文献类型：期刊文献

英文题名：Establishment and validation of a nomogram model for early diagnosis of gastric cancer: a large-scale cohort study

作者：Wang, Haiyu[1];Ding, Yumin[1];Zhao, Shujing[1];Li, Kaixu[1];Li, Dehong[2]

第一作者：汪海燕

通信作者：Li, DH[1]

机构：[1]Gansu Univ Chinese Med, Sch Publ Hlth, Lanzhou, Gansu, Peoples R China;[2]Gansu Prov Hosp, Dept Clin Lab, Lanzhou, Gansu, Peoples R China

第一机构：甘肃中医药大学公共卫生学院

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Clin Lab, Lanzhou, Gansu, Peoples R China.

年份：2024

卷号：14

外文期刊名：FRONTIERS IN ONCOLOGY

收录：;Scopus(收录号：2-s2.0-85211624196);WOS:【SCI-EXPANDED(收录号:WOS:001375529800001)】；

语种：英文

外文关键词：gastric cancer; nomogram model; early diagnosis; marker; characteristic pattern

摘要：Purpose Identifying high-risk populations and diagnosing gastric cancer (GC) early remains challenging. This study aimed to establish and verify a nomogram model for the early diagnosis of GC based on conventional laboratory indicators.Methods We performed a retrospective analysis of the clinical data of 2,770 individuals with first diagnosis of GC and 1,513 patients with benign gastric disease from January 2018 to December 2022. The cases were divided into the training set and validation set randomly, with a ratio of 7:3. Variable screening was performed by least absolute shrinkage and selection operator (LASSO) and logistic regression analysis. A nomogram was constructed in the training set to assist in the early diagnosis of GC.Results There were 4283 patients included in the study, with 2998 patients assigned in the training set and 1285 patients in the validation set. Through LASSO regression and logistic regression analysis, independent variables associated with GC were identified, including CEA, CA199, LYM, HGB, MCH, MCHC, PLT, ALB, TG, HDL, and AFR. The nomogram model was constructed using the above 11 independent indicators. The AUC was 0.803 for the training set and 0.797 for the validation set, indicating that the model showed high clinical diagnostic efficacy. The calibration curves and decision curve analysis (DCA) of the nomogram presented good calibration and clinical application ability.Conclusion Based on the analysis of large sample size, we constructed a nomogram model with 11 routine laboratory indicators, which showed good discrimination ability and calibration.