ArticleAdvances in radiation oncology
Clinical Validation of Siemens' Syngo.via Automatic Contouring System.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Comparison of Auto-Contouring Tools for Delineation of Normal Organs at Risk in Paediatric Patients Undergoing Radiotherapy.Journal of medical radiation sciences · 2026Article
- Automatic pelvic bone segmentation for radiotherapy using diagnostic imaging workstation.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
- 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
- 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
- AI-driven decision making for intravascular device selection in aortic disease. Current insights and prospects.Frontiers in cardiovascular medicine · 2025Review
- ASTRO's Advances in Radiation Oncology's Top Downloaded Articles of 2023.Advances in radiation oncology · 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
- Evaluation of multiple-vendor AI autocontouring solutions.Radiation oncology (London, England) · 2024Article
- Artificial Intelligence-Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics.Advances in radiation oncology · 2024Article
- Multicenter Study of Pelvic Nodal Autosegmentation Algorithm of Siemens Healthineers: Comparison of Male Versus Female Pelvis.Advances in radiation oncology · 2024Article
- Article
- Quantitative Evaluation of Artificial Intelligence-Based Organ Segmentation Across Multiple Anatomic Sites Using 8 Commercial Software Platforms.Practical radiation oncologyArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The manual delineation of organs at risk is a process that requires a great deal of time both for the technician and for the physician. Availability of validated software tools assisted by artificial intelligence would be of great benefit, as it would significantly improve the radiation therapy workflow, reducing the time required for segmentation. The purpose of this article is to validate the deep learning-based autocontouring solution integrated in syngo.via RT Image Suite VB40 (Siemens Healthineers, Forchheim, Germany). Methods and Materials: For this purpose, we have used our own specific qualitative classification system, RANK, to evaluate more than 600 contours corresponding to 18 different automatically delineated organs at risk. Computed tomography data sets of 95 different patients were included: 30 patients with lung, 30 patients with breast, and 35 male patients with pelvic cancer. The automatically generated structures were reviewed in the Eclipse Contouring module independently by 3 observers: an expert physician, an expert technician, and a junior physician. Results: There is a statistically significant difference between the Dice coefficient associated with RANK 4 compared with the coefficient associated with RANKs 2 and 3 ( Conclusions: Siemens' syngo.via RT Image Suite offers good autocontouring results and significant time savings.
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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.