Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study
Abstract Background With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately d...
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BMC
2025-01-01
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| Series: | World Journal of Surgical Oncology |
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| Online Access: | https://doi.org/10.1186/s12957-025-03671-y |
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| author | Yubing Wang Chao Qu Jiange Zeng Yumin Jiang Ruitao Sun Changlei Li Jian Li Chengzhi Xing Bin Tan Kui Liu Qing Liu Dianpeng Zhao Jingyu Cao Weiyu Hu |
| author_facet | Yubing Wang Chao Qu Jiange Zeng Yumin Jiang Ruitao Sun Changlei Li Jian Li Chengzhi Xing Bin Tan Kui Liu Qing Liu Dianpeng Zhao Jingyu Cao Weiyu Hu |
| author_sort | Yubing Wang |
| collection | DOAJ |
| description | Abstract Background With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms. Materials and methods We retrospectively analysed the patients’ clinical baseline data, serological indicators, and ultrasound imaging data. Using 12 machine learning algorithms, 110 combination predictive models were constructed. The models were evaluated using internal and external cohort validation, receiver operating characteristic curves, area under the curve (AUC) values, calibration curves, and clinical decision curves to determine the best predictive model. Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. The model-selected features included gallbladder wall thickness, polyp size, polyp echo, and pedicle. Evaluation through external cohort validation, calibration curves, and clinical decision curves further confirmed its excellent predictive ability for distinguishing gallbladder adenomas from cholesterol polyps. Additionally, this study identified age, adenosine deaminase level, and metabolic syndrome as potential predictive factors for gallbladder adenomas. Conclusion This study employed the machine learning combination algorithms and preoperative ultrasound imaging data to construct an SVM + RF predictive model, enabling effective preoperative differentiation of gallbladder adenomas and cholesterol polyps. These findings will assist clinicians in accurately assessing the risk of GPLs and providing personalised treatment strategies. |
| format | Article |
| id | doaj-art-e89f2af8bf5846aa9a593032f87d02a5 |
| institution | Kabale University |
| issn | 1477-7819 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | World Journal of Surgical Oncology |
| spelling | doaj-art-e89f2af8bf5846aa9a593032f87d02a52025-02-02T12:27:34ZengBMCWorld Journal of Surgical Oncology1477-78192025-01-0123111010.1186/s12957-025-03671-yEstablishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective studyYubing Wang0Chao Qu1Jiange Zeng2Yumin Jiang3Ruitao Sun4Changlei Li5Jian Li6Chengzhi Xing7Bin Tan8Kui Liu9Qing Liu10Dianpeng Zhao11Jingyu Cao12Weiyu Hu13Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary Surgery, Shandong Second Medical UniversityDepartment of Hepatobiliary Surgery, Yantai Mountain HospitalDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary Surgery, Yantai Mountain HospitalDepartment of Hepatobiliary Surgery, Shandong Second Medical UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityDepartment of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao UniversityAbstract Background With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms. Materials and methods We retrospectively analysed the patients’ clinical baseline data, serological indicators, and ultrasound imaging data. Using 12 machine learning algorithms, 110 combination predictive models were constructed. The models were evaluated using internal and external cohort validation, receiver operating characteristic curves, area under the curve (AUC) values, calibration curves, and clinical decision curves to determine the best predictive model. Results Among the 110 combination predictive models, the Support Vector Machine + Random Forest (SVM + RF) model demonstrated the highest AUC values of 0.972 and 0.922 in the training and internal validation sets, respectively, indicating an optimal predictive performance. The model-selected features included gallbladder wall thickness, polyp size, polyp echo, and pedicle. Evaluation through external cohort validation, calibration curves, and clinical decision curves further confirmed its excellent predictive ability for distinguishing gallbladder adenomas from cholesterol polyps. Additionally, this study identified age, adenosine deaminase level, and metabolic syndrome as potential predictive factors for gallbladder adenomas. Conclusion This study employed the machine learning combination algorithms and preoperative ultrasound imaging data to construct an SVM + RF predictive model, enabling effective preoperative differentiation of gallbladder adenomas and cholesterol polyps. These findings will assist clinicians in accurately assessing the risk of GPLs and providing personalised treatment strategies.https://doi.org/10.1186/s12957-025-03671-yMachine learningPredictive modelGallbladder polypsGallbladder adenoma |
| spellingShingle | Yubing Wang Chao Qu Jiange Zeng Yumin Jiang Ruitao Sun Changlei Li Jian Li Chengzhi Xing Bin Tan Kui Liu Qing Liu Dianpeng Zhao Jingyu Cao Weiyu Hu Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study World Journal of Surgical Oncology Machine learning Predictive model Gallbladder polyps Gallbladder adenoma |
| title | Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study |
| title_full | Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study |
| title_fullStr | Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study |
| title_full_unstemmed | Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study |
| title_short | Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study |
| title_sort | establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning a multicentre retrospective study |
| topic | Machine learning Predictive model Gallbladder polyps Gallbladder adenoma |
| url | https://doi.org/10.1186/s12957-025-03671-y |
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