A fully automated CT-based pelvimetry pipeline for quantifying mid-pelvic surgical workspace in rectal cancer

Pelvimetry may aid preoperative planning in rectal cancer surgery, yet manual measurements are time-consuming and MRI-based methods require dedicated protocols. We developed a fully automated CT-based pipeline for quantifying mid-pelvic geometry and soft tissue occupancy at the ischial spine level, the narrowest corridor encountered during total mesorectal excision.

This retrospective feasibility study included 73 patients with mid-to-low rectal cancer who underwent contrast-enhanced staging CT. Automated segmentation was performed using TotalSegmentator. The pipeline extracted interspinous distance (ISD) via an anatomically anchored search strategy with valley detection, constructed a posterior pelvic triangle bounded by the bilateral ischial spines and anterior sacrum, and quantified bowel and fat occupancy within this region. Automated measurements were validated against blinded manual annotations by two independent raters. Pipeline success rates, failure taxonomy, and agreement between contrast-enhanced and non-contrast acquisitions (n = 69 paired cases) were evaluated using intraclass correlation coefficients (ICC) and Bland-Altman analysis.

The pipeline achieved complete ISD extraction in 100% of contrast-enhanced and 95.8% of non-contrast cases. In blinded validation (n = 70), automated ISD agreement against manual reference (ICC = 0.977; bias = 0.45 mm) exceeded inter-rater reliability (ICC = 0.962), and triangle-derived metrics showed a good-to-excellent agreement (ICC = 0.86-0.89, auto vs. manual). ISD measurements showed excellent agreement across contrast conditions (ICC = 0.99; mean bias = 0.09 mm; 95% limits of agreement: - 2.51 to 2.69 mm). Bone-derived triangle metrics demonstrated strong concordance (ICC = 0.90-0.93). Fat-related metrics showed systematic differences between contrast conditions but maintained good agreement (ICC = 0.87-0.91). Sex-based differences were consistent with known pelvic dimorphism.

This fully automated pipeline reliably extracts mid-pelvic geometry and soft tissue metrics from routine staging CT, with measurement agreement matching or exceeding manual inter-rater variability, offering a standardized, practical, and reproducible approach for characterizing the surgical workspace in rectal cancer patients.