基于 SEER 数据库的列线图模型构建与验证：用于预测小肠腺癌患者癌症特异性生存的研究。

BACKGROUND: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging. METHODS: We analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: Multivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model. CONCLUSION: The SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

背景：小肠腺癌（SBA）是一种罕见的胃肠道恶性肿瘤，由于诊断较晚、对治疗的复杂反应和预后不良，生存率受到影响。准确的预后工具对于优化治疗策略和改善患者预后至关重要。本研究旨在基于监测、流行病学和最终结果（SEER）数据库开发和验证一种列线图，以预测 SBA 患者的癌症特异性生存（CSS），并将其与传统的美国癌症联合委员会（AJCC）分期进行比较。

方法：我们分析了来自 SEER 数据库的 2064 例 2010 年至 2020 年间诊断为 SBA 的患者的数据。患者被随机分配到训练和验证队列（比例为 7:3）。采用 Kaplan-Meier 生存分析、Cox 多变量回归和列线图对 3 年和 5 年 CSS 进行分析。通过 Harrell 的一致性指数（C 指数）、接受者操作特征（ROC）曲线下面积、校准曲线、决策曲线分析（DCA）、净重新分类改善（NRI）和综合判别改善（IDI）评估列线图的性能。

结果：多变量 Cox 回归确定了性别、诊断时年龄、婚姻状况、肿瘤部位、病理分级、T 分期、N 分期、M 分期、手术、区域淋巴结检索（RORLN）和化疗是与 CSS 相关的独立协变量。在训练和验证队列中，所开发的列线图均表现出优于 AJCC 分期系统的性能，C 指数分别为 0.764 和 0.759。3 年和 5 年 CSS 预测的 ROC 分析获得的曲线下面积（AUC）值显著优于 AJCC 模型。通过校准和决策曲线对列线图进行验证，证实了其临床实用性和优越的预测准确性。NRI 和 IDI 表明列线图模型具有增强的预测能力。

结论：基于 SEER 的列线图在预测 SBA 患者 CSS 方面具有显著优势，支持其在临床决策和管理 SBA 的个性化方法中应用，以改善生存结果。