Machine Learning in Radiation Oncology - Volume II

This Research Topic represents the second volume of "Machine Learning in Radiation Oncology." Our community made significant strides as documented in the first volume here and we are eager to build on that success with this new collection.

Machine learning (ML) as a pillar of artificial intelligence, continues to revolutionize radiation oncology, with unprecedented advances made since our first collection. As clinical workflows generate richer and more complex datasets, ML algorithms now offer deeper insights into treatment optimization, precision, and patient care. From human-engineered features and deep learning to federated, generative and explainable AI, the integration of ML into radiation oncology is transforming every stage of the patient journey, from diagnosis and treatment planning to adaptive therapy and follow-up.

Building on the success of Volume I, Volume II of this Research Topic invites the next wave of high-impact research and expert reviews, spotlighting both established ML applications and emerging frontiers. For this new edition, we are especially eager to feature AI-based motion management in IGRT (Image-Guided Radiation Therapy), a game-changing, rapidly maturing area enabling precise, real-time adaptation to patient and tumor motion, and driving improvements in safety and outcomes.

We welcome manuscripts covering innovations and challenges throughout the RT workflow, including:
• Initial treatment decision-making: ML for patient selection, risk stratification, and personalized dose prescription.
• Treatment planning and preparation: Advances in image segmentation, dosimetric planning, multi-modal image registration, and data harmonization.
• Treatment setup and delivery: AI-based motion management in IGRT, novel algorithms for target tracking, motion prediction, intrafraction adaptation, markerless motion detection, and workflow automation.
• New frontiers in IGRT: Broader innovations in image guidance, adaptive RT, beam delivery, and workflow integration.
• Response assessment and follow-up: ML for treatment response prediction, toxicity risk modeling, survivorship monitoring, and longitudinal data analytics.
• Clinical practice and QI: Robustness/validation, clinical implementation challenges, regulatory considerations, and explainable AI for decision support.
• Validation of AI/ML methods in real-world clinical cohorts: Transparent reporting and model interpretability, perspectives from multidisciplinary teams, including physicists, clinicians, and data scientists, and ethical, legal, and operational challenges in clinical deployment of AI in RT.

As the discipline matures, we hope this collection will further unite academy, industry, and clinical teams to showcase the latest advances and responsibly accelerate the translation of machine learning breakthroughs into patient benefit.

Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Machine Learning, Artificial Intelligence, Radiation Oncology, Image-Guided Radiation Therapy, Motion Management, Deep Learning, Treatment Planning, Clinical Validation, Adaptive Radiotherapy, Predictive Modeling, Explainable AI, Generative AI.

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.