Intelligent identification system of gastric stromal tumors based on blood biopsy indicators
Abstract 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 chall...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02324-y |
| _version_ | 1797559251289243648 |
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| author | Shangjun Han Meijuan Song Jiarui Wang Yalong Huang Zuxi Li Aijia Yang Changsheng Sui Zeping Zhang Jiling Qiao Jing Yang |
| author_facet | Shangjun Han Meijuan Song Jiarui Wang Yalong Huang Zuxi Li Aijia Yang Changsheng Sui Zeping Zhang Jiling Qiao Jing Yang |
| author_sort | Shangjun Han |
| collection | DOAJ |
| description | Abstract 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 . |
| first_indexed | 2024-03-10T17:42:46Z |
| format | Article |
| id | doaj.art-5ff2e6fb0c5942f3902ea0ea38902668 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-10T17:42:46Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-5ff2e6fb0c5942f3902ea0ea389026682023-11-20T09:38:43ZengBMCBMC Medical Informatics and Decision Making1472-69472023-10-0123111210.1186/s12911-023-02324-yIntelligent identification system of gastric stromal tumors based on blood biopsy indicatorsShangjun Han0Meijuan Song1Jiarui Wang2Yalong Huang3Zuxi Li4Aijia Yang5Changsheng Sui6Zeping Zhang7Jiling Qiao8Jing Yang9Department of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of Medical Information and Engineering, Xuzhou Medical UniversityDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of the First Clinical Medical College, Gansu University of Traditional Chinese MedicineDepartment of General Surgery, Gansu Provincial HospitalAbstract 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 .https://doi.org/10.1186/s12911-023-02324-yGastric stromal tumorsGST warning systemBlood indicatorsMachine learning |
| spellingShingle | Shangjun Han Meijuan Song Jiarui Wang Yalong Huang Zuxi Li Aijia Yang Changsheng Sui Zeping Zhang Jiling Qiao Jing Yang Intelligent identification system of gastric stromal tumors based on blood biopsy indicators BMC Medical Informatics and Decision Making Gastric stromal tumors GST warning system Blood indicators Machine learning |
| title | Intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| title_full | Intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| title_fullStr | Intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| title_full_unstemmed | Intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| title_short | Intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| title_sort | intelligent identification system of gastric stromal tumors based on blood biopsy indicators |
| topic | Gastric stromal tumors GST warning system Blood indicators Machine learning |
| url | https://doi.org/10.1186/s12911-023-02324-y |
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