Lin Yanhong, Chen Dinghang, Lu Jieming, Huang Yicheng, Han Ziyang, Kang Mingqiang
Department of Thoracic Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fuzhou, Fujian, China.
Front Oncol. 2025 Aug 20;15:1619106. doi: 10.3389/fonc.2025.1619106. eCollection 2025.
Surgery remains the primary treatment for patients with esophageal cancer (EC), yet postoperative prognosis is often unsatisfactory. Accurate prediction of cancer-specific survival (CSS) can assist clinicians in personalized treatment planning. This study aimed to develop an interactive web-based tool to estimate CSS in patients with T13N02M0 EC after surgery, based on the log odds of negative lymph nodes/T stage ratio (LONT).
A total of 2,221 patients with T13N02M0 EC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training and testing sets. Univariate Cox regression analysis was conducted to identify factors associated with CSS. Cox regression and random survival forest (RSF) models were used to compare the predictive performance of LONT and N stage. Model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. An interactive web-based tool was then constructed for individualized survival prediction.
Univariate analysis revealed that age, sex, T stage, N stage, chemotherapy, and LONT were significantly associated with CSS. ROC curve comparisons showed that LONT outperformed N stage in predictive accuracy, particularly for 1-year CSS. DCA and calibration curves indicated that the model had high predictive accuracy in both training and testing sets.
The developed interactive web-based tool provides effective estimation of 1-, 3-, and 5-year CSS, as well as survival trends, in postoperative patients with T13N02M0 EC. This tool may aid clinical decision-making by enabling more accurate individualized prognosis prediction.
手术仍然是食管癌(EC)患者的主要治疗方法,但术后预后往往不尽人意。准确预测癌症特异性生存率(CSS)有助于临床医生制定个性化的治疗方案。本研究旨在开发一种基于网络的交互式工具,以根据阴性淋巴结/T分期比值(LONT)的对数优势估计T13N02M0期EC术后患者的CSS。
从监测、流行病学和最终结果(SEER)数据库中识别出2221例T13N02M0期EC患者。将患者随机分为训练集和测试集。进行单因素Cox回归分析以确定与CSS相关的因素。使用Cox回归和随机生存森林(RSF)模型比较LONT和N分期的预测性能。使用受试者工作特征(ROC)曲线、决策曲线分析(DCA)和校准曲线评估模型性能。然后构建一个基于网络的交互式工具用于个性化生存预测。
单因素分析显示,年龄、性别、T分期、N分期、化疗和LONT与CSS显著相关。ROC曲线比较表明,LONT在预测准确性方面优于N分期,尤其是对于1年CSS。DCA和校准曲线表明,该模型在训练集和测试集中均具有较高的预测准确性。
所开发的基于网络的交互式工具能够有效估计T13N02M0期EC术后患者1年、3年和5年的CSS以及生存趋势。该工具通过实现更准确的个性化预后预测,可能有助于临床决策。