Table 1_CT-based deep learning auto-segmentation of high-risk clinical target volume in CT-guided cervical cancer brachytherapy: a single-center pragmatic study.doc

Task-level CT normalization parameters were estimated exclusively from the training partition and fixed for validation and testing; spatial cropping was image centered and contour independent.</p>Results<p>In the intact-cervix test cohort, 3D U-Net showed the most favorable descriptive combination of overlap and surface agreement (DSC 0.848 ± 0.059; HD95 2.755 ± 1.688 mm; ASD 1.008 ± 0.622 mm). In the exploratory vaginal stump cohort, corresponding values were 0.788 ± 0.060, 4.724 ± 2.434 mm, and 2.917 ± 2.150 mm.

On physician consensus review, 91/102 HR-CTV_c and 6/7 HR-CTV_v 3D U-Net contours required no or localized correction. In an ancillary 20-fraction independent-contouring analysis, physician-to-physician HR-CTV agreement was DSC 0.80 ± 0.06 and HD95 3.4 ± 1.8 mm.</p>Conclusions<p>In this internally validated CT-guided cohort, 3D U-Net provided the most favorable overall performance among the evaluated architectures for intact-cervix cases.

The postoperative results provide a preliminary feasibility signal, and confirmation in a larger cohort is required. These findings support further physician-supervised implementation, followed by multicenter external validation and cohort-level dosimetric assessment.</p>