Deep Learning-Based CT Segmentation of a Non-radiopaque Hydrogel Rectal Spacer in Prostate Radiotherapy.
Deep learning CT segmentation shows good accuracy for non-radiopaque rectal spacers.
Deep Learning-Based CT Segmentation of a Non-radiopaque Hydrogel Rectal Spacer in Prostate Radiotherapy.
The study evaluated the feasibility and geometric accuracy of CT-based automatic segmentation of a non-radiopaque hydrogel rectal spacer using a commercial deep learning platform (OncoStudio) and determined whether accurate spacer delineation can be achieved without MRI while preserving the contouring quality of adjacent organs.
This retrospective study included 21 patients with localized prostate cancer who underwent external beam radiotherapy with a non-radiopaque hydrogel rectal spacer (SpaceOAR; Boston Scientific).
this shows good accuracy in automatically drawing the rectal spacer's boundary
The adjacent organs showed high geometric agreement, with mean dice similarity coefficient values of 0.876, 0.890, and 0.963 for the prostate, rectum, and bladder, respectively.
No systematic over- or under-segmentation was observed, and the mean surface distance for all structures was below 2.5 mm.
CT-based automatic segmentation using OncoStudio provided clinically acceptable boundary delineation of a non-radiopaque hydrogel rectal spacer without requiring MRI, while maintaining the contouring quality of the surrounding organ.
These findings suggest that CT-only automatic contouring can reduce spacer-specific MRI use and associated registration uncertainty in prostate radiotherapy.