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Impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: a SIOP-RTSG radiotherapy committee study

Background and purpose - Integrating deep learning (DL) for auto-contouring has significantly improved organ-at-risk (OAR) delineation in adult radiotherapy. However, its application in paediatric radiotherapy remains limited. This study evaluates DL-based auto-contouring of OARs, followed by manual...

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Hauptverfasser: Ding, Mianyong (VerfasserIn) , Maspero, Matteo (VerfasserIn) , Harrabi, Semi B. (VerfasserIn) , Jouglar, Emmanuel (VerfasserIn) , Vennarini, Sabina (VerfasserIn) , Spencer, Timothy (VerfasserIn) , Weber, Britta (VerfasserIn) , Magelssen, Henriette (VerfasserIn) , Van Beek, Karen (VerfasserIn) , Stoica, Remus (VerfasserIn) , Saldi, Simonetta (VerfasserIn) , Boterberg, Tom (VerfasserIn) , Melchior, Patrick (VerfasserIn) , van den Heuvel-Eibrink, Marry M. (VerfasserIn) , Janssens, Geert O. (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: January 2026
In: Clinical and translational radiation oncology
Year: 2026, Jahrgang: 56, Pages: 1-9
ISSN:2405-6308
DOI:10.1016/j.ctro.2025.101051
Online-Zugang:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.ctro.2025.101051
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2405630825001430
Volltext
Verfasserangaben:Mianyong Ding, Matteo Maspero, Semi Harrabi, Emmanuel Jouglar, Sabina Vennarini, Timothy Spencer, Britta Weber, Henriette Magelssen, Karen Van Beek, Remus Stoica, Simonetta Saldi, Tom Boterberg, Patrick Melchior, Marry M. van den Heuvel-Eibrink, Geert O. Janssens
Beschreibung
Zusammenfassung:Background and purpose - Integrating deep learning (DL) for auto-contouring has significantly improved organ-at-risk (OAR) delineation in adult radiotherapy. However, its application in paediatric radiotherapy remains limited. This study evaluates DL-based auto-contouring of OARs, followed by manual revisions, for paediatric flank irradiation, focusing on delineation time, accuracy, and inter-observer variability (IOV). - Materials and methods - Twelve paediatric radiation oncologists from nine countries affiliated with the SIOP Renal Tumour Study Group participated in a two-day workshop. Participants were randomly divided into two groups: one performed manual delineation first, followed by DL-based revision, while the other group performed in reverse order. Eight thoracoabdominal OARs were delineated on non-contrast CTs of renal tumour patients (ages 1-6). DL-based contours were generated using a model for paediatric abdominal cases. Delineation time was recorded, accuracy and IOV were assessed using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, mean surface distance against a STAPLE consensus (threshold = 0.95), and an expert reference. - Results - In total, 122 manual delineations and 254 DL-based revisions were collected. DL-based auto-contouring reduced delineation time by 59 %, from 25.5 to 10.2 min. The mean DSC of all eight OARs improved from 0.91 to 0.97 using STAPLE reference and from 0.89 to 0.93 using expert reference. The pancreas exhibited the largest gain, with mean DSC increases ranging from 0.18 to 0.25. Delineation accuracy was significantly improved for seven OARs (p < 0.05), while IOV significantly decreased for the pancreas and heart in both references (p < 0.05). - Conclusion - Manually revising DL-based auto-contouring reduces delineation time, enhances accuracy, and reduces inter-observer variability in paediatric CT-based OAR delineation.
Beschreibung:Online verfügbar: 19. September 2025, Artikelversion: 15. Oktober 2025
Gesehen am 17.12.2025
Beschreibung:Online Resource
ISSN:2405-6308
DOI:10.1016/j.ctro.2025.101051

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