Third Department of Surgery, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei, China (mainland).
Med Sci Monit. 2022 May 9;28:e935696. doi: 10.12659/MSM.935696.
BACKGROUND Neoadjuvant chemotherapy is an important treatment for advanced gastric cancer, but it has been unclear whether neoadjuvant chemotherapy is closely related to lymph node metastasis. Therefore, based on the disease characteristics of the cT2-cT4N0M0 gastric cancer population, this study established a nomogram prediction model of lymph node metastasis risk in this gastric cancer population to help clinicians optimize clinical decision-making. MATERIAL AND METHODS We analyzed the data of 336 patients with advanced gastric cancer with CT imaging stage of cT2-cT4N0M0 admitted to the Third Department of the Fourth Hospital of Hebei Medical University from 2015 to 2021. Combined with the results of univariate and multivariate logistic regression analysis, 7 indicators were selected to establish a nomogram prediction model. The calibration curves, ROC curves, and decision curves were drawn against the nomogram model using R language. RESULTS The results showed that the AUC value of the model and the external validation data set were 0.925 and 0.911, respectively. The P value of the Hosmer-Lemeshow test for the internal validation dataset was 0.082, and the P value of Hosmer-Lemeshow test for the external validation dataset was 0.076.The decision curve results showed that when the threshold probability was 0.1-0.9, this model could benefit patients by predicting the risk of lymph node metastasis in patients with advanced gastric cancer, and formulating appropriate treatment schemes accordingly. CONCLUSIONS This nomogram has shown good discrimination and fit, and can also be combined with imaging examination to screen the populations suitable for neoadjuvant chemotherapy, avoid the risk of misdiagnosis of N staging to the greatest extent, and to assist clinicians to optimize clinical decision-making.
新辅助化疗是治疗晚期胃癌的重要手段，但新辅助化疗与淋巴结转移的关系尚不清楚。因此，本研究基于 cT2-cT4N0M0 期胃癌人群的疾病特征，建立了该人群淋巴结转移风险的列线图预测模型，以帮助临床医生优化临床决策。
我们分析了 2015 年至 2021 年期间，河北医科大学第四医院第三科收治的 336 例 CT 影像学分期为 cT2-cT4N0M0 的晚期胃癌患者的数据。结合单因素和多因素 logistic 回归分析的结果，选择 7 个指标建立列线图预测模型。使用 R 语言对列线图模型进行校准曲线、ROC 曲线和决策曲线分析。
模型和外部验证数据集的 AUC 值分别为 0.925 和 0.911。内部验证数据集的 Hosmer-Lemeshow 检验 P 值为 0.082，外部验证数据集的 Hosmer-Lemeshow 检验 P 值为 0.076。决策曲线结果表明，当阈值概率为 0.1-0.9 时，该模型可以通过预测晚期胃癌患者淋巴结转移的风险，使患者受益，并制定相应的治疗方案。
该列线图具有良好的区分度和拟合度，还可以结合影像学检查筛选适合新辅助化疗的人群，最大程度避免 N 分期的误诊风险，辅助临床医生优化临床决策。
