Validation of different automated segmentation models for target volume contouring in postoperative radiotherapy for breast cancer and regional nodal irradiation

• Published in: Clinical and translational radiation oncology 2024-11, Vol.49, p.100855, Article 100855
• Main Authors: Meixner, Eva, Glogauer, Benjamin, Klüter, Sebastian, Wagner, Friedrich, Neugebauer, David, Hoeltgen, Line, Dinges, Lisa A., Harrabi, Semi, Liermann, Jakob, Vinsensia, Maria, Weykamp, Fabian, Hoegen-Saßmannshausen, Philipp, Debus, Jürgen, Hörner-Rieber, Juliane
• Format: Article
• Language: English
• Subjects: AI contouring; Artificial intelligence; Auto-segmentation; Clinical implementation; Deep learning segmentation; Machine learning; Original; Quality assurance; Target volume delineation
• ISSN: 2405-6308; 2405-6308
• DOI: 10.1016/j.ctro.2024.100855

•Deep learning software show high-quality accuracy and standardization for target delineation.•Resulting geometries required no or only minor corrections in 64–78 % of the cases.•Clinical usability significantly correlated with the DICE (cut-off of 0.82–0.86).•Implementation with adherence to institutional workflows seems feasible. 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. 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. 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. 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.