Peng Chao, Li Ting
Department of Gastrointestinal Surgery, Gansu Provincial Cancer Hospital, Lanzhou, China.
Department of Anesthesiology, Gansu Provincial People's Hospital, Lanzhou, China.
J Gastrointest Oncol. 2025 Jun 30;16(3):950-964. doi: 10.21037/jgo-24-745. Epub 2025 Jun 18.
Signet ring cell (SRC) gastric cancer is known for its aggressive behavior and poor prognosis. To date, no nomogram has been specifically developed for SRC gastric cancer patients post-surgery. Our objective was to create a nomogram to personalize the prediction of both overall survival (OS) and cancer-specific survival (CSS).
We analyzed data from 3,481 patients with histologically confirmed SRC gastric cancer, diagnosed between 2004 and 2021, using information from the Surveillance Epidemiology, and End Results (SEER) database. Patients diagnosed between 2004 and 2015 were randomly divided into two groups: one for training and the other for validation. Additionally, patients diagnosed between 2016 and 2021 were selected as the second validation set. Univariate and multivariate Cox regression models were employed to identify key predictors, which were then used to construct a nomogram. Only the variables significantly linked to OS were incorporated into the final model. The nomogram's accuracy and performance were tested using several evaluation tools, including the concordance index (C-index), calibration plots, and receiver operating characteristic (ROC) curves.
Univariate and multivariate analyses identified race, chemotherapy, T and M stages, age, tumor size, primary tumor location, and lymph node ratio (LNR) as independent prognostic factors for OS and CSS. These key variables were used to construct the nomogram. The model's predictive accuracy was reflected by a C-index of 0.748 [95% confidence interval (CI): 0.734-0.763] for OS and 0.763 (95% CI: 0.761-0.764) for CSS. In the first validation cohort, the C-index for OS was 0.746 (95% CI: 0.702-0.791), and for CSS, it was 0.751 (95% CI: 0.706-0.796). In the second validation cohort, the C-index for OS was 0.784 (95% CI: 0.733-0.836), while for CSS, it was 0.818 (95% CI: 0.767-0.870). For CSS in the training set, the AUC values were 0.760, 0.821, and 0.833 at 1, 3, and 5 years, respectively. In the first validation set, the area under the curve (AUC) values were 0.738, 0.800, and 0.824 for the same time points. In the second validation set, the AUC values were 0.808, 0.872, and 0.885 at 1, 3, and 5 years, respectively. For OS predictions, the AUC in the training set was 0.737, 0.812, and 0.825 at 1, 3, and 5 years. In the first validation set, the AUC values were 0.727, 0.789, and 0.813, while in the second validation set, the AUC values were 0.792, 0.851, and 0.839.
We have successfully developed effective nomograms to evaluate the prognosis of patients with SRC gastric cancer, focusing on both OS and cumulative survival CSS. These nomograms integrate key clinical factors and provide valuable tools for personalized patient prognosis, enhancing clinical decision-making and potentially improving treatment outcomes.
印戒细胞(SRC)胃癌以其侵袭性生物学行为和不良预后而闻名。迄今为止,尚未专门为SRC胃癌术后患者开发列线图。我们的目标是创建一个列线图,以个性化预测总生存期(OS)和癌症特异性生存期(CSS)。
我们使用监测、流行病学和最终结果(SEER)数据库中的信息,分析了2004年至2021年间3481例经组织学确诊的SRC胃癌患者的数据。2004年至2015年间诊断的患者被随机分为两组:一组用于训练,另一组用于验证。此外,2016年至2021年间诊断的患者被选为第二个验证集。采用单因素和多因素Cox回归模型确定关键预测因素,然后用于构建列线图。只有与OS显著相关的变量被纳入最终模型。使用几种评估工具测试列线图的准确性和性能,包括一致性指数(C指数)、校准图和受试者操作特征(ROC)曲线。
单因素和多因素分析确定种族、化疗、T和M分期、年龄、肿瘤大小、原发肿瘤位置和淋巴结比率(LNR)为OS和CSS的独立预后因素。这些关键变量用于构建列线图。该模型的预测准确性通过OS的C指数为0.748[95%置信区间(CI):0.734 - 0.763]和CSS的C指数为0.763(95%CI:0.761 - 0.764)来反映。在第一个验证队列中,OS的C指数为0.746(95%CI:0.702 - 0.791),CSS的C指数为0.751(95%CI:0.706 - 0.796)。在第二个验证队列中,OS的C指数为0.784(95%CI:0.733 - 0.836),而CSS的C指数为0.818(95%CI:0.767 - 0.870)。对于训练集中的CSS,1年、3年和5年的AUC值分别为0.760、0.821和0.833。在第一个验证集中,相同时间点的曲线下面积(AUC)值为0.738、0.800和0.824。在第二个验证集中,1年、3年和5年的AUC值分别为0.808、0.872和0.885。对于OS预测,训练集中1年、3年和5年的AUC分别为0.737、0.812和0.825。在第一个验证集中,AUC值为0.727、0.789和0.813,而在第二个验证集中,AUC值为0.792、0.851和0.839。
我们成功开发了有效的列线图,以评估SRC胃癌患者的预后,重点关注OS和累积生存CSS。这些列线图整合了关键临床因素,为个性化患者预后提供了有价值的工具,增强了临床决策并可能改善治疗结果。