ArticleAnnals of surgical oncology2025
A Novel Liver Metastasis Score for Patients Undergoing Surgical Resection of Gastroenteropancreatic Neuroendocrine Tumors: A Multi-institutional Study.
Article in Annals of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine learning predicts prognosis in patients with gastroenteropancreatic neuroendocrine tumors with liver metastases.Discover oncology · 2025Article
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17 authors.
Funding
Abstract
backgroundLiver metastasis impacts survival in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs); however, current guidelines lack consensus on post-resection surveillance and adjuvant therapy. A comprehensive risk stratification tool is needed to guide personalized management.
objectiveWe aimed to develop and validate a predictive model for liver metastasis risk after surgical resection of GEP-NETs that incorporates pathological factors and adjuvant therapy.
methodsPatients with GEP-NETs who underwent surgical resection with curative intent at three major Chinese hospitals (2010-2022) were identified. Univariable and multivariable Cox regression analysis identified independent risk factors of liver metastasis. The liver metastasis score (LMS) was developed using weighted risk factors and validated by tenfold cross-validation.
resultsAmong the 724 patients included in the analytic cohort, liver metastasis occurred in 66 patients (9.1%) at a median of 36 months; patients with liver metastasis had a worse 5-year overall survival (no liver metastasis 63.6% vs. liver metastasis 95.8%; p < 0.001). Independent predictors were Ki-67 index (hazard ratio [HR] 10.36 for Ki-67 3-20%, HR 18.30 for Ki-67 >20%, vs. <3%), vascular invasion (HR 5.03), lymph node metastases (HR 2.24), and lack of adjuvant therapy (HR 3.03). The LMS demonstrated excellent discrimination (C-index 0.888) and stratified patients into low, intermediate, and high-risk relative to 5-year risk of liver metastasis: 2.9%, 20.8%, and 49.7%, respectively (p < 0.001).
conclusionsThe novel LMS effectively predicted the risk of liver metastasis after surgical resection of GEP-NETs. This validated model can help guide personalized surveillance and adjuvant treatment strategies, potentially improving outcomes for high-risk patients.
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