{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Patel TR"],"funding":["National Institutes of Health"],"pagination":["1321"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8534082"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(10)"],"pubmed_abstract":["A direct aspiration-first pass technique (ADAPT) has recently gained popularity for the treatment of large vessel ischemic stroke. Here, we sought to create a machine learning-based model that uses pre-treatment imaging metrics to predict successful outcomes for ADAPT in middle cerebral artery (MCA) stroke cases. In 119 MCA strokes treated by ADAPT, we calculated four imaging parameters-clot length, perviousness, distance from the internal carotid artery (ICA) and angle of interaction (AOI) between clot/catheter. We determined treatment success by first pass effect (FPE), and performed univariate analyses. We further built and validated multivariate machine learning models in a random train-test split (75%:25%) of our data. To test model stability, we repeated the machine learning procedur"],"journal":["Brain sciences"],"pubmed_title":["Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning."],"pmcid":["PMC8534082"],"funding_grant_id":["R21NS109575"],"pubmed_authors":["Borlongan CV","Levy EI","Snyder KV","Mokin M","Ren Z","Sarayi SMMJ","Dossani R","Patel TR","Davies JM","Waqas M","Siddiqui AH","Tutino VM"],"additional_accession":[]},"is_claimable":false,"name":"Revascularization Outcome Prediction for A Direct Aspiration-First Pass Technique (ADAPT) from Pre-Treatment Imaging and Machine Learning.","description":"A direct aspiration-first pass technique (ADAPT) has recently gained popularity for the treatment of large vessel ischemic stroke. Here, we sought to create a machine learning-based model that uses pre-treatment imaging metrics to predict successful outcomes for ADAPT in middle cerebral artery (MCA) stroke cases. In 119 MCA strokes treated by ADAPT, we calculated four imaging parameters-clot length, perviousness, distance from the internal carotid artery (ICA) and angle of interaction (AOI) between clot/catheter. We determined treatment success by first pass effect (FPE), and performed univariate analyses. We further built and validated multivariate machine learning models in a random train-test split (75%:25%) of our data. To test model stability, we repeated the machine learning procedur","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-19T15:09:19.294Z","creation":"2025-04-19T15:09:19.294Z"},"accession":"S-EPMC8534082","cross_references":{"pubmed":["34679386"],"doi":["10.3390/brainsci11101321"]}}