| 安全明,缪莉莉,王 磊,等.结合p53构建胃癌根治术后患者生存率的列线图预测模型[J].中国肿瘤,2023,32(5):394-400. |
| 结合p53构建胃癌根治术后患者生存率的列线图预测模型 |
| Construction of Nomogram Model Combined with p53 for Predicting Survival of Gastric Cancer Patients After Radical Gastrectomy |
| 投稿时间：2022-07-20 |
| DOI：10.11735/j.issn.1004-0242.2023.05.A009 |
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| 中文关键词: p53 胃癌根治术 生存率 列线图预测模型 |
| 英文关键词:p53 radical gastrectomy survival rate nomogram prediction model |
| 基金项目:宁夏回族自治区重点研发计划项目（2021BEG03037）；宁夏医科大学科学研究基金资助项目（XM2021015） |
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| 中文摘要: |
| 摘 要：［目的］ 结合p53蛋白构建预测胃癌根治术后生存率的列线图模型，验证并评价该模型的预测价值。［方法］ 收集2016年1月至2017年5月在宁夏医科大学总医院胃肠外科行胃癌手术患者203例的临床病理及生存数据资料，建立数据库，采用Kaplan-Meier单因素分析、Log-rank检验和Cox多因素回归分析，筛选出影响胃癌术后患者总生存期（overall survival，OS）的独立预后因素，通过R软件构建胃癌根治术后1年、3年、5年生存率的列线图模型，计算Cox 模型的一致性指数( C指数)、绘制受试者工作特征曲线（receiver operating characteristic curve，ROC 曲线），计算曲线下面积（area under the cure，AUC）及绘制校准曲线验证模型的有效性。将上述患者作为训练组，并收集2017年6月至7 月宁夏医科大学总医院胃肠外科51例行胃癌根治手术的患者资料作为验证组进行外部验证。［结果］ 有203例患者被纳入本研究，通过Kaplan-Meier单因素及Cox 多因素回归分析显示，影响胃癌根治术后生存率的独立预后因素分别为年龄、浸润深度、淋巴结转移、Lauren分型以及p53表达状态。通过R软件构建胃癌根治术后的1年、3年、5年生存率的列线图模型，C指数为0.76（95%CI：0.71～0.81），预测价值高于第8版美国癌症联合会（AJCC）-TNM分期系统（C指数为0.68），胃癌根治术后5年ROC 曲线的AUC 为0.88（95%CI：0.83～0.93），校准曲线显示胃癌患者的生存率的预测校准曲线与理想参考线拟合度良好，外部验证提示训练组和验证组中校准曲线一致性良好，提示列线图模型的预测能力较为准确。［结论］ p53为重要的独立预后因素之一，结合p53构建的列线图模型可准确预测胃癌根治术的生存率，为临床胃癌根治术后生存率的评估提供依据。 |
| 英文摘要: |
| Abstract：［Purpose］ To construct a nomogram model combined with p53 for predicting the survival of gastric cancer patients after radical gastrectomy. ［Methods］ The clinical data of 203 patients who underwent gastric cancer surgery in General Hospital of Ningxia Medical University from January 2016 to May 2017 were collected. The factors influencing the postoperative survival of patients were analyzed， and a nomogram model for predicting survival of patients was developed by R software. The consistency index(C index) of the Cox model and receiver operating characteristic curve(ROC curve) was used to evaluate the effectiveness of the model. Fifty one patients who underwent radical surgery for gastric cancer during June to July 2017 were used as the validation set for external validation. ［Results］ Kaplan-Meier univariate and Cox multivariate regression analysis showed that age， depth of invasion， lymph node metastasis， and Lauren classification and p53 expression status were the independent prognostic factors for the survival of patients， based on which a nomogram model for predicting 1-year， 3-year and 5-year survival after radical gastrectomy was constructed. The C index of the model was 0.76(95%CI： 0.71～0.81)， the predictive value was higher than the 8th edition of the AJCC-TNM staging system(C index was 0.68). The AUC of the model for predicting 5-year survival after radical gastrectomy was 0.88(95%CI： 0.83～0.93). Fit test showed that the predictive value was very close to the actual observation value. The external validation suggested that the calibration curves in the training set and validation set were in good agreement and the prediction ability of nomogram model was more accurate. ［Conclusion］ The nomogram model constructed in the study can accurately predict the survival of gastric cancer patients after radical gastrectomy. |
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