ReviewQuantitative imaging in medicine and surgery2021
Artificial intelligence in image-guided radiotherapy: a review of treatment target localization.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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
17 citing papers in PubMed.
- Risk stratification and cross-center prediction of thoracoabdominal 6D setup errors.BMC cancer · 2026Article
- Impact of daily volumetric imaging on target tracking without fiducial markers during robotic treatment of pancreas.Physics in medicine and biology · 2026Article
- FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Head and Neck Radiotherapy and Dentomaxillofacial Diagnostic Imaging: Biological Interactions and Protective Approaches.Biomedicines · 2025Review
- AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits.Radiation oncology (London, England) · 2025Article
- Advancing Hepatocellular Carcinoma Therapy with Next-Generation Molecular and Immunotherapeutics.Journal of hepatocellular carcinoma · 2025Review
- Artificial intelligence-based motion tracking in cancer radiotherapy: A review.Journal of applied clinical medical physics · 2024Review
- Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.Quantitative imaging in medicine and surgery · 2024Review
- Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- Going from 3D/3D to 2D/3D registration for noncoplanar setup verification in intracranial single-isocenter multiple-target hypofractionated stereotactic radiotherapy: comparison between kilo-voltage/mega-voltage image pairs and noncoplanar cone-beam computed tomography.Quantitative imaging in medicine and surgery · 2023Article
- Article
- Emerging technologies for cancer therapy using accelerated particles.Progress in particle and nuclear physics · 2023Article
- Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.Quantitative imaging in medicine and surgery · 2023Article
- Editorial: Image-guided particle therapy.Frontiers in oncology · 2023Article
- What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology.Journal of personalized medicine · 2022Article
- Novel-view X-ray projection synthesis through geometry-integrated deep learning.Medical image analysis · 2022Article
- Historical Progress of Stereotactic Radiation Surgery.Journal of medical physicsReview
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.
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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.