ArticleScientific reports2025
Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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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.
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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
3 citing papers in PubMed.
- Review
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
Contouring organs-at-risk (OARs) is a critical yet time-consuming step in head and neck (HaN) radiotherapy planning. Auto-segmentation methods have been widely studied, and commercial solutions are increasingly entering clinical use. However, their adoption warrants a comprehensive, multi-perspective evaluation. The purpose of this study is to compare three commercial artificial intelligence (AI) software solutions (Limbus, MIM and MVision) for HaN OAR auto-segmentation on a cohort of 10 computed tomography images with reference contours obtained from the public HaN-Seg dataset, from both observational (descriptive and empirical) and analytical (geometric, dosimetric and psychometric) perspectives. The observational evaluation included vendor questionnaires on technical specifications and radiographer feedback on usability. The analytical evaluation covered geometric (Dice similarity coefficient, DSC, and 95th percentile Hausdorff distance, HD95), dosimetric (dose constraint compliance, OAR priority-based analysis), and psychometric (5-point Likert scale) assessments. All software solutions covered a broad range of OARs. Overall geometric performance differences were relatively small (Limbus: 69.7% DSC, 5.0 mm HD95; MIM: 69.2% DSC, 5.6 mm HD95; MVision: 66.7% DSC, 5.3 mm HD95), however, statistically significant differences were observed for smaller structures such as the cochleae, optic chiasm, and pituitary and thyroid glands. Differences in dosimetric compliance were overall minor, with the lowest compliance observed for the oral cavity and submandibular glands. In terms of qualitative assessment, radiographers gave the highest average Likert rating to Limbus (3.9), followed by MVision (3.7) and MIM (3.5). With few exceptions, most software solutions produced good-quality AI-generated contours (Likert ratings ≥ 3), yet some editing should still be performed to reach clinical acceptability. Notable discrepancies were seen for the optic chiasm and in cases affected by mouth bites or dental artifacts. Importantly, no clear relationship emerged between geometric, dosimetric, and psychometric metrics, underscoring the need for a multi-perspective evaluation without shortcuts.
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