AI-Driven Advanced Radiotherapy: Towards Personalized, Predictive and Adaptive Cancer Treatment
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 689
Special Issue Editors
Interests: treatment planning; low dose imaging; Monte Carlo simulation; deep learning applications in radiotherapy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has emerged as a transformative paradigm in healthcare and precision medicine by enabling computational models that represent key aspects of patient health. By incorporating data-driven AI and ML methods, these models can learn from multimodal patient data, enabling adaptive modeling, real-time monitoring, outcome prediction, and personalized clinical decision support.
This Special Issue aims to highlight the rapidly evolving applications of AI-driven technologies in radiotherapy, showcasing how these innovations can revolutionize cancer care. We welcome original research articles, reviews, and clinical studies that present novel AI and ML applications in areas such as adaptive radiotherapy, personalized outcome prediction, radiobiological response modeling, and clinical decision support. We are particularly interested in contributions that demonstrate how AI approaches can enhance clinical workflows, treatment precision, and patient outcomes, ultimately improving quality of life.
Building on AI- and machine learning-driven approaches, potential topics include, but are not limited to:
- Adaptive Radiotherapy
- Personalization of dose regimens.
- Adaptive treatment planning.
- Plan robustness analysis and uncertainty quantification.
- Outcome Prediction
- Predicting toxicity and tumor control probability.
- Modeling of immune response.
- Radiobiological Response Modeling
- Multi-scale biological modeling across molecular to organ levels.
- Simulation of tumor–immune microenvironment dynamics.
- Patient Motion and Organ Dynamics
- Real-time organ motion prediction and compensation.
- Modeling of inter- and intra-fraction anatomical deformation.
- Clinical Workflow and Decision Support
- AI-assisted selection of treatment strategies.
- Simulation of treatment pathways and clinical workflows.
- AI for QA and Commissioning
- Virtual patient models for end-to-end QA.
- Predictive machine and delivery system modeling for commissioning.
- Emerging Frontiers
- Radiopharmaceutical innovations.
- Population-level modeling for in silico clinical trials.
We eagerly anticipate your contributions that showcase the innovative applications of AI-driven approaches in advancing cancer radiotherapy.
Dr. Zhen Tian
Dr. Chaoqiong Ma
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 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. 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
- radiotherapy
- artificial intelligence
- machine learning
- deep learning
- outcome prediction
- treatment planning
- adaptive RT
- personalized RT
- radiomics
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