Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy

Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (−0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: −15.6 ± 11.5%, p = 0.0002; long-cranial: −20.4 ± 7.6%, p = 0.0126; long-caudal: −16.1 ± 9.4%, p