Patient-specific virtual three-dimensional surgical navigation for gastric cancer surgery: A prospective study for preoperative planning and intraoperative guidance.

Introduction: Abdominal computed tomography (CT) can accurately demonstrate organs and vascular structures around the stomach, and its potential role for image guidance is becoming increasingly established. However, solely using two-dimensional CT images to identify critical anatomical structures is undeniably challenging and not surgeon-friendly. To validate the feasibility of a patient-specific 3-D surgical navigation system for preoperative planning and intraoperative guidance during robotic gastric cancer surgery. Materials and methods: A prospective single-arm open-label observational study was conducted. Thirty participants underwent robotic distal gastrectomy for gastric cancer using a virtual surgical navigation system that provides patient-specific 3-D anatomical information with a pneumoperitoneum model using preoperative CT-angiography. Turnaround time and the accuracy of detecting vascular anatomy with its variations were measured, and perioperative outcomes were compared with a control group after propensity-score matching during the same study period. Results: Among 36 registered patients, 6 were excluded from the study. Patient-specific 3-D anatomy reconstruction was successfully implemented without any problems in all 30 patients using preoperative CT. All vessels encountered during gastric cancer surgery were successfully reconstructed, and all vascular origins and variations were identical to operative findings. The operative data and short-term outcomes between the experimental and control group were comparable. The experimental group showed shorter anesthesia time (218.6 min Conclusions: Patient-specific 3-D surgical navigation system for robotic gastrectomy for gastric cancer is clinically feasible and applicable with an acceptable turnaround time. This system enables patient-specific preoperative planning and intraoperative navigation by visualizing all the anatomy required for gastrectomy in 3-D models without any error. Clinical trial registration: Clinicaltrials.gov, identifier NCT05039333.