Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation
Introduction - Target volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institutional-specific implementation of different auto-...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
11 September 2024
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| In: |
Clinical and translational radiation oncology
Year: 2024, Volume: 49, Pages: 100855-1-100855-7 |
| ISSN: | 2405-6308 |
| DOI: | 10.1016/j.ctro.2024.100855 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ctro.2024.100855 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2405630824001320 
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| Author Notes: | Eva Meixner, Benjamin Glogauer, Sebastian Klüter, Friedrich Wagner, David Neugebauer, Line Hoeltgen, Lisa A. Dinges, Semi Harrabi, Jakob Liermann, Maria Vinsensia, Fabian Weykamp, Philipp Hoegen-Saßmannshausen, Jürgen Debus, Juliane Hörner-Rieber |
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| 520 | |a Introduction - Target volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institutional-specific implementation of different auto-segmentation tools into clinical routine. - Methods - Three different commercially available, artificial intelligence-, ESTRO-guideline-based segmentation models (M1-3) were applied to fifty consecutive reference patients who received postoperative local RT including regional nodal irradiation for breast cancer for the delineation of clinical target volumes: the residual breast, implant or chestwall, axilla levels 1 and 2, the infra- and supraclavicular regions, the interpectoral and internal mammary nodes. Objective evaluation metrics of the created structures were conducted with the Dice similarity index (DICE) and the Hausdorff distance, and a manual evaluation of usability. - Results - The resulting geometries of the segmentation models were compared to the reference volumes for each patient and required no or only minor corrections in 72 % (M1), 64 % (M2) and 78 % (M3) of the cases. The median DICE and Hausdorff values for the resulting planning target volumes were 0.87-0.88 and 2.96-3.55, respectively. Clinical usability was significantly correlated with the DICE index, with calculated cut-off values used to define no or minor adjustments of 0.82-0.86. Right or left sided target and breathing method (deep inspiration breath hold vs. free breathing) did not impact the quality of the resulting structures. - Conclusion - 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. | ||
| 650 | 4 | |a AI contouring | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Auto-segmentation | |
| 650 | 4 | |a Clinical implementation | |
| 650 | 4 | |a Deep learning segmentation | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Quality assurance | |
| 650 | 4 | |a Target volume delineation | |
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