Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation
Artificial intelligence-based auto-segmentation programs showed high-quality accuracy and provided standardization and efficient support for guideline-based target volume contouring as a precondition for fully automated workflows in radiotherapy treatment planning.
Authors
- Semi Harrabi (ORCID: https://orcid.org/0000-0003-1195-7150)
- Lisa A Dinges
- Jakob Liermann (ORCID: https://orcid.org/0000-0002-1445-1940)
- Eva Meixner (ORCID: https://orcid.org/0000-0001-7087-9581)
- Line Hoeltgen (ORCID: https://orcid.org/0000-0002-3433-2455)
- Maria Vinsensia
- Sebastian Klüter (ORCID: https://orcid.org/0000-0003-3139-3444)
- Juliane Hörner‐Rieber (ORCID: https://orcid.org/0000-0003-3911-4438)
- Jürgen Debus (ORCID: https://orcid.org/0000-0002-6411-4811)
- Fabian Weykamp (ORCID: https://orcid.org/0000-0003-1731-7090)
- Philipp Hoegen-Saßmannshausen
- Benjamin Glogauer
- Friedrich Wagner
- David Neugebauer
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- Düsseldorf University Hospital (DE)
- Heidelberger Institut für Radioonkologie (DE)
- National Center for Tumor Diseases (DE)
- Heinrich Heine University Düsseldorf (DE)
Publication Details
- Journal
- Clinical and Translational Radiation Oncology
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1016/j.ctro.2024.100855
- Citations
- 5
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Universitätsklinikum Heidelberg