ArticleReports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology2025
Linking RayStation AI auto-contouring with Eclipse TPS: a scripted workflow for clinical integration.
Article in Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Accuracy assessment of a hybrid deep learning and image processing approach for lung CT segmentation in external radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Efficient and accurate delineation of target volumes and organs-at-risk (OARs) is critical in radiotherapy planning. Deep learning-based auto-contouring improves consistency and reduces manual effort, but integrating AI tools into established workflows is challenging when platforms differ. This work presents an automated integration of RayStation's AI-based auto-contouring with the Eclipse treatment planning system (TPS). Materials and methods: Custom scripts were developed in Eclipse (C#) and RayStation (Python), supported by DICOM networking, to enable automated data exchange. The Eclipse script exports computed tomography (CT) images and a list of requested structures to RayStation. Upon receipt, RayStation 2024B automatically triggers a Python script that performs deep learning segmentation using built-in models. The resulting contours are then exported back to Eclipse for review, requiring no user interaction beyond initial input. Results: The integration was validated on 35 clinical cases. The complete process, from CT export to contour availability in Eclipse, averaged 1.1 minutes per case. The solution significantly reduces contouring time and improves workflow consistency. Conclusion: This automated cross-platform integration enables deep learning segmentation from RayStation to be accessed directly within Eclipse, offering a streamlined and scalable contouring solution. It enhances planning efficiency, reduces inter-observer variability, and allows clinicians to benefit from state-of-the-art AI tools within their existing TPS. The framework is modular and adaptable to evolving clinical protocols, serving as a practical example of multi-vendor system interoperability in modern radiotherapy planning.
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Registered trials
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