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Establishment of a LASSO Regression-Based Risk Prediction Model for Early Recurrence of SiewertⅡ/Ⅲ Adenocarcinoma of Esophagogastric Junction Post-Surgery
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摘要:
目的 探讨Siewert Ⅱ/Ⅲ 型食管胃结合部腺癌（ adenocarcinoma ofesophagogastric junction，AEG）根治术后早期复发的危险因素，构建可视化预测模型。 方法 回顾性分析 2016 年 1 月至 2021 年 3 月兰州大学第二医院诊断为Siewert Ⅱ/Ⅲ型 AEG 且接受根治性切除术患者的临床病理学资料，将样本以 7:3 的比例随机分为建模组与验证组。采用LASSO-logistic 回归分析法筛选出预测Siewert Ⅱ/Ⅲ型 AEG 早期复发的变量，并构建早期复发预测模型。 基于 Bootstrap 法进行 1000 次重复抽样验证模型。 绘制受试者工作特征曲线（receiver operating characteristic，ROC），计算曲线下面积（ area under curve，AUC），绘制校准曲线和决策曲线（ decision curve analysis，DCA）对模型的稳定性进行评估。 结果 根据纳入与排除标准，共 320 例Siewert Ⅱ/Ⅲ型 AEG 患者最终被纳入分析，其中 2 年内复发者 122例，2 年内无复发者 198 例； LASSO-logistic 回归分析显示，AJCC 分期、 分化程度、 糖类抗原 199、 癌胚抗原、 中性粒细胞与淋巴细胞比值及肿瘤长径是Siewert Ⅱ/Ⅲ型 AEG 早期复发的独立预测因素，依此构建预测模型并绘制列线图。 绘制 ROC曲线得到建模组 AUC为 0.836，95%CI（ 0.785～0.887），灵敏度为 81.4%，特异度为 85.6%；验证组 AUC 为 0.812，95%CI（ 0.711～0.912），灵敏度为 80.6%，特异度为 87.7%。 建模组与验证组的校准曲线显示拟合曲线与参考曲线接近，表明模型具有较高稳定性。 DCA 曲线显示阈值概率在 0.05～0.75 时模型具有良好的净收益。 结论 本研究基于 LASSO-logistic 开发了预测 Siewert Ⅱ/Ⅲ型 AEG 早期复发的多因素模型，有助于临床判断患者预后，并为 Siewert Ⅱ/Ⅲ型 AEG 患者术后病情监测与管理提供参考依据。
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关键词:
- SiewertⅡ/Ⅲ型食管胃结合部腺癌 /
- 复发 /
- 预测模型 /
- LASSO-logistic回归 /
- 列线图
Abstract:Objective To investigate the risk factors for early relapse after curative resection of Siewert type II/III adenocarcinoma of the esophagogastric junction (AEG) and to construct a visual predictive model. Methods A retrospective analysis was conducted on the clinicopathological data of patients diagnosed with Siewert type II/III AEG who underwent curative resection at the Second Hospital of Lanzhou University from January 2016 to March 2021. The samples were randomly divided into a training group and a validation group in a 7:3 ratio. The LASSO-logistic regression method was used to select variables predictive of early recurrence of Siewert type II/III AEG and to construct a predictive model for early recurrence. The model was validated through 1000 bootstrap resampling. Receiver operating characteristic (ROC) curves， area under the curve (AUC)， calibration curves， and decision curve analysis (DCA) were used to evaluate the model's stability. Results According to the inclusion and exclusion criteria of this study， a total of 320 Siewert type II/III AEG patients were included， with 122 experiencing recurrence within two years and 198 without recurrence within the same timeframe. Lasso-logistic regression analysis revealed AJCC staging， degree of differentiation， CA199， CEA， NLR， and tumor maximum diameter as independent predictive factors for early recurrence of Siewert type II/III AEG. A predictive model was constructed with these factors and depicted as a nomogram. The AUC of the ROC curve for the training group was 0.836， 95%CI (0.785–0.887)， with a sensitivity of 81.4% and a specificity of 85.6%； for the validation group， the AUC was 0.812， 95%CI (0.711–0.912)， with a sensitivity of 80.6% and a specificity of 87.7%. Calibration curves for both the training and validation groups displayed curves close to the reference line， indicating high model stability. The DCA curve showed the model provided a good net benefit with threshold probabilities between 0.05 and 0.75. Conclusions A multivariate model to predict early relapse in patients with Siewert type II/III AEG was developed using LASSO-logistic regression， which may be instrumental in assessing patient prognoses and in guiding postoperative surveillance and management for patients with Siewert type II/III AEG.
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