文献类型：期刊文献

英文题名：Intelligent identification system of gastric stromal tumors based on blood biopsy indicators

作者：Han, Shangjun[1,2];Song, Meijuan[1,2];Wang, Jiarui[3];Huang, Yalong[1,2];Li, Zuxi[1,2];Yang, Aijia[1,2];Sui, Changsheng[1,2];Zhang, Zeping[1,2];Qiao, Jiling[1,2];Yang, Jing[2]

第一作者：Han, Shangjun

通信作者：Yang, J[1]

机构：[1]Gansu Univ Tradit Chinese Med, Dept Clin Med Coll 1, Lanzhou, Peoples R China;[2]Gansu Prov Hosp, Dept Gen Surg, Lanzhou, Peoples R China;[3]Xuzhou Med Univ, Dept Med Informat & Engn, Xuzhou, Peoples R China

第一机构：甘肃中医药大学

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

年份：2023

卷号：23

期号：1

外文期刊名：BMC MEDICAL INFORMATICS AND DECISION MAKING

收录：;Scopus(收录号：2-s2.0-85174201023);WOS:【SCI-EXPANDED(收录号:WOS:001099637300003)】；

基金：The authors would like to thank Prof. Dr. Bin Li from The First Hospital of Lanzhou University, Dr. Zeping Huang from Lanzhou University Second Hospital and Dr. Haining Mi from Cancer Hospital of Gansu Province for their contribution to the blood data of GST patients, which were used to develop and validate the GST prediction models.

语种：英文

外文关键词：Gastric stromal tumors; GST warning system; Blood indicators; Machine learning

摘要：Background The most prevalent mesenchymal-derived gastrointestinal cancers are gastric stromal tumors (GSTs), which have the highest incidence (60-70%) of all gastrointestinal stromal tumors (GISTs). However, simple and effective diagnostic and screening methods for GST remain a great challenge at home and abroad. This study aimed to build a GST early warning system based on a combination of machine learning algorithms and routine blood, biochemical and tumour marker indicators.Methods In total, 697 complete samples were collected from four hospitals in Gansu Province, including 42 blood indicators from 318 pretreatment GST patients, 180 samples of gastric polyps and 199 healthy individuals. In this study, three algorithms, gradient boosting machine (GBM), random forest (RF), and logistic regression (LR), were chosen to build GST prediction models for comparison. The performance and stability of the models were evaluated using two different validation techniques: 5-fold cross-validation and external validation. The DeLong test assesses significant differences in AUC values by comparing different ROC curves, the variance and covariance of the AUC value.Results The AUC values of both the GBM and RF models were higher than those of the LR model, and this difference was statistically significant (P < 0.05). The GBM model was considered to be the optimal model, as a larger area was enclosed by the ROC curve, and the axes indicated robust model classification performance according to the accepted model discriminant. Finally, the integration of 8 top-ranked blood indices was proven to be able to distinguish GST from gastric polyps and healthy people with sensitivity, specificity and area under the curve of 0.941, 0.807 and 0.951 for the cross-validation set, respectively.Conclusion The GBM demonstrated powerful classification performance and was able to rapidly distinguish GST patients from gastric polyps and healthy individuals. This identification system not only provides an innovative strategy for the diagnosis of GST but also enables the exploration of hidden associations between blood parameters and GST for subsequent studies on the prevention and disease surveillance management of GST. The GST discrimination system is available online for free testing of doctors and high-risk groups at https://jzlyc.gsyy.cn/bear/mobile/index.html.