Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China; Sun Yat-sen University Cancer Center, Guangzhou, China.
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Int J Biol Sci. 2020 Feb 10;16(7):1230-1237. doi: 10.7150/ijbs.39161. eCollection 2020.
Gastric cancer (GC) with lymph node metastasis (LNM) at diagnosis is associated with a unstable prognosis and indefinite survival times. The aim of the present study was to construct and validate a model for the Overall survival (OS) estimation for patients with LNM. The nomogram was constructed to predict the OS for LNM-positive GC using the primary group of 836 patients and validated using an independent cohort of 411 patients. Factors in the nomogram were identified by multivariate Cox hazard analysis. The predictive capability of nomogram was evaluated by calibration analysis and decision curve analysis. Multivariate analysis suggested that eight pre-treatment characteristics were used for developing the nomogram. In the primary cohort, the C-index for OS prediction was 0.788 (95% CI: 0.753-0.823), while in validation cohort, the C-index for OS prediction was 0.769 (95% CI: 0. 720-0.818). The calibration plot for the probability of OS and decision curve analyses showed an optimal agreement. Based on the nomogram, we could divided patients into three groups: low-risk group, middle-risk group and a high-risk group(p <0.001).Taken together, we have provided an easy-to-used and accurate tool for predicting OS, furthermore could be used for risk stratification of OS of LNM-positive GC patients.
在诊断时伴有淋巴结转移 (LNM) 的胃癌 (GC) 预后不稳定,生存时间不确定。本研究旨在构建和验证用于预测 LNM 阳性 GC 患者总生存 (OS) 的模型。使用原发性 836 例患者的组构建了用于预测 LNM 阳性 GC 的 OS 的列线图,并使用 411 例独立患者的队列进行了验证。列线图中的因素通过多变量 Cox 风险分析确定。通过校准分析和决策曲线分析评估了列线图的预测能力。多变量分析表明,有 8 个治疗前特征用于开发列线图。在原发性队列中,OS 预测的 C 指数为 0.788(95%CI:0.753-0.823),而在验证队列中,OS 预测的 C 指数为 0.769(95%CI:0.720-0.818)。OS 概率的校准图和决策曲线分析显示出最佳一致性。根据列线图,我们可以将患者分为三组:低危组、中危组和高危组(p<0.001)。总之,我们提供了一种易于使用且准确的预测 OS 的工具,并且可以用于分层 LNM 阳性 GC 患者的 OS 风险。