AI-Driven Advanced Radiotherapy: Towards Personalized, Predictive and Adaptive Cancer Treatment

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:

1. Adaptive Radiotherapy
   1. Personalization of dose regimens.
   2. Adaptive treatment planning.
   3. Plan robustness analysis and uncertainty quantification.
2. Outcome Prediction
   1. Predicting toxicity and tumor control probability.
   2. Modeling of immune response.
3. Radiobiological Response Modeling
   1. Multi-scale biological modeling across molecular to organ levels.
   2. Simulation of tumor–immune microenvironment dynamics.
4. Patient Motion and Organ Dynamics
   1. Real-time organ motion prediction and compensation.
   2. Modeling of inter- and intra-fraction anatomical deformation.
5. Clinical Workflow and Decision Support
   1. AI-assisted selection of treatment strategies.
   2. Simulation of treatment pathways and clinical workflows.
6. AI for QA and Commissioning
   1. Virtual patient models for end-to-end QA.
   2. Predictive machine and delivery system modeling for commissioning.
7. Emerging Frontiers
   1. Radiopharmaceutical innovations.
   2. 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

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