ReviewPhysics and imaging in radiation oncology2025
A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer.
Review in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Multimodality Multimask and Multitask Auto-Segmentation Network for Organs-at-Risk in Head and Neck Radiation Therapy.Advances in radiation oncology · 2026Article
- Multimodal Characterization of Radiation-Induced Optic Neuropathy after Proton Radiotherapy.Ophthalmology science · 2026Article
- Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.Technical innovations & patient support in radiation oncology · 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
- Refining imaging parameters for dual-energy cone-beam computed tomography in image-guided radiation therapy.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- The Evolving Role of Radiation Therapy Technologists in Head and Neck Cancer: A Narrative Review and Operational Framework.Journal of imaging · 2026Review
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Advances in the Diagnosis of Rheumatoid Arthritis-Associated Interstitial Lung Disease: Integrating Conventional Tools and Emerging Biomarkers.International journal of molecular sciences · 2026Review
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Development of nasopharyngeal carcinoma target delineation: from two-dimensional radiotherapy to adaptive precision radiotherapy.Journal of Cancer · 2026Review
- A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck.Frontiers in digital health · 2026Article
- Maintaining quality of scientific peer-review and publishing inPhysics and imaging in radiation oncology · 2026Article
- Multiscale Segmentation-Guided Diffusion Model for CBCT-to-CT Synthesis.Life (Basel, Switzerland) · 2025Article
- A global bibliometric and visualization study of the adaptive radiotherapy research landscape.Discover oncology · 2025Article
- Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review.Brain sciences · 2025Review
- Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.BMC cancer · 2025Article
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- Reducing radiomics errors in nasopharyngeal cancer via deep learning-based synthetic CT generation from CBCT.BMC medical imaging · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Adaptive radiotherapy (ART) may improve treatment quality by monitoring variations in patient anatomy and incorporating them into the treatment plan. This systematic review investigated the role of artificial intelligence (AI) in computed tomography (CT)-based ART for head and neck (H&N) cancer. Methods: A comprehensive search of main electronic databases was conducted until April 2024. Titles and abstracts were reviewed to evaluate the compliance with inclusion criteria: CT-based imaging for photon ART of H&N patients and AI applications. 17 original retrospective studies with samples sizes ranging from 37 to 239 patients were included. The quality of the studies was evaluated with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. Key metrics were examined to evaluate the performances of the proposed AI-methods. Results: Overall, the risk of bias was low. The average CLAIM score was 70%. A major finding was that generated synthetic CTs improved similarity metrics with planning CT compared to original cone-beam CTs, with average mean absolute error up to 39 HU and maximum improvement of 80%. Auto-segmentation provided an efficient and accurate option for organ-at-risk delineation, with average Dice similarity coefficient ranging from 80 to 87%. Finally, AI models could be trained using clinical and radiomic features to predict the effectiveness of ART with accuracy above 80%. Conclusions: Automation of processes in ART for H&N cancer is very promising throughout the entire chain, from the generation of synthetic CTs and auto-segmentation to predict the effectiveness of ART.
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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.