ArticlePhysics and imaging in radiation oncology2022
Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy.
Article in Physics and imaging in radiation oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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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.
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Who cites it
12 citing papers in PubMed.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026Article
- The impact of DICOM import/export on radiotherapy structures in commercial systems.Physics and imaging in radiation oncology · 2026Article
- Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis.Physical and engineering sciences in medicine · 2025Article
- Tools and recommendations for commissioning and quality assurance of deformable image registration in radiotherapy.Physics and imaging in radiation oncology · 2024Review
- Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.Diagnostics (Basel, Switzerland) · 2024Article
- Understanding metric-related pitfalls in image analysis validation.Nature methods · 2024Review
- Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- Analytic calculations and synthetic shapes for validation of quantitative contour comparison software.Physics and imaging in radiation oncology · 2023Article
- Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'?Diagnostics (Basel, Switzerland) · 2023Review
- Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy.Physics and imaging in radiation oncology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different implementations of the measures to the same data in order to investigate how consistently such measures are defined and implemented in radiation oncology. Materials and Methods: A survey was conducted to assess if there were any differences in definitions of contouring measures or their implementations that would lead to variation in reported results between institutions. This took two forms: a set of computed tomography (CT) image data with "Test" and "Reference" contours was distributed for participants to process using their preferred tools and report results, and a questionnaire regarding the definition of measures and their implementation was completed by the participants. Results: Thirteen participants completed the survey and submitted results, with one commercial and twelve in-house solutions represented. Excluding outliers, variations of up to 50% in Dice Similarity Coefficient (DSC), 50% in 3D Hausdorff Distance (HD), and 200% in Average Distance (AD) were observed between the participant submitted results. Collaborative investigation with participants revealed a large number of bugs in implementation, confounding the understanding of intentional implementation choices. Conclusion: Care must be taken when comparing quantitative results between different studies. There is a need for a dataset with clearly defined measures and ground truth for validation of such tools prior to their use.
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Registered trials
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