ReviewRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2021
Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.
Review in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 138 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
138 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- The current landscape of machine learning-based radiomics in arteriovenous malformations: a systematic review and radiomics quality score assessment.Frontiers in neurology · 2024Pooled it
- Clinical evaluation of three deep learning contouring models for head and neck organs at risk and lymph node levels.Technical innovations & patient support in radiation oncology · 2026Article
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- External evaluation of a deep learning-based algorithm for automated lung tumor and esophagus segmentation on CBCT.Biomedical physics & engineering express · 2026Article
- Segmenting scaffolds with ingrown bone in micro-CT images: considerations of evaluation metrics and loss functions.Medical & biological engineering & computing · 2026Article
- First-in-human dosimetry and biodistribution of [European journal of nuclear medicine and molecular imaging · 2026Article
- Clinical evaluation and regression test of a commercial deep-learning auto-segmentation model.Journal of applied clinical medical physics · 2026Article
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- Real-world clinical impact of implementing and updating a deep learning-based automatic contouring system in rectal cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- Adaptive Radiotherapy in the Era of Image Guidance and Artificial Intelligence: A Critical Review.Cancers · 2026Review
- Evaluating artificial intelligence versus human-generated contours in the thorax.Physics and imaging in radiation oncology · 2026Article
- A review of clinical trial radiotherapy quality assurance: value, evolution and lessons.Clinical and translational radiation oncology · 2026Review
- Multimodal deep learning to support delineation of neuro-oncology organs based on the EPTN international neurological contouring atlas.Physics and imaging in radiation oncology · 2026Article
- CT-Based Liver Segmentation for Liver Surgery: A Hybrid Approach Based on 3D U-Net-ELM Model.Biomedicines · 2026Article
- Deep Learning-Based CT Segmentation of a Non-radiopaque Hydrogel Rectal Spacer in Prostate Radiotherapy.Cureus · 2026Article
- Evaluation of efficacy in prostate cancer contouring for MR-guided radiotherapy using structured contouring training modules.Technical innovations & patient support in radiation oncology · 2026Article
- The illusory correlation between geometrical similarity and physician satisfaction in heart auto-segmentation revealed by a physician-blind test.Journal of applied clinical medical physics · 2026Article
- Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation inEJNMMI physics · 2026Article
- Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.Physics and imaging in radiation oncology · 2026Article
78 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with poor clinical outcomes and increased efficiency in the treatment planning workflow. However, there are no uniform standards for evaluating auto-segmentation platforms to assess their efficacy at meeting these goals. Here, we review the most frequently used evaluation techniques which include geometric overlap, dosimetric parameters, time spent contouring, and clinical rating scales. These data suggest that many of the most commonly used geometric indices, such as the Dice Similarity Coefficient, are not well correlated with clinically meaningful endpoints. As such, a multi-domain evaluation, including composite geometric and/or dosimetric metrics with physician-reported assessment, is necessary to gauge the clinical readiness of auto-segmentation for radiation treatment planning.
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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.