Anatomy-guided computational framework for classifying vascular ligation and lymphadenectomy in oncologic...

The optimal vascular ligation strategy and lymphadenectomy level in oncological sigmoidectomy remain controversial, with inconsistent definitions and a lack of standardized postoperative assessment. This study aimed to anatomically and radiologically define D2 and D3 lymphadenectomy in sigmoid colon cancer and to develop an objective multimodal protocol for postoperative classification of vascular ligation and recurrence patterns.

A three-phase multimodal anatomical study was conducted. Phase 1 involved cadaveric dissections simulating D2 lymphadenectomy and D3 dissection with either low or high ligation of the inferior mesenteric artery (IMA). Phase 2 retrospectively assessed 14 patients with pre- and postoperative contrast-enhanced CT scans to classify vascular ligation type and recurrence pattern. Phase 3 validated these findings through AI-assisted computational segmentation and 3D reconstruction.

In cadaveric simulation, each vascular strategy (D2, D3-low tie, D3-high tie) was anatomically characterized in terms of arterial division point, venous drainage control, and residual mesocolon, allowing systematic differentiation of the three approaches. Radiological evaluation successfully identified the level of vascular ligation in all cases. Among patients with recurrence (n = 5), the classification protocol distinguished mesenteric from non-mesenteric recurrences based on vascular territory. The 3D reconstruction phase showed full concordance between the radiological classification and the 3D model regarding both the level of inferior mesenteric artery ligation and the anatomical localization of locoregional recurrence.

This standardized anatomical-radiological workflow, integrating cadaveric dissection, CT-based vascular analysis, and AI-assisted 3D reconstruction, provides an objective tool to classify the level of vascular ligation performed in oncological sigmoidectomy and to anatomically categorize locoregional recurrence, establishing a foundation for future surgical audit and outcome studies, and representing a step toward AI-supported surgical audit systems capable of standardizing vascular ligation classification and recurrence mapping.