ArticleInternational journal of radiation oncology, biology, physics2024
NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.
Article in International journal of radiation oncology, biology, physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Clinical edge-case stress testing of two Asia-developed AI auto-contouring systems using prostate radiotherapy planning CT images.Radiological physics and technology · 2026Article
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.Journal of the Egyptian National Cancer Institute · 2026Article
- A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy.Nature communications · 2026Observational
- PURE-MRI: An international study assessing physician accuracy in delineating the prostate and urethra on prostate MRI.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Clinical validation of AI-assisted contouring in prostate radiation therapy treatment planning: Highlighting automation bias and the need for standardized quality assurance.Journal of applied clinical medical physics · 2026Article
- Modality-AGnostic image Cascade (MAGIC) for multi-modality cardiac substructure segmentation.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Article
- Enhancing Radiation Oncology Education Through Artificial Intelligence: A Review of Applications, Limitations, and Future Directions.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025Review
- Strategies to reduce intestinal toxicity in neoadjuvant management of locally advanced rectal cancer.Discover oncology · 2025Review
- Geometric, dosimetric and psychometric evaluation of three commercial AI software solutions for OAR auto-segmentation in head and neck radiotherapy.Scientific reports · 2025Article
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- Article
- Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.Medical physics · 2025Article
- Multicentre evaluation of deep learning CT autosegmentation of the head and neck region for radiotherapy.NPJ digital medicine · 2025Article
- A Japanese national survey on IMRT/SBRT in 2023 by the JASTRO High-Precision External Beam Radiotherapy Group.Journal of radiation research · 2025Article
- Evaluation and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk.Journal of applied clinical medical physics · 2025Article
- The Real-World Impact of Vestibular Schwannoma Fully Automated Volume Measures on the Evaluation of Size Change and Clinical Management Outcomes in a Multidisciplinary Meeting Setting.The journal of international advanced otology · 2025Article
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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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.