Distinguishing second primary tumors from local recurrence in type 1 gastric neuroendocrine tumors: a risk-stratified management strategy.

BACKGROUND

Type 1 gastric neuroendocrine tumors (gNETs) exhibit high recurrence rates. However, distinguishing true local recurrence from second primary tumors (SPTs) remains challenging.

OBJECTIVES

To characterize the endoscopic features of SPTs, identify independent risk factors, and develop a risk-stratification model to guide personalized surveillance.

METHODS

We retrospectively analyzed 70 patients with type 1 gNETs at Peking University Third Hospital between May 2015 and February 2025. SPTs were strictly defined as lesions detected outside the initial resection scar during follow-up, whereas local recurrence was defined as lesions within the scar. Logistic regression was used to identify independent risk factors. A nomogram was constructed, and disease-free survival (DFS) was analyzed.

RESULTS

During a median follow-up of 17 months, SPTs were identified in 38 patients (54.3%), whereas local recurrence occurred in only 6 (8.6%), confirming that post-resection lesions are predominantly SPTs. Morphologically, SPTs were significantly smaller than primary tumors (0.4 cm vs. 0.5 cm, P < 0.001) and more frequently exhibited flat morphology (36.3% vs. 17.6%, P = 0.007). Lymphovascular invasion (LVI) (OR = 10.61, P = 0.035) and submucosal invasion (OR = 4.69, P = 0.006) were identified as independent predictors. A risk-stratification nomogram based on these factors showed good discriminatory power (AUC = 0.758). Based on these factors, patients were stratified into low-, medium-, and high-risk groups. The high-risk group (with LVI and submucosal invasion) demonstrated a rapid disease progression, with no patients remaining disease-free at 3 years (P = 0.015).

CONCLUSIONS

Surveillance lesions in type 1 gNETs are primarily SPTs characterized by subtle, flat morphology, necessitating high-quality endoscopic observation. The proposed risk-stratification model based on LVI and invasion depth effectively identifies high-risk patients, justifying intensified monitoring for this subgroup while reducing the burden for low-risk individuals.