Jia Jinyan, Yu Zixuan, Zhang Maorun, Hu Fang, Liu Gang
Department of General Surgery, Tianjin Medical University General Hospital; China Tianjin General Surgery Institute; Tianjin Key Laboratory of Precise Vascular Reconstruction and Organ Function Repair.
Department of General Surgery, Tianjin Medical University General Hospital; China Tianjin General Surgery Institute; Tianjin Key Laboratory of Precise Vascular Reconstruction and Organ Function Repair; Department of Nursing, Tianjin Medical University General Hospital.
J Vis Exp. 2025 Apr 18(218). doi: 10.3791/67941.
Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered limited attention. This study investigates the prognostic predictive capacity of the log odds of positive lymph nodes (LODDS), lymph node ratio (LNR), and pN staging in SRCC patients using machine learning models (Random Forest, XGBoost, and Neural Network) alongside competing risk models. Relevant data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. For the machine learning models, prognostic factors for cancer-specific survival (CSS) were identified through univariate and multivariate Cox regression analyses, followed by the application of three machine learning methods-XGBoost, RF, and NN-to ascertain the optimal lymph node staging system. In the competing risk model, univariate and multivariate competing risk analyses were employed to identify prognostic factors, and a nomogram was constructed to predict the prognosis of SRCC patients. The area under the receiver operating characteristic curve (AUC-ROC) and calibration curves were utilized to assess the model's performance. A total of 2,409 SRCC patients were included in this study. To validate the effectiveness of the model, an additional cohort of 15,122 colorectal cancer patients, excluding SRCC cases, was included for external validation. Both the machine learning models and the competing risk nomogram exhibited strong performance in predicting survival outcomes. Compared to pN staging, the LODDS staging systems demonstrated superior prognostic capability. Upon evaluation, machine learning models and competing risk models achieved excellent predictive performance characterized by good discrimination, calibration, and interpretability. Our findings may assist in informing clinical decision-making for patients.
淋巴结状态是患者重要的预后预测指标；然而，结直肠印戒细胞癌（SRCC）的预后受到的关注有限。本研究使用机器学习模型（随机森林、XGBoost和神经网络）以及竞争风险模型，调查SRCC患者中阳性淋巴结对数优势（LODDS）、淋巴结比率（LNR）和pN分期的预后预测能力。相关数据从监测、流行病学和最终结果（SEER）数据库中提取。对于机器学习模型，通过单变量和多变量Cox回归分析确定癌症特异性生存（CSS）的预后因素，随后应用三种机器学习方法——XGBoost、RF和NN——来确定最佳淋巴结分期系统。在竞争风险模型中，采用单变量和多变量竞争风险分析来确定预后因素，并构建列线图以预测SRCC患者的预后。利用受试者操作特征曲线下面积（AUC-ROC）和校准曲线来评估模型的性能。本研究共纳入2409例SRCC患者。为验证模型的有效性，额外纳入15122例结直肠癌患者（不包括SRCC病例）的队列进行外部验证。机器学习模型和竞争风险列线图在预测生存结果方面均表现出强大性能。与pN分期相比，LODDS分期系统显示出更好的预后能力。经评估，机器学习模型和竞争风险模型均实现了出色的预测性能，具有良好的区分度、校准度和可解释性。我们的研究结果可能有助于为患者的临床决策提供参考。
