Validation of Risk Factors and Assessment of Their Combined Predictive Efficacy for Perioperative Complications in Laparoscopic Radical Resection of Transverse Colon Cancer

Aims/Background: Although laparoscopic surgery for colon cancer offers advantages over open surgery, such as minimal invasion and faster recovery, the transverse colon presents unique challenges due to its special anatomical location and complex vascular and lymphatic drainage. This study aimed to identify independent risk factors and to develop a corresponding combined predictor for perioperative complications following laparoscopic radical resection of transverse colon cancer. Methods: Retrospective clinical data from 170 patients with transverse colon cancer who underwent laparoscopic colectomy at The First People’s Hospital of Yongkang between January 2020 and July 2025 were analyzed. According to the Clavien–Dindo classification system, patients were divided into a complication group (Clavien–Dindo grade ≥I) and a non-complication group. Baseline demographics, surgical variables, and occurrence of perioperative complications were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for complications. The combination of these independent risk factors was then assessed as a composite predictor. Its predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Among the cohort, 43 patients experienced complications (complication group) while 127 did not (non-complication group). Univariate logistic regression analysis revealed that age, presence or absence of comorbid diabetes mellitus, type of abdominal anastomosis, and operation time were associated with perioperative complications following laparoscopic colectomy for transverse colon cancer (p 3 hours are independent risk factors for perioperative complications in patients undergoing laparoscopic resection of transverse colon cancer. The combination of these three risk factors shows good predictive value and holds promise for guiding targeted clinical interventions to improve patient outcomes.