Shi Chang'e, Tao Rui, Wang Wensheng, Tang Jinzhi, Dou Zhengli, Yuan Xiaoping, Xu Guodong, Liu Huanzhong, Chen Xi
Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Gastroenterology, Anhui Public Health Clinical Center, Hefei, China.
Front Oncol. 2024 Nov 20;14:1419845. doi: 10.3389/fonc.2024.1419845. eCollection 2024.
The purpose of this study was to construct a nomogram to identify patients at high risk of gastric precancerous lesions (GPLs). This identification will facilitate early diagnosis and treatment and ultimately reduce the incidence and mortality of gastric cancer.
In this single-center retrospective cohort study, 563 participants were divided into a gastric precancerous lesion (GPL) group (n=322) and a non-atrophic gastritis (NAG) group (n=241) based on gastroscopy and pathology results. Laboratory data and demographic data were collected. A derivation cohort (n=395) was used to identify the factors associated with GPLs to develop a predictive model. Then, internal validation was performed (n=168). We used the area under the receiver operating characteristic curve (AUC) to determine the discriminative ability of the predictive model; we constructed a calibration plot to evaluate the accuracy of the predictive model; and we performed decision curve analysis (DCA) to assess the clinical practicability predictive model.
Four -predictors (i.e., age, body mass index, smoking status, and -triglycerides) were included in the predictive model. The AUC values of this predictive model were 0.715 (95% CI: 0.665-0.765) and 0.717 (95% CI: 0.640-0.795) in the derivation and internal validation cohorts, respectively. These values indicated that the predictive model had good discrimination ability. The calibration plots and DCA suggested that the predictive model had good accuracy and clinical net benefit. The Hosmer-Lemeshow test results in the derivation and validation cohorts for this predictive model were 0.774 and 0.468, respectively.
The nomogram constructed herein demonstrated good performance in terms of predicting the risk of GPLs. This nomogram can be beneficial for the early detection of patients at high risk of GPLs, thus facilitating early treatment and ultimately reducing the incidence and mortality of gastric cancer.
本研究旨在构建一种列线图,以识别胃癌前病变(GPLs)的高危患者。这种识别将有助于早期诊断和治疗,并最终降低胃癌的发病率和死亡率。
在这项单中心回顾性队列研究中,根据胃镜检查和病理结果,将563名参与者分为胃癌前病变(GPL)组(n = 322)和非萎缩性胃炎(NAG)组(n = 241)。收集实验室数据和人口统计学数据。使用一个推导队列(n = 395)来识别与GPLs相关的因素,以建立一个预测模型。然后,进行内部验证(n = 168)。我们使用受试者操作特征曲线下面积(AUC)来确定预测模型的判别能力;构建校准图以评估预测模型的准确性;并进行决策曲线分析(DCA)以评估预测模型的临床实用性。
预测模型纳入了四个预测因子(即年龄、体重指数、吸烟状况和甘油三酯)。该预测模型在推导队列和内部验证队列中的AUC值分别为0.715(95%CI:0.665 - 0.765)和0.717(95%CI:0.640 - 0.795)。这些值表明预测模型具有良好的判别能力。校准图和DCA表明预测模型具有良好的准确性和临床净效益。该预测模型在推导队列和验证队列中的Hosmer-Lemeshow检验结果分别为0.774和0.468。
本文构建的列线图在预测GPLs风险方面表现良好。该列线图有助于早期发现GPLs高危患者,从而促进早期治疗并最终降低胃癌的发病率和死亡率。