ArticleBMC medical imaging2026
A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal cancer tumors exhibit complex and variable distribution. Due to differences in clinical experience, junior oncologists often show less accuracy in gross tumor volume (GTV) delineation compared to their senior counterparts. Deep learning methods have the potential to assist junior oncologists in improving the accuracy of GTV delineation. This study proposes a human-AI collaborative workflow for esophageal cancer radiotherapy and explores its clinical feasibility.
methodsA retrospective collection of 730 esophageal cancer radiotherapy cases from our center was divided into training and testing sets at a 4:1 ratio. The human-AI collaborative workflow involves the following steps: (1) developing an automatic GTV delineation model based on planning CT images and obtaining an AI-prompt; (2) junior oncologists manually refining the GTVs generated by the model to serve as human-guided prompt; (3) using both prompts in combination with planning CT images to build two automatic refinement models; and (4) junior oncologists comparing the refined GTVs obtained by the above models, ultimately submitting the finalized GTVs for expert review by senior oncologists. Three junior oncologists from our center independently completed the manual tasks in steps 2 and 4, and the GTVs derived from the new approach were compared for accuracy against the ground truth delineated by senior oncologists.
resultsThe results from the testing set indicated that after manual refinement by the three junior oncologists, the Dice similarity coefficient (DSC) between the automatic GTV delineation (step 1) and the ground truth increased from an initial value of 0.6538 ± 0.2022 to 0.7109 ± 0.1958, 0.7253 ± 0.1632, and 0.7236 ± 0.1965. Furthermore, by combining manual refinement, automatic refinement, and decision-making, the accuracy of the GTV delineation further improved to 0.7552 ± 0.1366, 0.7671 ± 0.1190, and 0.7757 ± 0.1354. These results demonstrate that the new approach significantly enhances the accuracy and stability of esophageal cancer GTV delineation.
conclusionWith the support of the human-AI collaborative workflow, junior oncologists can delineate esophageal cancer GTV contours with improved accuracy, providing a reliable foundation for the integration of AI in clinical radiotherapy. The study also demonstrates that, in the current trend toward radiotherapy automation, clinical oncologists remain an indispensable and vital part of the process.
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.