Observational studyNature communications2026
A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.
Observational study in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05787522 (Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk), which is not on this map. Cited by 2 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.
Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
Who cites it
2 citing papers in PubMed.
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Interpretable Model for Clinical Use in Left Atrial Appendage Segmentation via an Optimised Deformable-Attention U-Net With Spatial-Channel Fusion.Healthcare technology lettersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
Widespread clinical implementation of rapidly evolving auto-segmentation tools remains constrained by a scarcity of high-quality prospective evidence. Here we show the results of a prospective, multicenter, observational trial (NCT05787522) evaluating the clinical performance of a deep learning model (iCurveE) for artificial intelligence (AI)-assisted delineation of organs at risk (OARs) in thoracic and breast cancer radiotherapy. Computed tomography images from 500 patients across five centers are annotated by 37 physicians using manual, AI-generated, and AI-assisted methods. Eleven thoracic OARs are evaluated based on the primary endpoints of volumetric Dice similarity coefficient (vDSC) and contouring time, alongside secondary metrics including 95% Hausdorff Distance (HD95). We prospectively annotate 2,483 OAR sets (27,043 OARs): 993 manual, 497 AI-generated, and 993 AI-assisted. AI-assisted delineation achieves significantly better vDSC (mean, 0.902) and HD95 (mean, 5.20 mm) than manual delineation (mean vDSC, 0.857; mean HD95, 8.01 mm; p < 0.0001) while improving time efficiency by 81.63% (median: 10.0 vs. 55.0 min; p < 0.0001). AI-assisted delineation reduces performance variability across centers and physicians with varying expertise. This study validates the clinical applicability of AI-assisted delineation in improving delineation performance and promoting healthcare equity.
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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.