{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bie C"],"funding":["China Scholarship Council","NIBIB NIH HHS","National Institutes of Health"],"pagination":["e4626"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8876537"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["35(2)"],"pubmed_abstract":["Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often required to extract the signal of interest. We investigated whether deep learning could help identify pertinent Z-spectral features for distinguishing tumor aggressiveness as well as the possibility of acquiring only the pertinent spectral regions for more efficient CEST acquisition. Human breast cancer cells, MDA-MB-231 and MCF-7, were used to establish bi-lateral tumor xenografts in mice to represent higher and lower aggressive tumors, respectively. A convolutional neural network (CNN)-based classification model, trained on simulated data, u"],"journal":["NMR in biomedicine"],"pubmed_title":["Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging."],"pmcid":["PMC8876537"],"funding_grant_id":["201906970024","EB025295","EB031771","R21 EB025295","P41 EB031771"],"pubmed_authors":["Bie C","Li Y","Bhujwalla ZM","van Zijl PCM","Yadav NN","Zhou Y","Song X","Liu G"],"additional_accession":[]},"is_claimable":false,"name":"Deep learning-based classification of preclinical breast cancer tumor models using chemical exchange saturation transfer magnetic resonance imaging.","description":"Chemical exchange saturation transfer (CEST) magnetic resonance imaging has shown promise for classifying tumors based on their aggressiveness, but CEST contrast is complicated by multiple signal sources and thus prolonged acquisition times are often required to extract the signal of interest. We investigated whether deep learning could help identify pertinent Z-spectral features for distinguishing tumor aggressiveness as well as the possibility of acquiring only the pertinent spectral regions for more efficient CEST acquisition. Human breast cancer cells, MDA-MB-231 and MCF-7, were used to establish bi-lateral tumor xenografts in mice to represent higher and lower aggressive tumors, respectively. A convolutional neural network (CNN)-based classification model, trained on simulated data, u","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Feb","modification":"2025-04-19T17:57:00.451Z","creation":"2025-04-19T17:57:00.451Z"},"accession":"S-EPMC8876537","cross_references":{"pubmed":["34668251"],"doi":["10.1002/nbm.4626"]}}