{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["23(1)"],"submitter":["Wang Y"],"pubmed_abstract":["<h4>Background</h4>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.<h4>Materials and methods</h4>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 "],"journal":["World journal of surgical oncology"],"pagination":["27"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11773841"],"repository":["biostudies-literature"],"pubmed_title":["Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study."],"pmcid":["PMC11773841"],"pubmed_authors":["Qu C","Li J","Liu K","Li C","Cao J","Hu W","Xing C","Liu Q","Tan B","Jiang Y","Sun R","Zeng J","Zhao D","Wang Y"],"additional_accession":[]},"is_claimable":false,"name":"Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.","description":"<h4>Background</h4>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.<h4>Materials and methods</h4>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 ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2025-04-18T19:59:03.703Z","creation":"2025-04-07T07:48:49.93Z"},"accession":"S-EPMC11773841","cross_references":{"pubmed":["39875897"],"doi":["10.1186/s12957-025-03671-y"]}}