Development and validation of a risk prediction model for piecemeal resection during endoscopic resection of gastric GISTs

BACKGROUND: Piecemeal resection (PR) during endoscopic resection (ER) of gastric gastrointestinal stromal tumors (gGISTs) is associated with an increased risk of tumor recurrence and incomplete resection. Identifying risk factors and developing a predictive model for PR may aid in preoperative planning and patient counseling. METHODS: This multi-center retrospective study analyzed 809 patients who underwent ER for gGISTs. Patients were divided into a training cohort (TC, n = 387), an internal validation cohort (IVC, n = 166), and an external validation cohort (EVC, n = 256). Baseline characteristics were compared between the en bloc resection and PR groups in the TC. Independent risk factors for PR were identified using multivariate logistic regression analysis, based on which a scoring system was developed. The model underwent internal and external validation, and its performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: In the TC, tumor size ≥ 2.5 cm (OR = 2.61, 95% CI 1.28-5.45, P = 0.009), irregular shape (OR = 5.03, 95% CI 2.28-11.08, P < 0.001), severe intraoperative bleeding (OR = 5.53, 95% CI 2.23-13.55, P < 0.001), and operator experience < 50 cases (OR = 4.23, 95% CI 2.10-8.73, P < 0.001) were independently associated with piecemeal resection. A scoring system based on these factors showed good predictive performance, with AUCs of 0.799 in the TC, 0.847 in the IVC, and 0.842 in an EVC. Sensitivity and specificity ranged from 0.652 to 0.818 and 0.736 to 0.833, respectively. CONCLUSION: We developed and validated a simple scoring system incorporating tumor size, shape, intraoperative bleeding, and operator experience to predict the risk of piecemeal resection during endoscopic resection of gGISTs. This tool may assist in preoperative risk stratification and optimize treatment strategies.