ReviewTranslational cancer research2026
Artificial intelligence applications in adaptive radiotherapy-a narrative review.
Review in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Fast fourier transform based spectral features for machine learning prediction of gamma passing rates in virtual VMAT PSQA: A feasibility study.Journal of applied clinical medical physics · 2026Article
- Review
- Evaluation of an interpretable deep-learning model for the automated plan review of intensity-modulated radiation therapy.Frontiers in oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Online adaptive radiotherapy (ART) is an emerging tool to adapt radiotherapy plans to interfraction changes during the treatment course, reducing dose to organs at risk (OARs) while ensuring adequate dose to the tumor volume. Artificial intelligence (AI) can be a promising tool to support online ART techniques given that daily treatment changes can be resource- and time-intensive. With increasing integration of AI into radiation treatments, this literature review summarizes current applications and promising future directions of AI for online ART. Methods: A comprehensive search of databases (EMBASE, MEDLINE, PsycInfo, PubMed, CINAHL, CENTRAL, Scopus, and Web of Science, 2015-2025) was performed. Examples of search terms include "artificial intelligence", "synthetic image generation", "autosegmentation", and "adaptive radiotherapy". Selected studies were peer-reviewed studies that describe, validate, or evaluate an AI tool for any online ART-related function in the domains of decision support, imaging, contouring, planning, and quality assurance (QA). Key Content and Findings: AI applications for online ART can be divided broadly into three domains: image registration, auto-contouring of tumor volumes and OARs, and ART decision support through dose and anatomical change prediction. New deep-learning based synthetic computed tomography (CT) generation using architectures like generative adversarial networks have enabled magnetic resonance imaging (MRI)-guided adaptive workflows. AI-driven deformable image registration (DIR) has also been shown to demonstrate alignment between planning and on-treatment imaging. Similarly, AI-driven enhancement of cone-beam CTs has improved their resolution, improving spatial information valuable for segmentation of tumor volumes. Auto-segmentation using U-Nets, ResNets, and one-shot learning has substantially reduced contouring time and approaches expert performance for many organs at risk (OARs), though challenges persist for small structures and evolving gross tumor volume (GTV)/clinical target volume (CTV) volumes. AI-based dose prediction and personalized plan optimization demonstrate clinically meaningful reductions in OAR dose, and early commercial systems such as Varian Ethos show promising real-world feasibility. Decision-support systems leverage these AI-workflows, using predictive modeling to guide daily dose adjustments. Conclusions: AI has the potential to transform ART, automating labour-intensive tasks while still achieving high-quality plans. To support safe clinical adoption, future work should emphasize multi-institutional validation, standardized benchmarking and contouring guidelines, and development of transparent, uncertainty-aware models.
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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 Socratic graph.