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

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.

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).

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 D_2cm3 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 D_{90 %} 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.

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.