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937659.pdf (3.37 MB)

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2026-05-18

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cc-by-nc-nd (c) Casamitjana, Adrià. et al., 2026
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/230495

Robust framework for adaptive field-of-view automatic segmentation in vaginal brachytherapy for endometrial cancer

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Recurs relacionat

https://doi.org/10.1016/j.phro.2026.101000

Resum

Background and purpose: The lack of robust automatic tools for segmentation in vaginal brachytherapy (VBT) limits efficiency and reproducibility in clinical practice. We aimed to develop a framework for automatic segmentation of the clinical target volume (CTV) and organs of interest (OOIs) for endometrial cancer patients that undergo vaginal cuff brachytherapy. Material and methods: We developed a three-step framework based on nnUNet and adapted to our context to segment the CTV/OOIs on pre-treatment computed tomography (CT) scans. Our method was adaptive to different contouring protocols used in clinical practice, where images were partly labelled, by either providing labels within a region of interest and/or considering only a subset of present structures. A dataset of 289 patients treated between 2014 and 2021 was used for model development (139/35/115 for training, validation and testing). Results: The Dice similarity coefficient of the CTV was 87.7%, while OOI Dice coefficients were 72.6%, 88.9%, 86.3% for the small bowel, bladder and rectum, respectively. The average absolute D2cm3 deviations were 27.5(±58.7), 25.5(±21.0), and 21(±19.1) cGy for the small bowel, bladder and rectum, respectively. The average absolute D90 % deviation was 17.0(±19.6) cGy. Clinical transferability was assessed using a 1-4 scale, achieving a median of 4 for the CTV, 3 for bladder and rectum, and 2 for the small bowel. Conclusions: Our method segmented the CTV and OOIs from CT scans with high quality and reliable dose calculations within the relevant range. Our framework outperformed state-of-the-art methods and potentially could help reduce complexity and time in VBT protocols. Keywords: Artificial intelligence in oncology; Automatic segmentation; Dosimetry evaluation; Endometrial cancer; Vaginal cuff brachytherapy.

Matèries

Càncer d'endometri, Braquiteràpia, Intel·ligència artificial

Matèries (anglès)

Endometrial cancer, Radioisotope brachytherapy, Artificial intelligence

Citació

Col·leccions

Articles publicats en revistes (Fonaments Clínics)
Articles publicats en revistes (Cirurgia i Especialitats Medicoquirúrgiques)
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

Citació

CASAMITJANA, Adrià, et al. Robust framework for adaptive field-of-view automatic segmentation in vaginal brachytherapy for endometrial cancer. Physics and Imaging in Radiation Oncology. 2026. Vol. 39. ISSN 2405-6316. [consulted: 3 of August of 2026]. Available at: https://hdl.handle.net/2445/230495

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