotherapy.
We invite submissions that demonstrate the clinical utility of AI in enhancing radiation treatment outcomes, particularly those supported by real-world cases, retrospective analyses, or prospective implementation studies. We strongly encourage interdisciplinary contributions from radiation oncologists, medical physicists, biomedical engineers, data scientists, and health informaticians.
Topics of interest include, but are not limited to, the following:
- Development and validation of predictive models for treatment outcomes and toxicity;
- Clinical integration of AI in contouring, planning, and quality assurance workflows;
- Multimodal data fusion (e.g., imaging, genomics, and dosimetry) for personalized decision support;
- AI-driven adaptive radiotherapy based on patient response or imaging changes;
- Explainable AI (XAI) for enhancing transparency in clinical adoption;
- Case studies of AI application in routine radiotherapy practice;
- Cost-effectiveness, workflow optimization, and ethical considerations in AI deployment;
- Collaborative platforms and infrastructure supporting AI integration in oncology.
This Special Issue aims to serve as a cross-disciplinary platform that bridges technical innovation with clinical practice. By sharing the practical applications, challenges, and successes of AI-based predictive modeling, we seek to foster meaningful collaborations between oncologists, engineers, and data scientists working to shape the next generation of radiation therapy.
Dr. Yujie Huang
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.
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. Cancers 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 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
- AI-based radiotherapy
- predictive modeling in oncology
- personalized radiation therapy
- radiomics and dosiomics
- adaptive radiotherapy
- treatment outcome prediction
- explainable AI in medicine
- multimodal data integration
- clinical decision support systems
- precision cancer treatment
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