ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2025
Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.
Review in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- Evaluation of AI-based segmentation and synthetic CT generation for MRI-only prostate radiotherapy planning.Journal of applied clinical medical physics · 2026Article
- Dnq-unet: a two-level fusion framework for few-shot domain adaptation in cervical cancer CTV segmentation.Radiological physics and technology · 2026Article
- Deep learning-based computed tomography detection of early lymph node metastasis in head and neck cancer.Quantitative imaging in medicine and surgery · 2026Article
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- Quantifying intrafractional colon tumor motion on 1.5 T MR-linac cine-MRI and applying anisotropic residual-motion margins for SBRT.Radiation oncology (London, England) · 2026Observational
- Reply to comment on impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: A SIOP-RTSG radiotherapy committee.Clinical and translational radiation oncology · 2026Article
- Automated deep learning segmentation and planning for left-sided breast radiotherapy with minimised adaptations based on dose, TCP and NTCP criteria.Physics and imaging in radiation oncology · 2026Article
- Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.Physics and imaging in radiation oncology · 2026Article
- Comment on "Deep learning in CT-based organ-at-risk delineation for pediatric flank irradiation": Methodological and clinical considerations.Clinical and translational radiation oncology · 2026Article
- Deep ensemble optimized models for probabilistic CTV breast segmentation.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence-assisted radiotherapy for pelvic and abdominal malignancies: assessing feasibility in the context of Africa-specific risks.Frontiers in oncology · 2026Review
- Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.