ArticlePhysics and imaging in radiation oncology2023
Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
- Radiotherapy quality assurance in the PRO-GLIO trial: results from a dummy run comparing experts across twelve institutions in two Scandinavian countries.Clinical and translational radiation oncology · 2026Article
- Multimodal PET-MR segmentation for glioblastoma: complementarity for treatment planning and recurrence definition.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- 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
- Improving confidence in MRI-based auto-segmentation via uncertainty assessment.Acta oncologica (Stockholm, Sweden) · 2026Article
- Synthetic CT imaging for pelvic and abdominal MR-only radiotherapy: Clinical validation on a 1.5T MR-Linac.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Clinically validated deep learning auto-contouring for glioblastoma volumetric modulated arc therapy planning: a dosimetric equivalence study.Radiation oncology journal · 2026Article
- A combined automatic organ at risk contouring and synthetic Computed Tomography solution for pelvic Magnetic Resonance-only radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Clinical evaluation of two glioblastoma delineation methods based on neural networks.Technical innovations & patient support in radiation oncology · 2025Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.International journal of computer assisted radiology and surgery · 2024Article
- Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues.Diagnostics (Basel, Switzerland) · 2024Article
- Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications.Physics and imaging in radiation oncology · 2024Article
- High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.Physics in medicine and biology · 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
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Normal tissue sparing in radiotherapy relies on proper delineation. While manual contouring is time consuming and subject to inter-observer variability, auto-contouring could optimize workflows and harmonize practice. We assessed the accuracy of a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation. Materials and methods: Thirty adult brain tumor patients were retrospectively manually recontoured. Two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours). For 15 selected cases, identical plans were optimized for each structure set. We used Dice Similarity Coefficient (DSC) and mean surface-distance (MSD) for geometric comparison and gamma analysis and dose-volume-histogram comparison for dose metrics evaluation. Wilcoxon signed-ranks test was used for paired data, Spearman coefficient(ρ) for correlations and Bland-Altman plots to assess level of agreement. Results: Auto-contouring was significantly faster than manual (1.1/20 min, p < 0.01). Median DSC and MSD were 0.7/0.9 mm for AI and 0.8/0.5 mm for AIedit. DSC was significantly correlated with structure size (ρ = 0.76, p < 0.01), with higher DSC for large structures. Median gamma pass rate was 74% (71-81%) for Plan_AI and 82% (75-86%) for Plan_AIedit, with no correlation with DSC or MSD. Differences between Dmean_AI and Dmean_Ref were ≤ 0.2 Gy (p < 0.05). The dose difference was moderately correlated with DSC. Bland Altman plot showed minimal discrepancy (0.1/0) between AI and reference Dmean/Dmax. Conclusions: The AI-model showed good accuracy for large structures, but developments are required for smaller ones. Auto-segmentation was significantly faster, with minor differences in dose distribution caused by geometric variations.
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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.