Artificial Intelligence in Radiation Therapy for Head and Neck Cancers: Prediction, Personalization, and Precision
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 5
Special Issue Editor
Special Issue Information
Dear Colleagues,
The management of head and neck cancer (HNC) remains clinically complex due to tumor heterogeneity, proximity to critical organs-at-risk (OARs), and the risk of significant treatment-related toxicities. With the increasing availability of high-dimensional data from imaging, dosimetry, and clinical records, artificial intelligence (AI)—including machine learning (ML) and deep learning (DL)—is emerging as a transformative tool to support decision-making in radiation oncology.
We are pleased to invite you to contribute to this Special Issue, which focuses on the application of AI in predicting treatment outcomes, optimizing radiotherapy planning, and identifying patients at risk for acute and late toxicities such as xerostomia, mucositis, or dysphagia. We particularly welcome studies that highlight the integration of multimodal data (e.g., imaging, dosiomics, radiomics, genomics) into predictive frameworks, as well as research on model interpretability and clinical translation.
This Special Issue aims to highlight recent advances in AI-based prediction and personalization in radiation therapy for HNC, aligning with the journal’s scope on translational, clinical, and technological innovations in oncology. Submissions may include retrospective and prospective studies, model validation papers, algorithm development, or reviews summarizing the current landscape.
In this Special Issue, original research articles and review papers are welcome. Suggested themes include, but are not limited to, the following:
- AI-driven toxicity prediction models (e.g., for xerostomia, dysphagia, mucositis);
- Machine learning for treatment outcome forecasting in HNC;
- Dosiomics and radiomics for organ-at-risk modeling;
- Multi-objective optimization for radiotherapy planning;
- Clinical validation of deep learning models in radiation oncology;
- Multimodal data integration (CT, PET, MRI, genomic, clinical);
- Explainable AI and uncertainty quantification in medical decision-making;
- Personalized radiotherapy workflows enhanced by AI;
- Synthetic data and augmentation strategies in model training;
- Limitations and ethical considerations in AI-based HNC management.
We look forward to receiving your contributions and building a high-impact collection that advances the role of artificial intelligence in radiation therapy for head and neck cancers.
Dr. Meysam Tavakoli
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
- head and neck cancer
- radiation therapy
- artificial intelligence
- machine learning
- deep learning
- toxicity prediction
- radiomics
- dosiomics
- outcome modeling
- personalized radiotherapy
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