ArticlePractical radiation oncology
Quantitative Evaluation of Artificial Intelligence-Based Organ Segmentation Across Multiple Anatomic Sites Using 8 Commercial Software Platforms.
Article in Practical radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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.
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
3 citing papers in PubMed.
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- Evaluating artificial intelligence versus human-generated contours in the thorax.Physics and imaging in radiation oncology · 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
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
purposeThis study aims to evaluate organs-at-risk (OARs) segmentation variability across 8 commercial artificial intelligence (AI)-based segmentation software using independent multi-institutional data sets, and to provide recommendations for clinical practices using AI-segmentation. METHODS AND MATERIALS: A total of 160 planning computed tomography image sets from 4 anatomic sites: head and neck, thorax, abdomen, and pelvis were retrospectively pooled from 3 institutions. Contours for 31 OARs generated by the software were compared to clinical contours using multiple accuracy metrics, including: dice similarity coefficient (DSC), 95 percentile of Hausdorff distance, surface DSC, as well as relative added path length as an efficiency metric. A 2-factor analysis of variance was used to quantify variability in contouring accuracy across software platforms (intersoftware) and patients (interpatient). Pairwise comparisons were performed to categorize the software into different performance groups, and intersoftware variations were calculated as the average performance differences between the groups.
resultsSignificant intersoftware and interpatient contouring accuracy variations (P < .05) were observed for most OARs. The largest intersoftware variations in DSC in each anatomic region were cervical esophagus (0.41), trachea (0.10), spinal cord (0.13), and prostate (0.17). Among the organs evaluated, 7 had mean DSC >0.9 (ie, heart, liver), 15 had DSC ranging from 0.7 to 0.89 (ie, parotid, esophagus). The remaining organs (ie, optic nerves, seminal vesicle) had DSC<0.7. Of the 31 organs, 16 (52%) had relative added path length less than 0.1.
conclusionsOur results reveal significant intersoftware and interpatient variability in the performance of AI-segmentation software. These findings highlight the need of thorough software commissioning, testing, and quality assurance across disease sites, patient-specific anatomies, and image acquisition protocols.
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Registered trials
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