Deep Learning-Based CT Segmentation of a Non-radiopaque Hydrogel Rectal Spacer in Prostate Radiotherapy.

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Abstract

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). CT-based automatic segmentation of the rectal spacer, prostate, rectum, and bladder was performed on the planning CT using OncoStudio (version 2.0; Oncosoft Inc.). Reference contours were manually delineated on the planning CT with reference to co-registered T2-weighted MRI. Geometric accuracy was assessed using the Dice similarity coefficient (DSC), 95th-percentile Hausdorff distance (HD₉₅), and mean surface distance (MSD). The mean DSC for the rectal spacer was 0.787 ± 0.073, with a mean HD₉₅ of 4.03 ± 1.83 mm and a mean MSD of 1.18 ± 0.63 mm. The adjacent organs showed high geometric agreement, with mean DSC 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 MSD 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.

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