Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features
Abstract Background The clinical course and manifestations of subepithelial lesions (SEL) patients tend to vary from different pathological types, therefore the accurate diagnosis of SEL would undoubtedly be beneficial to the treatment and prognosis of SEL patients. Based on the decision tree method...
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BMC
2025-07-01
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| Series: | BMC Gastroenterology |
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| Online Access: | https://doi.org/10.1186/s12876-025-03993-x |
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| author | Shen Su Shun-Yao Wu Yun-He Tang Lin-Lin Ren Fan Yin Yu-Shuang Xu Xiao-Yu Li Hua Liu Shao-Hua Zhang Xing-Lin Zhang Zi-Bin Tian Tao Mao |
| author_facet | Shen Su Shun-Yao Wu Yun-He Tang Lin-Lin Ren Fan Yin Yu-Shuang Xu Xiao-Yu Li Hua Liu Shao-Hua Zhang Xing-Lin Zhang Zi-Bin Tian Tao Mao |
| author_sort | Shen Su |
| collection | DOAJ |
| description | Abstract Background The clinical course and manifestations of subepithelial lesions (SEL) patients tend to vary from different pathological types, therefore the accurate diagnosis of SEL would undoubtedly be beneficial to the treatment and prognosis of SEL patients. Based on the decision tree method, we developed a novel classification model for SELs by combining endoscopy with endoscopic ultrasound (EUS). Methods We retrospectively collected data from 469 patients hospitalized in the Affiliated Hospital of Qingdao University between January 2017 to November 2021 for endoscopic resection. Chi-square test (P < 0.05), independence test (P < 0.001), and Pearson correlation analysis (|r|<0.8) were performed to identify significant variables among endoscopic and EUS features, which were subsequently incorporated into decision tree analysis. Finally, a hierarchical diagnostic model based on multiple decision trees was constructed. The predictive performance of the model was obtained through a five-fold cross-validation, and each decision tree model was evaluated by the area under the curve (AUC) and F1. Results A total of 13 variables were included in the construction of the model. The overall accuracy of this hierarchical model was 75.12%. The AUC values for each pathology type, namely gastrointestinal stromal tumor (GIST) and schwannoma, leiomyoma, inflammatory fibroid polyp, heterotopic pancreas, and lipoma, were 0.882, 0.866, 0.964, 0.863, and 0.953, respectively. And F1 of them were 0.777, 0.697, 0.658, 0.904, and 0.698, respectively. Conclusions This decision tree-based hierarchical model can potentially assist in the preoperative diagnosis of SEL and guide clinical decision-making for the individualized treatment of SEL patients. |
| format | Article |
| id | doaj-art-a5dad27bb89f4cc28a069f38356558a3 |
| institution | Kabale University |
| issn | 1471-230X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Gastroenterology |
| spelling | doaj-art-a5dad27bb89f4cc28a069f38356558a32025-08-20T03:46:03ZengBMCBMC Gastroenterology1471-230X2025-07-0125111210.1186/s12876-025-03993-xDiagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography featuresShen Su0Shun-Yao Wu1Yun-He Tang2Lin-Lin Ren3Fan Yin4Yu-Shuang Xu5Xiao-Yu Li6Hua Liu7Shao-Hua Zhang8Xing-Lin Zhang9Zi-Bin Tian10Tao Mao11Affiliated Hospital of Qingdao UniversityQingdao UniversityAffiliated Hospital of Qingdao UniversityAffiliated Hospital of Qingdao UniversityQingdao Municipal Center for Disease Control and PreventionAffiliated Hospital of Qingdao UniversityAffiliated Hospital of Qingdao UniversityAffiliated Hospital of Qingdao UniversityAffiliated Hospital of Qingdao UniversityQingdao UniversityAffiliated Hospital of Qingdao UniversityAffiliated Hospital of Qingdao UniversityAbstract Background The clinical course and manifestations of subepithelial lesions (SEL) patients tend to vary from different pathological types, therefore the accurate diagnosis of SEL would undoubtedly be beneficial to the treatment and prognosis of SEL patients. Based on the decision tree method, we developed a novel classification model for SELs by combining endoscopy with endoscopic ultrasound (EUS). Methods We retrospectively collected data from 469 patients hospitalized in the Affiliated Hospital of Qingdao University between January 2017 to November 2021 for endoscopic resection. Chi-square test (P < 0.05), independence test (P < 0.001), and Pearson correlation analysis (|r|<0.8) were performed to identify significant variables among endoscopic and EUS features, which were subsequently incorporated into decision tree analysis. Finally, a hierarchical diagnostic model based on multiple decision trees was constructed. The predictive performance of the model was obtained through a five-fold cross-validation, and each decision tree model was evaluated by the area under the curve (AUC) and F1. Results A total of 13 variables were included in the construction of the model. The overall accuracy of this hierarchical model was 75.12%. The AUC values for each pathology type, namely gastrointestinal stromal tumor (GIST) and schwannoma, leiomyoma, inflammatory fibroid polyp, heterotopic pancreas, and lipoma, were 0.882, 0.866, 0.964, 0.863, and 0.953, respectively. And F1 of them were 0.777, 0.697, 0.658, 0.904, and 0.698, respectively. Conclusions This decision tree-based hierarchical model can potentially assist in the preoperative diagnosis of SEL and guide clinical decision-making for the individualized treatment of SEL patients.https://doi.org/10.1186/s12876-025-03993-xGastric subepithelial lesionsEndoscopyEndoscopic ultrasoundDecision treeDiagnosis |
| spellingShingle | Shen Su Shun-Yao Wu Yun-He Tang Lin-Lin Ren Fan Yin Yu-Shuang Xu Xiao-Yu Li Hua Liu Shao-Hua Zhang Xing-Lin Zhang Zi-Bin Tian Tao Mao Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features BMC Gastroenterology Gastric subepithelial lesions Endoscopy Endoscopic ultrasound Decision tree Diagnosis |
| title | Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| title_full | Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| title_fullStr | Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| title_full_unstemmed | Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| title_short | Diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| title_sort | diagnosis model for gastric submucosal tumor based on multiple decision trees comprising endoscopic and endoscopic ultrasonography features |
| topic | Gastric subepithelial lesions Endoscopy Endoscopic ultrasound Decision tree Diagnosis |
| url | https://doi.org/10.1186/s12876-025-03993-x |
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