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Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer

Abstract This study evaluated segmentation accuracy, efficiency, and radiomic feature stability for manual (MD), artificial intelligence-based (AI), and hybrid (MD + AI) contouring of pelvic organs on planning CT (pCT) and HyperSight cone-beam CT (hCBCT) for adaptive radiotherapy. Dice similarity an...

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Збережено в:
Автори: Ralf Schmidt, Dion Bajerski, Alicia S. Bicu, Victor Siefert, Miriam Eckl, Marvin Willam, Matthias F. Froelich, Stefan O. Schoenberg, Michael Ehmann, Daniel Buergy, Florian Stieler, Jens Fleckenstein, Frank A. Giordano, Judit Boda-Heggemann, Constantin Dreher
Формат: Artigo
Мова:Inglês
Опубліковано: Nature Portfolio 2026-04-01
Серія:Scientific Reports
Предмети:
Radiomics
Autocontouring
Adaptive radiotherapy
HyperSight-CBCT
Prostate cancer
Ethos
Онлайн доступ:https://doi.org/10.1038/s41598-026-46359-3
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