ArticleNature communications2022
Comprehensive and clinically accurate head and neck cancer organs-at-risk delineation on a multi-institutional study.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Contouring and dose extraction of the maxilla, mandible and teeth on head and neck cancer radiotherapy planning scans.EJC paediatric oncology · 2026Review
- Auto-segmentation of organs-of-interest clinical acceptability & reproducibility framework in head and neck cancer.Physics and imaging in radiation oncology · 2026Article
- A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.Nature communications · 2026Observational
- A Flexible Hybrid Quantum-classical Training Framework of Organ-at-Risk and Tumor Segmentation Models for Radiation Therapy Planning.Scientific reports · 2026Article
- Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.Scientific reports · 2025Article
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.NPJ digital medicine · 2025Article
- Prevalence of cancer-related cognitive impairment among patients with nasopharyngeal carcinoma: a cross-sectional study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Review
- A frequency attention-embedded network for polyp segmentation.Scientific reports · 2025Article
- Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.Scientific reports · 2024Article
- Clinical acceptability of automatically generated lymph node levels and structures of deglutition and mastication for head and neck radiation therapy.Physics and imaging in radiation oncology · 2024Article
- Adaptive Region-Specific Loss for Improved Medical Image Segmentation.IEEE transactions on pattern analysis and machine intelligence · 2023Article
- Federated Multi-Organ Segmentation With Inconsistent Labels.IEEE transactions on medical imaging · 2023Article
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26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3-5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.
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