UNASSIGNED: We extracted clinical data for patients diagnosed with intestinal-type gastric adenocarcinoma between 2010 and 2015 from the SEER database, selecting 257 cases based on predefined inclusion and exclusion criteria. Independent risk factors for overall survival (OS) and cancer-specific survival (CSS) were identified using a Cox regression model. A nomogram model for predicting OS or CSS was developed from the Cox risk regression analysis and validated through the consistency index (C-index), ROC curve, and calibration curve.
UNASSIGNED: Age, primary tumor resection, chemotherapy, lymph node metastasis, and tumor size were identified as independent prognostic factors for OS and CSS (P < 0.05). The nomogram model, constructed from these indicators, demonstrated superior predictive consistency for OS and CSS compared to the AJCC-TNM staging system. ROC curve analysis confirmed the model\'s higher accuracy, and calibration curve analysis indicated good agreement between the nomogram\'s predictions and actual observed outcomes.
UNASSIGNED: The nomogram model derived from SEER database analyses accurately predicts OS and CSS for patients with intestinal-type gastric adenocarcinoma. This model promises to facilitate more tailored treatments in clinical practice.
■我们从SEER数据库中提取了2010年至2015年间诊断为肠型胃腺癌的患者的临床数据,根据预定义的纳入和排除标准选择257例。使用Cox回归模型确定总生存期(OS)和癌症特异性生存期(CSS)的独立危险因素。从Cox风险回归分析中建立了预测OS或CSS的列线图模型,并通过一致性指数(C指数)进行了验证,ROC曲线,和校准曲线。
■年龄,原发肿瘤切除,化疗,淋巴结转移,肿瘤大小是OS和CSS的独立预后因素(P<0.05)。列线图模型,根据这些指标构建,与AJCC-TNM分期系统相比,OS和CSS具有更好的预测一致性。ROC曲线分析证实了模型的较高准确性,和校准曲线分析表明,列线图的预测和实际观察到的结果之间有很好的一致性。
■来自SEER数据库分析的列线图模型可以准确预测肠型胃腺癌患者的OS和CSS。该模型有望在临床实践中促进更多定制的治疗。