ArticleJournal of applied clinical medical physics2025
Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Cross-fraction prior learning for scalable organ-at-risk segmentation in abdominal MR-guided radiotherapy.Medical physics · 2026Article
- AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.Biomedicines · 2026Article
- MaxI-Net: A 3D AI Framework for CBCT-Based Maxillofacial Defect Reconstruction and Patient-Specific Implant Generation with Biomechanical Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence-assisted radiotherapy for pelvic and abdominal malignancies: assessing feasibility in the context of Africa-specific risks.Frontiers in oncology · 2026Review
- Clinical validation of AI-assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance.Journal of applied clinical medical physics · 2026Article
- Inter-Observer variability in organs at risk contouring among radiation therapy students and qualified radiation therapists.Technical innovations & patient support in radiation oncology · 2025Article
- Article
- Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.Journal of applied clinical medical physics · 2025Article
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
- Quantitative Evaluation of Artificial Intelligence-Based Organ Segmentation Across Multiple Anatomic Sites Using 8 Commercial Software Platforms.Practical radiation oncologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundAccurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.
methodsA cohort of 36 patients previously treated at our institution were selected for the software performance assessment. Fifty-eight OAR structures from seven disease sites were automatically segmented with each tool. Five radiation oncologists with different specialties qualitatively scored the automatic OAR contours' clinical usability by a 4-level scale (0-3), termed as quality score (QS), representing from "0: not usable" to "3: directly usable for a clinic." Additionally, quantitative comparison with clinically approved contours using Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95) was performed in complement to QS from physicians.
resultSoftware A achieved an average QS of 2.17 ± 0.69, comparable to Software B's average QS of 2.17 ± 0.72. Software B performed better with more OARs (42 vs. 37) that required minor or no modification than Software A. Major modifications were needed for 13 out of 58 automated contours from both tools. Both DSC and HD95 scores for the two tools were comparable to each other, with DSC: 0.67 ± 0.23 versus 0.66 ± 0.21 and HD95: 13.07 ± 15.84 versus 15.55 ± 18.45 for Software A and Software B, respectively. Correlation coefficients between the physician score and the quantitative metrics suggested that the contouring results from Software A aligned more closely with the physician's evaluations.
conclusionBased on our study, either software tool could produce clinically acceptable contours for about 65% of the OAR structures. However, further refinement is necessary for several challenging OARs to improve model performance.
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.