{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wei CJ"],"funding":["Science and Technology Commission of Shanghai Municipality (Shanghai Municipal Science and Technology Commission)","National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["56"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11763078"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(1)"],"pubmed_abstract":["Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neu"],"journal":["NPJ digital medicine"],"pubmed_title":["A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification."],"pmcid":["PMC11763078"],"funding_grant_id":["82172228","22MC1940300","82102344; 82172228; 82472579;"],"pubmed_authors":["Liu H","Liu J","Huang JN","Wang ZC","Li QF","Yang JL","Sun YB","Wei CJ","Yang TL","Wang MH","Yan C","Tang Y","Yao ZW"],"additional_accession":[]},"is_claimable":false,"name":"A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.","description":"Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neu","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jan","modification":"2025-04-21T23:55:38.438Z","creation":"2025-04-05T19:20:39.657Z"},"accession":"S-EPMC11763078","cross_references":{"pubmed":["39863790"],"doi":["10.1038/s41746-025-01454-z"]}}