ArticleJournal of applied clinical medical physics2026
Deep learning auto-contouring of target and organs-at-risk on post-catheter implant CT images for prostate HDR brachytherapy.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAccurate delineation of the prostate and surrounding organs-at-risk (OARs) is essential for HDR prostate brachytherapy. Manual contouring on post-catheter CT images is time-consuming and prone to variability due to artifacts and anatomical deformation from the implanted brachytherapy catheters. PURPOSE: To develop and validate a 3D deep learning autosegmentation model for prostate and OAR contouring on post-catheter CT images for prostate brachytherapy planning.
methodsA self-configuring U-Net architecture (nnU-Net) was trained on 206 HDR prostate brachytherapy cases and tested on an additional 36 patients. Structures included prostate PTV, bladder, rectum, and urethra identified from a Foley catheter. Performance was evaluated against a commercially available model using geometric (Dice coefficient (DSC), Hausdorff distance (HD95%), average surface distance (ASD)) and dosimetric metrics (PTV V100%, bladder and rectum D1cc, urethra D0.1cc). Statistical significance was assessed using paired Wilcoxon signed-rank tests.
resultsNnU-Net achieved superior geometric accuracy versus the commercial model for all structures within slices containing the prostate. For prostate PTV, nnU-Net yielded DSC = 0.91 +/- 0.04, HD95% = 3.62 +/- 2.23 mm, and ASD = 1.25 +/- 0.66 mm, compared to DSC = 0.78 +/- 0.07, HD95% = 9.52 +/- 4.73 mm, and ASD = 2.97 +/- 0.90 mm for the commercial model. Urethra segmentation was only provided by nnU-Net (DSC = 0.84 +/- 0.09). Dosimetric differences for nnU-Net were clinically negligible: ΔPTV V100% = -0.25 +/-2.30%, bladder ΔD1cc = 0.13 +/- 0.75 Gy, rectum ΔD1cc = 0.34 +/- 0.74 Gy, and urethra ΔD0.1cc = -0.03 +/- 0.16 Gy. Differences in PTV V100% were not significant for nnU-Net (p = 0.91) but significant for the commercial model (p < 0.001).
conclusionsA brachytherapy-specific nnU-Net model enables accurate autosegmentation of prostate and OARs on post-catheter CT images, outperforming an EBRT-trained commercial solution. Minimal dosimetric differences support clinical feasibility and potential workflow improvements in HDR prostate brachytherapy.
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