Zheng Qi-Xuan, Xu Jia-Hao, Yang Fa-Ji, Liu Zhi-Peng, Wang Ming-Da, Hao Yi-Jie, Li Chao, Niu Zhe-Yu, Xu Xin-Fei, Gao Heng-Jun, Li Yi-Fan, Gong Jin-Bo, Chen Zhong, Pawlik Timothy M, Shen Feng, Lu Jun, Yang Tian
Department of Hepatobiliary Surgery, Shandong Provincial Hospital, Shandong Provincial Hospital Affiliated to Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China.
Ann Surg Oncol. 2025 Feb;32(2):1176-1186. doi: 10.1245/s10434-024-16389-0. Epub 2024 Oct 31.
Liver 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.
We 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.
Patients 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.
Among 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).
The 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.
肝转移影响胃肠胰腺神经内分泌肿瘤(GEP-NETs)患者的生存率;然而,目前的指南在切除术后监测和辅助治疗方面缺乏共识。需要一种全面的风险分层工具来指导个性化管理。
我们旨在开发并验证一种用于GEP-NETs手术切除后肝转移风险的预测模型,该模型纳入了病理因素和辅助治疗。
确定在中国三家主要医院(2010 - 2022年)接受根治性手术切除的GEP-NETs患者。单变量和多变量Cox回归分析确定肝转移的独立危险因素。使用加权危险因素制定肝转移评分(LMS),并通过十倍交叉验证进行验证。
在纳入分析队列的724例患者中,66例(9.1%)发生肝转移,中位时间为36个月;发生肝转移的患者5年总生存率较差(无肝转移为63.6%,肝转移为95.8%;p < 0.001)。独立预测因素为Ki-67指数(Ki-67为3 - 20%时,风险比[HR]为10.36;Ki-67 > 20%时,HR为18.30,对比< 3%时)、血管侵犯(HR为5.03)、淋巴结转移(HR为2.24)以及未接受辅助治疗(HR为3.03)。LMS显示出良好的区分度(C指数为0.888),并根据5年肝转移风险将患者分为低、中、高风险组:分别为2.9%、20.8%和49.7%(p < 0.001)。
新型LMS有效地预测了GEP-NETs手术切除后肝转移的风险。这种经过验证的模型有助于指导个性化监测和辅助治疗策略,可能改善高危患者的预后。