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| 直径1 cm以内RNETs患者内镜治疗后垂直切缘切除不足风险预测模型构建 |
| Risk prediction model construction for insufficient vertical margin resection of RNETs patients with diameter of less than 1cm after endoscopic treatment |
| 投稿时间：2025-10-10 |
| DOI：10.3969/j.issn.1000-0399.2026.09.012 |
| 中文关键词: 直肠 神经内分泌肿瘤 内镜 垂直切缘 危险因素 |
| 英文关键词: Rectum Neuroendocrine tumor Endoscopy Vertical margin Risk factor |
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| 摘要点击次数: 203 |
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| 中文摘要: |
| 目的 探讨直径1 cm以内直肠神经内分泌肿瘤（RNETs）患者内镜治疗后垂直切缘切除不足危险因素并构建预测模型，以期为未来更优内镜治疗方案制定及预后改善提供借鉴。方法 回顾性分析2018年1月至2025年1月于河南中医药大学第一附属医院接受内镜治疗的直径1 cm以内的197例RNETs患者相关临床资料，根据术后病理组织学评估垂直切缘状态分为不足组（n=45）和充足组（n=152）；对比两组患者的临床资料，通过单因素以及多因素分析直径1 cm以内RNETs患者内镜治疗后垂直切缘切除不足的独立影响因素。利用多因素分析的结果构建直径1 cm以内RNETs患者内镜治疗后垂直切缘切除不足的预测模型，并对模型的预测效能进行分析。结果 197例患者内镜治疗后非完整切除和垂直切缘切除不足例数分别为19例，45例；术后并发症发生率为4.57%。单因素分析结果显示，术前是否接受内镜活检、内镜手术方式及医师内镜操作经验均与直径1 cm以内直肠神经内分泌肿瘤（RNETs）患者内镜治疗后垂直切缘切除不足有关（P<0.05）。logistic多因素分析结果显示，术前接受内镜活检（OR=3.645,95%CI:1.506～8.824,P=0.004）、内镜下黏膜切除术治疗（OR=3.049,95%CI:1.306～7.116,P=0.010）均是直径1 cm以内RNETs患者内镜治疗后垂直切缘切除不足的危险因素（P<0.05）。医师内镜操作经验丰富（OR=0.242,95%CI:0.114～0.514,P<0.001）是直径1 cm以内RNETs内镜治疗后垂直切缘切除不足的独立保护因素（P<0.05）。利用多因素分析的结果构建直径1 cm以内RNETs内镜治疗后垂直切缘切除不足风险预测列线图模型。利用Bootstrap自抽样法进行列线图模型的内部验证，重复自抽样1 000次，获得校准曲线，平均绝对误差为0.075。临床决策分析显示，风险阈值概率在15%～76%，净获益率>0。ROC曲线下面积为0.744，最佳截断值为24.71%，预测灵敏度为73.33%，特异度为63.16%。结论 直径≤1 cm的RNETs内镜治疗后，垂直切缘不足与术前活检、手术方式及操作经验有关；基于这些因素构建的模型预测效能良好。 |
| 英文摘要: |
| Objective To investigate the risk factors for insufficient vertical margin resection after endoscopic treatment of rectal neuroendocrine tumors(RNETs) patients with diameter of less than 1 cm and to construct a relevant predictive model, with the aim of providing reference for the formulation of better endoscopic treatment plans and prognosis improvement in the future. Methods A retrospective analysis was conducted on the clinical data of 197 patients with RNETs measuring ≤1 cm in diameter who underwent endoscopic treatment at the First Affiliated Hospital of Henan University of Chinese Medicine from January 2018 to January 2025. According to the vertical margin status assessed by postoperative histopathology, the patients were divided into an inadequate margin group(n=45) and an adequate margin group(n=152). Clinical data of the two groups were compared, and univariate and multivariate analyses were performed to identify independent risk factors for an inadequate vertical margin after endoscopic treatment in patients with RNETs ≤1 cm in diameter. A prediction model was constructed based on the results of the multivariate analysis, and its predictive performance of was evaluated. Results Among the 197 patients, there were 19 cases of incomplete resection and 45 cases of insufficient vertical margin resection after endoscopic treatment; the incidence of postoperative complications was 4.57%. Univariate analysis showed that preoperative endoscopic biopsy, endoscopic surgical approach, and the endoscopic's experience were significantly associated with an insufficient vertical margin(P<0.05). Multivariate logistic regression analysis indicated that preoperative endoscopic biopsy(OR=3.645, 95%CI: 1.506～8.824, P=0.004) and endoscopic mucosal resection(EMR)(OR=3.049, 95%CI: 1.306～7.116, P=0.010) were independent risk factors for an insufficient vertical margin resection(P<0.05). Rich experience of the endoscopist(OR=0.242, 95%CI: 0.114～0.514, P<0.001) was an independent protective factor(P<0.05). Based on these results, a nomogram prediction model was constructed. Internal validation using the Bootstrap resampling method with 1 000 repeated resamplings yielded a calibration curve with a mean absolute error of 0.075. Decision curve analysis(DCA) revealed a net benefit rate >0 within the risk threshold probability range of 15%～76%. The receiver operating characteristic(ROC) curve showed an area under the curve(AUC) of 0.744, with an optimal cut-off value of 24.71%, predictive sensitivity of 73.33%, and specificity of 63.16%. Conclusion For RNETs ≤1 cm in diameter treated endoscopically, an inadequate vertical resection margin is associated with preoperative biopsy, surgical procedure, and operator experience. The nomogram model constructed based on these factors shows favorable predictive performance. |
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