“AI tumor delineation for all breathing phases in early-stage NSCLC”
The Swin + Dyn Net algorithm, trained on the maximum expiration CT-scan effectively delineated gross tumor volumes in all breathing phases and the resulting ITV showed a good agreement with the ground truth (surface DSC = 0.79 ± 0.16 using all 10 BPs and 0.77 ± 0.14 using 2 BPs.). The proposed approach could reduce delineation time and inter-performer variability in the tumor contouring process for NSCLC SBRT workflows.
Authors
- Johannes A. Langendijk (ORCID: https://orcid.org/0000-0003-1083-372X)
- Nanna M. Sijtsema
- Robin Wijsman (ORCID: https://orcid.org/0000-0001-5139-0908)
- Peter M. A. van Ooijen (ORCID: https://orcid.org/0000-0002-8995-1210)
- Lisanne V. van Dijk (ORCID: https://orcid.org/0000-0002-9515-5616)
- Luis R DelaO-Arevalo
Institutions
- University Medical Center Groningen (NL)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2025-08-09
- DOI
- https://doi.org/10.1016/j.radonc.2025.111095
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Rijksuniversiteit Groningen
- Hanarth Fonds