Department of General Surgery, General Hospital of Northern Theater Command (Teaching Hospital of China Medical University), Shenyang, China.
Jinzhou Medical University, Jinzhou, China.
J Gastrointest Cancer. 2024 Sep;55(3):1111-1124. doi: 10.1007/s12029-024-01046-2. Epub 2024 May 3.
This study aimed to compare the prognostic value of rectal cancer by comparing different lymph node staging systems, and a nomogram was constructed based on superior lymph node staging.
Overall, 8700 patients with rectal cancer was obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The area under the curve (AUC), the C index, and the Akaike informativeness criteria (AIC) were used to examine the predict ability of various lymph node staging methods. Prognostic indicators were assessed using univariate and multivariate COX regression, and further correlation nomograms were created after the data were randomly split into training and validation cohorts. To evaluate the effectiveness of the model, the C index, calibration curves, decision curves (DCA), and receiver operating characteristic curve (ROC) were used. We ran Kaplan-Meier survival analyses to look for variations in risk classification.
While compared to the N-stage positive lymph node ratio (LNR), the log odds ratio of positive lymph nodes (LODDS) had the highest predictive effectiveness. Multifactorial COX regression analyses were used to create nomograms for overall survival (OS) and cancer-specific survival (CSS). The C indices of OS and CSS for this model were considerably higher than those for TNM staging in the training cohort. The created nomograms demonstrated good efficacy based on ROC, rectification, and decision curves. Kaplan-Meier survival analysis revealed notable variations in patient survival across various patient strata.
Compared to AJCC staging, the LODDS-based nomograms have a more accurate predictive effectiveness in predicting OS and CSS in patients with rectal cancer.
本研究旨在通过比较不同的淋巴结分期系统来比较直肠癌的预后价值，并构建基于优势淋巴结分期的列线图。
本研究从 2010 年至 2015 年的监测、流行病学和最终结果（SEER）数据库中获得了 8700 例直肠癌患者的数据。使用曲线下面积（AUC）、C 指数和 Akaike 信息量标准（AIC）来评估各种淋巴结分期方法的预测能力。使用单因素和多因素 COX 回归评估预后指标，并在将数据随机分为训练和验证队列后进一步构建相关联的列线图。为了评估模型的有效性，使用 C 指数、校准曲线、决策曲线（DCA）和接收器操作特征曲线（ROC）进行评估。我们进行了 Kaplan-Meier 生存分析，以寻找风险分类的变化。
与 N 分期阳性淋巴结比例（LNR）相比，阳性淋巴结的对数优势比（LODDS）具有最高的预测效果。使用多因素 COX 回归分析为总生存（OS）和癌症特异性生存（CSS）创建了列线图。该模型在训练队列中的 OS 和 CSS 的 C 指数明显高于 TNM 分期。基于 ROC、校正和决策曲线，所创建的列线图显示出良好的效果。Kaplan-Meier 生存分析显示，不同患者分层的患者生存存在显著差异。
与 AJCC 分期相比，基于 LODDS 的列线图在预测直肠癌患者的 OS 和 CSS 方面具有更高的预测准确性。
