{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["89"],"submitter":["Feng X"],"pubmed_abstract":["<h4>Background</h4>Primary retroperitoneal neoplasms (PRNs) are a diverse group of tumors that pose significant diagnostic challenges. Currently, no multicenter-validated diagnostic model exists for multiple PRN types based on computed tomography (CT) images. This study aimed to develop and validate an end-to-end deep learning model, REMIND (REtroperitoneal neoplasMs artificial-INtelligence Diagnosis), for the accurate diagnosis and segmentation of PRNs using enhanced CT images.<h4>Methods</h4>Patients from 12 Chinese centers between January 2012 and June 2024 were involved in this study. The dataset comprised patients with histologically confirmed PRNs, including seven types of PRNs: dedifferentiated liposarcoma, well-differentiated liposarcoma, leiomyosarcoma, ganglioneuroma, lymphoma, s"],"journal":["EClinicalMedicine"],"pagination":["103498"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12508580"],"repository":["biostudies-literature"],"pubmed_title":["End-to-end deep learning model for the diagnosis and segmentation of primary retroperitoneal neoplasm: a multicenter cohort study."],"pmcid":["PMC12508580"],"pubmed_authors":["Meng T","Hao Q","Wang B","Gao J","Deng L","Zhao T","Feng C","Zhang Q","Shao C","Kang F","Xu B","Zhuang A","Wang X","Chen R","Song Z","Chen Q","Yang L","Gu Y","Feng X","He H","Cui J","Li Q","Liang B","Yang Y","Li S","Zhang Z","Zhang Y","Yang P","Yang Q"],"additional_accession":[]},"is_claimable":false,"name":"End-to-end deep learning model for the diagnosis and segmentation of primary retroperitoneal neoplasm: a multicenter cohort study.","description":"<h4>Background</h4>Primary retroperitoneal neoplasms (PRNs) are a diverse group of tumors that pose significant diagnostic challenges. Currently, no multicenter-validated diagnostic model exists for multiple PRN types based on computed tomography (CT) images. This study aimed to develop and validate an end-to-end deep learning model, REMIND (REtroperitoneal neoplasMs artificial-INtelligence Diagnosis), for the accurate diagnosis and segmentation of PRNs using enhanced CT images.<h4>Methods</h4>Patients from 12 Chinese centers between January 2012 and June 2024 were involved in this study. The dataset comprised patients with histologically confirmed PRNs, including seven types of PRNs: dedifferentiated liposarcoma, well-differentiated liposarcoma, leiomyosarcoma, ganglioneuroma, lymphoma, s","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-04T10:12:13.541Z","creation":"2026-05-08T03:10:27.328Z"},"accession":"S-EPMC12508580","cross_references":{"pubmed":["41079030"],"doi":["10.1016/j.eclinm.2025.103498"]}}