ReviewStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2025
Artificial intelligence for treatment delivery: image-guided radiotherapy.
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 6 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
6 citing papers in PubMed.
- 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
- Automated workflow for target volume propagation in online adaptive magnetic resonance guided radiotherapy for lung lesions using foundation models.Physics and imaging in radiation oncology · 2026Article
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.Cardio-oncology (London, England) · 2026Review
- A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck.Frontiers in digital health · 2026Article
- Advancing offline magnetic resonance-guided prostate radiotherapy through dedicated imaging and deep learning-based automatic contouring of targets and neurovascular structures.Physics and imaging in radiation oncology · 2025Article
- The increasing role of artificial intelligence in radiation oncology: how should we navigate it?Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.
Indexed as
Identifiers
39138806What Socratic holds
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.