ArticlePhysical and engineering sciences in medicine2024
Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation.
Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- A multi-centre real-world evaluation of AI-assisted organ at risk contouring on radiotherapy treatment planning workflows.The British journal of radiology · 2026Article
- Deep learning-enhanced image registration for accelerating daily adaptive magnetic resonance imaging-guided prostate radiotherapy.Physics and imaging in radiation oncology · 2026Article
- A Dosimetric Comparison of the Accumulated Dose in Prostate SBRT for Non-Adaptive and Adaptive External Beam Radiotherapy.Cancers · 2026Article
- Developing a web-based dashboard for adaptive radiotherapy workflows.Journal of applied clinical medical physics · 2026Article
- Comparative analysis of artificial intelligence-based contouring of cardiac substructures on computed tomography scans for radiation therapy.Physics and imaging in radiation oncology · 2026Article
- Comparative analysis of AI-generated and deformed image registration contours on daily CBCT in prostate cancer radiation therapy: accuracy and dosimetric implications using commercial tools.Physical and engineering sciences in medicine · 2026Article
- 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
- Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.Journal of applied clinical medical physics · 2025Article
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- Artificial intelligence-assisted radiation imaging pathways for distinguishing uterine fibroids and malignant lesions in patients presenting with cancer pain: a literature review.Frontiers in oncology · 2025Review
- The use of artificial intelligence in stereotactic ablative body radiotherapy for hepatocellular carcinoma.Frontiers in medicine · 2025Review
- Article
- Evaluation of Two Commercial Artificial Intelligence Segmentation Systems for Radiation Therapy.Journal of medical physicsArticle
- Artificial intelligence in neuro-oncology: Current advances and legal considerations in the European Union.Neuro-oncology advancesReview
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Manual contouring of organs at risk (OAR) is time-consuming and subject to inter-observer variability. AI-based auto-contouring is proposed as a solution to these problems if it can produce clinically acceptable results. This study investigated the performance of multiple AI-based auto-contouring systems in different OAR segmentations. The auto-contouring was performed using seven different AI-based segmentation systems (Radiotherapy AI, Limbus AI version 1.5 and 1.6, Therapanacea, MIM, Siemens AI-Rad Companion and RadFormation) on a total of 42 clinical cases with varying anatomical sites. Volumetric and surface dice similarity coefficients and maximum Hausdorff distance (HD) between the expert's contours and automated contours were calculated to evaluate their performance. Radiotherapy AI has shown better performance than other software in most tested structures considered in the head and neck, and brain cases. No specific software had shown overall superior performance over other software in lung, breast, pelvis and abdomen cases. Each tested AI system was able to produce comparable contours to the experts' contours of organs at risk which can potentially be used for clinical use. A reduced performance of AI systems in the case of small and complex anatomical structures was found and reported, showing that it is still essential to review each contour produced by AI systems for clinical uses. This study has also demonstrated a method of comparing contouring software options which could be replicated in clinics or used for ongoing quality assurance of purchased systems.
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