ArticlePhysics and imaging in radiation oncology2025
Feasibility and time gain of implementing artificial intelligence-based delineation tools in daily magnetic resonance image-guided adaptive prostate cancer radiotherapy.
Article 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 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Deep learning-enhanced image registration for accelerating daily adaptive magnetic resonance imaging-guided prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Improving workflow efficiency during prostate stereotactic body radiotherapy using real-time adaptive planning associated with reduced intra-fractional target motion.Radiation oncology (London, England) · 2026Article
- Direct-to-treatment MRI-guided prostate radiotherapy using a generic patient-agnostic reference plan.Technical innovations & patient support in radiation oncology · 2026Article
- Dosimetric evaluation of cone beam computed tomography-guided online adaptive radiotherapy in gastric mucosa-associated lymphoid tissue lymphoma.Technical innovations & patient support in radiation oncology · 2025Article
- Implementation and clinical evaluation of an in-house thoracic auto-segmentation model for 0.35 T magnetic resonance imaging guided radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Recent innovations in offline and online Magnetic Resonance Imaging guided radiation oncology.Physics and imaging in radiation oncology · 2025Article
- The promise of artificial intelligence-assisted radiotherapy for prostate cancer in Morocco: a transformational opportunity.Frontiers in medicine · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: Daily magnetic resonance image (MRI)-guided radiotherapy plan adaptation requires time-consuming manual contour edits of targets and organs at risk in the online workflow. Recent advances in auto-segmentation promise to deliver high-quality delineations within a short time frame. However, the actual time benefit in a clinical setting is unknown. The current study investigated the feasibility and time gain of implementing online artificial intelligence (AI)-based delineations at a 1.5 T MRI-Linac. Materials and methods: Fifteen consecutive prostate cancer patients, treated to 60 Gy in 20 fractions at a 1.5 T MRI-Linac, were included in the study. The first 5 patients (Group 1) were treated using the standard contouring workflow for all fractions. The last 10 patients (Group 2) were treated with the standard workflow for fractions 1 up to 3 (Group 2 - Standard) and an AI-based workflow for the remaining fractions (Group 2 - AI). AI delineations were delivered using an in-house developed AI inference service and an in-house trained nnU-Net. Results: The AI-based workflow reduced delineation time from 9.8 to 5.3 min. The variance in delineation time seemed to increase during the treatment course for Group 1, while the delineation time for the AI-based workflow was constant (Group 2 - AI). Fewer occurrences of readaptation due to target movement occurred with the AI-based workflow. Conclusion: Implementing an AI-based workflow at the 1.5 T MRI-Linac is feasible and reduces the delineation time. Lower variance in delineation duration supports a better ability to plan daily treatment schedules and avoids delays.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.