Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions.
| Autor: | Chen L; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria.; Erasmus MC Cancer Institute, University Medical Center, Department of Radiotherapy, Rotterdam, the Netherlands.; Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering, Delft, the Netherlands.; Leiden University Medical Center, Faculty of Medicine, Leiden, the Netherlands., Platzer P; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria.; Fachhochschule Wiener Neustadt, Department MedTech, Wiener Neustadt, Austria., Reschl C; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria., Schafasand M; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria.; Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria.; Karl Landsteiner University of Health Sciences, Department of Oncology, Krems an der Donau, Austria., Nachankar A; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria.; ACMIT Gmbh, Department of Medicine, Wiener Neustadt, Austria., Lukas Hajdusich C; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria., Kuess P; Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria., Stock M; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria.; Karl Landsteiner University of Health Sciences, Department of Oncology, Krems an der Donau, Austria., Habraken S; Erasmus MC Cancer Institute, University Medical Center, Department of Radiotherapy, Rotterdam, the Netherlands.; Holland Proton Therapy Center, Department of Medical Physics & Informatics, Delft, the Netherlands., Carlino A; MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria. |
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| Jazyk: | angličtina |
| Zdroj: | Physics and imaging in radiation oncology [Phys Imaging Radiat Oncol] 2023 Dec 27; Vol. 29, pp. 100527. Date of Electronic Publication: 2023 Dec 27 (Print Publication: 2024). |
| DOI: | 10.1016/j.phro.2023.100527 |
| Abstrakt: | Background and Purpose: Autocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy with fixed beam lines, focusing on validation and implementation for routine clinical use. Materials and Methods: Autocontouring was performed on sixteen organs at risk (OARs) for 98 adult and pediatric patients with 137 H&N CT scans in eight orientations. A geometric comparison of the autocontours and manual segmentations was performed using the Hausdorff Distance 95th percentile, Dice Similarity Coefficient (DSC) and surface DSC and compared to interobserver variability where available. Additional qualitative scoring and dose-volume-histogram (DVH) parameters analyses were performed for twenty patients in two positions, consisting of scoring on a 0-3 scale based on clinical usability and comparing the mean (D Results: For the geometric analysis, the model performance in head-first-supine straight and hyperextended orientations was in the same range as the interobserver variability. HD95, DSC and surface DSC was heterogeneous in other orientations. No significant geometric differences were found between pediatric and adult autocontours. The qualitative scoring yielded a median score of ≥ 2 for 13/16 OARs while 7/32 DVH parameters were significantly different. Conclusions: For head-first-supine straight and hyperextended scans, we found that 13/16 OAR autocontours were suited for use in daily clinical practice and subsequently implemented. Further development is needed for other patient orientations before implementation. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Author(s).) |
| Databáze: | MEDLINE |
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