背景:小肠腺癌(SBA)是一种罕见的胃肠道恶性肿瘤,晚期诊断会阻碍患者的生存,对治疗的复杂反应,预后不良。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。本研究旨在开发和验证基于监测的列线图,流行病学,和最终结果(SEER)数据库来预测SBA患者的癌症特异性生存率(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SEER数据库中2010年至2020年间诊断为SBA的2,064例患者的数据。患者被随机分配到训练和验证队列(7:3比例)。Kaplan-Meier生存分析,Cox多元回归,并构建列线图以分析3年和5年的CSS。使用Harrell一致性指数(C指数)评估列线图的性能,接收器工作特性(ROC)曲线下的面积,校正曲线,决策曲线分析(DCA),净重新分类改进(NRI),和综合歧视改进(IDI)。
结果:多变量Cox回归确定性别,诊断时的年龄,婚姻状况,肿瘤部位,病理分级,T级,N级,M阶段,手术,区域淋巴结检索(RORLN),和化疗作为与CSS相关的独立协变量。在培训和验证队列中,开发的列线图表现出优于AJCC分期系统的性能,C指数分别为0.764和0.759。通过ROC分析获得的3年和5年CSS预测的曲线下面积(AUC)值显着超过AJCC模型。使用校准和决策曲线验证了列线图,确认其临床实用性和优越的预测准确性。NRI和IDI表明了列线图模型的增强的预测能力。
结论:基于SEER的列线图在预测SBA患者的CSS方面提供了显著优越的能力,支持其在临床决策和个性化方法管理SBA中的潜在应用,以改善生存结局。
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.
CONCLUSIONS: 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.