ArticlePhysics and imaging in radiation oncology2020
External validation of deep learning-based contouring of head and neck organs at risk.
Article in Physics and imaging in radiation oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist.Frontiers in oncology · 2021Pooled it
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans.Physics and imaging in radiation oncology · 2024Article
- Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.Frontiers in oncology · 2024Article
- Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study.Frontiers in oncology · 2024Article
- Leveraging global binary masks for structure segmentation in medical images.Physics in medicine and biology · 2023Article
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
- Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.Medical physics · 2023Article
- Analytic calculations and synthetic shapes for validation of quantitative contour comparison software.Physics and imaging in radiation oncology · 2023Article
- Clinical evaluation of deep learning and atlas-based auto-segmentation for critical organs at risk in radiation therapy.Journal of medical radiation sciences · 2023Article
- Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.Frontiers in oncology · 2023Article
- Fully automated clinical target volume segmentation for glioblastoma radiotherapy using a deep convolutional neural network.Polish journal of radiology · 2023Article
- How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images.Frontiers in oncology · 2023Article
- Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning.Frontiers in oncology · 2023Article
- Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.Entropy (Basel, Switzerland) · 2022Article
- Towards real-time radiotherapy planning: The role of autonomous treatment strategies.Physics and imaging in radiation oncology · 2022Article
- Evaluating the Effectiveness of Deep Learning Contouring across Multiple Radiotherapy Centres.Physics and imaging in radiation oncology · 2022Article
- Strategies for tackling the class imbalance problem of oropharyngeal primary tumor segmentation on magnetic resonance imaging.Physics and imaging in radiation oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeHead and neck (HN) radiotherapy can benefit from automatic delineation of tumor and surrounding organs because of the complex anatomy and the regular need for adaptation. The aim of this study was to assess the performance of a commercially available deep learning contouring (DLC) model on an external validation set. MATERIALS AND
methodsThe CT-based DLC model, trained at the University Medical Center Groningen (UMCG), was applied to an independent set of 58 patients from the Radboud University Medical Center (RUMC). DLC results were compared to the RUMC manual reference using the Dice similarity coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Craniocaudal spatial information was added by calculating binned measures. In addition, a qualitative evaluation compared the acceptance of manual and DLC contours in both groups of observers.
resultsGood correspondence was shown for the mandible (DSC 0.90; HD95 3.6 mm). Performance was reasonable for the glandular OARs, brainstem and oral cavity (DSC 0.78-0.85, HD95 3.7-7.3 mm). The other aerodigestive tract OARs showed only moderate agreement (DSC 0.53-0.65, HD95 around 9 mm). The binned measures displayed the largest deviations caudally and/or cranially.
conclusionsThis study demonstrates that the DLC model can provide a reasonable starting point for delineation when applied to an independent patient cohort. The qualitative evaluation did not reveal large differences in the interpretation of contouring guidelines between RUMC and UMCG observers.
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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.