作者投稿
专家审稿
编辑办公
邮件订阅
RSS
Establishment of a LASSO Regression-Based Risk Prediction Model for Early Recurrence of SiewertⅡ/Ⅲ Adenocarcinoma of Esophagogastric Junction Post-Surgery
-
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。
-
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.
-
[1] Ajani J A, D'Amico T A, Bentrem D J, et al. Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology[J]. J Natl Compr Canc Netw, 2022, 20(2):167-92. [2] 中华医学会外科学分会腹腔镜与内镜外科学组. Siewert Ⅱ型食管胃结合部腺癌腔镜手术治疗中国专家共识(2023版)[J] 中华消化外科杂志. 2023, (07):799-809. [3] 陈凌, 刘凤林. 食管胃结合部腺癌的定义和分型:从历史到现状[J] 中华外科杂志. 2022, (09):813-8. [4] Arnold M, Ferlay J, van Berge Henegouwen M I, et al. Global burden of oesophageal and gastric cancer by histology and subsite in 2018[J]. Gut, 2020, 69(9):1564-71. [5] Xu H, Zhang L, Miao J, et al. Patterns of recurrence in adenocarcinoma of the esophagogastric junction:a retrospective study[J]. World J Surg Oncol, 2020, 18(1):144. [6] Kang W-M, Meng Q-B, Yu J-C, et al. Factors associated with early recurrence after curative surgery for gastric cancer[J]. World J Gastroenterol, 2015, 21(19):5934-40. [7] Zhao J, Zhao J, Du F, et al. Cardia and Non-Cardia Gastric Cancer Have Similar Stage-for-Stage Prognoses After R0 Resection:a Large-Scale, Multicenter Study in China[J]. J Gastrointest Surg, 2016, 20(4):700-7. [8] Feng Y, Jiang Y, Zhao Q, et al. Long-term outcomes and prognostic factor analysis of resected Siewert type II adenocarcinoma of esophagogastric junction in China:a seven-year study[J]. BMC Surg, 2020, 20(1):302. [9] Yang Y, Huang X, Zhou L, et al. Clinical use of tumor biomarkers in prediction for prognosis and chemotherapeutic effect in esophageal squamous cell carcinoma[J]. BMC Cancer, 2019, 19(1):526. [10] Shimada H, Noie T, Ohashi M, et al. Clinical significance of serum tumor markers for gastric cancer:a systematic review of literature by the Task Force of the Japanese Gastric Cancer Association[J]. Gastric Cancer, 2014, 17(1):26-33. [11] Jing R, Cui M, Ju S, et al. The Changes and Clinical Significance of Preoperative and Postoperative Serum CEA and CA19-9 in Gastric Cancer[J]. Clin Lab, 2020, 66(4). [12] He K, Si L, Pan X, et al. Preoperative Systemic Immune-Inflammation Index (SII) as a Superior Predictor of Long-Term Survival Outcome in Patients With Stage I-II Gastric Cancer After Radical Surgery[J]. Front Oncol, 2022, 12:829689. [13] Riley R D, Ensor J, Snell K I E, et al. Calculating the sample size required for developing a clinical prediction model[J]. BMJ, 2020, 368:m441. [14] Li S, Ying X, Shan F, et al. Laparoscopic vs. open lower mediastinal lymphadenectomy for Siewert type II/III adenocarcinoma of esophagogastric junction:An exploratory, observational, prospective, IDEAL stage 2b cohort study (CLASS-10 study)[J]. Chin J Cancer Res, 2022, 34(4):406-14. [15] Guo Z, Guo H, Tian Y, et al. Nomograms for Predicting Disease-Free Survival in Patients With Siewert Type II/III Adenocarcinoma of the Esophagogastric Junction Receiving Neoadjuvant Therapy and Radical Surgery[J]. Front Oncol, 2022, 12:908229. [16] Gao Y, Xin L, Lin H, et al. Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction:a nationwide, multicohort, prospective study[J]. Lancet Gastroenterol Hepatol, 2023, 8(5):432-45. [17] Takeda F R, Kodama Pertille Ramos M F, Pereira M A, et al. Predictive factors of recurrence in adenocarcinoma of the esophagogastric junction in the multimodal era[J]. Am J Surg, 2021, 221(3):631-6. [18] 赵龙, 蒋洪朋, 杨长江, 等. Siewert Ⅱ型和Ⅲ型食管胃结合部腺癌的临床病理特征及预后影响因素分析[J] 中华消化外科杂志. 2022, (12):1560-6. [19] Al-Batran S-E, Goetze T O, Mueller D W, et al. The RENAISSANCE (AIO-FLOT5) trial:effect of chemotherapy alone vs. chemotherapy followed by surgical resection on survival and quality of life in patients with limited-metastatic adenocarcinoma of the stomach or esophagogastric junction-a phase III trial of the German AIO/CAO-V/CAOGI[J]. BMC Cancer, 2017, 17(1):893. [20] Chen J, Xia Y-J, Liu T-Y, et al. Development and validation of a survival nomogram for patients with Siewert type II/III adenocarcinoma of the esophagogastric junction based on real-world data[J]. BMC Cancer, 2021, 21(1):532. [21] 张克昌, 范林广, 王杰, 等. Siewert Ⅱ型和Ⅲ型食管胃结合部腺癌下纵隔淋巴结转移影响因素及转移规律分析[J] 中华消化外科杂志. 2022, (10):1370-5. [22] Hedrick C C, Malanchi I. Neutrophils in cancer:heterogeneous and multifaceted[J]. Nat Rev Immunol, 2022, 22(3):173-87. [23] De Meo M L, Spicer J D. The role of neutrophil extracellular traps in cancer progression and metastasis[J]. Semin Immunol, 2021, 57:101595. [24] Szczerba B M, Castro-Giner F, Vetter M, et al. Neutrophils escort circulating tumour cells to enable cell cycle progression[J]. Nature, 2019, 566(7745):553-7. [25] Herre M, Cedervall J, Mackman N, et al. Neutrophil extracellular traps in the pathology of cancer and other inflammatory diseases[J]. Physiol Rev, 2023, 103(1):277-312. [26] Man Y-G, Stojadinovic A, Mason J, et al. Tumor-infiltrating immune cells promoting tumor invasion and metastasis:existing theories[J]. J Cancer, 2013, 4(1):84-95. [27] Coussens L M, Werb Z. Inflammation and cancer[J]. Nature, 2002, 420(6917):860-7. [28] Rossi J-F, Lu Z Y, Massart C, et al. Dynamic Immune/Inflammation Precision Medicine:The Good and the Bad Inflammation in Infection and Cancer[J]. Front Immunol, 2021, 12:595722. [29] Stotz M, Pichler M, Absenger G, et al. The preoperative lymphocyte to monocyte ratio predicts clinical outcome in patients with stage III colon cancer[J]. Br J Cancer, 2014, 110(2):435-40. [30] Wang Y, Hu X, Xu W, et al. Prognostic value of a novel scoring system using inflammatory response biomarkers in non-small cell lung cancer:A retrospective study[J]. Thorac Cancer, 2019, 10(6):1402-11. [31] Xiong S, Dong L, Cheng L. Neutrophils in cancer carcinogenesis and metastasis[J]. J Hematol Oncol, 2021, 14(1):173. [32] 于小鹏, 陈家璐, 唐玥, 等. 基于炎症相关指标术前预测肝内胆管癌淋巴结转移的列线图模型构建[J] 中华外科杂志. 2023, (04):321-9. [33] Deng K, Yang L, Hu B, et al. The prognostic significance of pretreatment serum CEA levels in gastric cancer:a meta-analysis including 14651 patients[J]. PLoS One, 2015, 10(4):e0124151. [34] Xiao Y, Zhang J, He X, et al. Diagnostic values of carcinoembryonic antigen in predicting peritoneal recurrence after curative resection of gastric cancer:a meta-analysis[J]. Ir J Med Sci, 2014, 183(4):557-64. -
点击查看大图
计量
- 文章访问数: 52
- HTML全文浏览量: 9
- PDF下载量: 2
- 被引次数: 0
下载:
