Work

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

Year: 2026
Type: article
Study design: Observational Study
Abstract: 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.
Language: English
Subfield: Epidemiology
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $4,390
OpenAlex ID: W7140134555
FWCI:
Cites: 28
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