ArticleNPJ digital medicine2025
Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed.
- 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
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Anticipating femoral shaft nonunion at 1-3 months: a multimodal framework with automated X‑ray segmentation despite metal implants.International orthopaedics · 2026Article
- Uncertainty estimation for reliable neural network-based radiotherapy dose modelling using Monte Carlo dropout, mean variance estimation and deep ensemble.Physics and imaging in radiation oncology · 2026Article
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Validation of MRI-based nnU-Net model for automated segmentation of neck lymph nodes in head and neck squamous cell carcinoma: a multicenter study.Neuroradiology · 2026Article
- AI-Based Dose Compliance of Secondary Organs at Risk in Head and Neck Cancer Radiotherapy.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- Inter-observer variability in radiotherapy contouring with the use of autocontouring software: A systematic review.Clinical and translational radiation oncology · 2026Review
- Enhancing the precision and uniformity of clinical target volume delineation for cervical cancer through a structured educational program.BMC medical education · 2026Article
- Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.Scientific reports · 2026Article
- Clinically validated deep learning auto-contouring for glioblastoma volumetric modulated arc therapy planning: a dosimetric equivalence study.Radiation oncology journal · 2026Article
- Interobserver Variation Within Planning Target Volume and Organs at Risk in a Patient with Oropharyngeal Carcinoma: A Contouring Study with Anatomical Analysis.Current oncology (Toronto, Ont.) · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability.Medical physics · 2025Article
- Time efficiency, geometric accuracy, and clinical impact of AI-assisted contouring of organs at risk in head and neck cancer radiotherapy.Acta oncologica (Stockholm, Sweden) · 2025Article
- Improving segmentation precision in prostate cancer adaptive radiation therapy with a patient-specific network.PloS one · 2025Article
- Predictors of Perceived Organizational Innovation Among Allied Health and Pharmacy Professionals.Journal of healthcare leadership · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This is a multi-institutional study to evaluate a head-and-neck CT auto-segmentation software across seven institutions globally. 11 lymph node levels and 7 organs-at-risk contours were evaluated in a two-phase study design. Time savings were measured in both phases, and the inter-observer variability across the seven institutions was quantified in phase two. Overall time savings were found to be 42% in phase one and 49% in phase two. Lymph node levels IA, IB, III, IVA, and IVB showed no significant time savings, with some centers reporting longer editing times than manual delineation. All the edited ROIs showed reduced inter-observer variability compared to manual segmentation. Our study shows that auto-segmentation plays a crucial role in harmonizing contouring practices globally. However, the clinical benefits of auto-segmentation software vary significantly across ROIs and between clinics. To maximize its potential, institution-specific commissioning is required to optimize the clinical benefits.
Identifiers
What OpenQuestion holds
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.