{"doi":"10.1111/jon.13109","title":"Imaging biomarkers of cerebral edema automatically extracted from routine CT scans of large vessel occlusion strokes","abstract":"BACKGROUND AND PURPOSE: Volumetric and densitometric biomarkers have been proposed to better quantify cerebral edema after stroke, but their relative performance has not been rigorously evaluated. METHODS: Patients with large vessel occlusion stroke from three institutions were analyzed. An automated pipeline extracted brain, cerebrospinal fluid (CSF), and infarct volumes from serial CTs. Several biomarkers were measured: change in global CSF volume from baseline (ΔCSF); ratio of CSF volumes between hemispheres (CSF ratio); and relative density of infarct region compared with mirrored contralateral region (net water uptake [NWU]). These were compared to radiographic standards, midline shift and relative hemispheric volume (RHV) and malignant edema, defined as deterioration resulting in need for osmotic therapy, decompressive surgery, or death. RESULTS: We analyzed 255 patients with 210 baseline CTs, 255 24-hour CTs, and 81 72-hour CTs. Of these, 35 (14%) developed malignant edema and 63 (27%) midline shift. CSF metrics could be calculated for 310 (92%), while NWU could only be obtained from 193 (57%). Peak midline shift was correlated with baseline CSF ratio (ρ = -.22) and with CSF ratio and ΔCSF at 24 hours (ρ = -.55/.63) and 72 hours (ρ = -.66/.69), but not with NWU (ρ = .15/.25). Similarly, CSF ratio was correlated with RHV (ρ = -.69/-.78), while NWU was not. Adjusting for age, National Institutes of Health Stroke Scale, tissue plasminogen activator treatment, and Alberta Stroke Program Early CT Score, CSF ratio (odds ratio [OR]: 1.95 per 0.1, 95% confidence interval [CI]: 1.52-2.59) and ΔCSF at 24 hours (OR: 1.87 per 10%, 95% CI: 1.47-2.49) were associated with malignant edema. CONCLUSION: CSF volumetric biomarkers can be automatically measured from almost all routine CTs and correlate better with standard edema endpoints than net water uptake.","journal":"Journal of Neuroimaging","year":2023,"id":347717,"datarank":0.39683861533767095,"base_score":2.4849066497880004,"endowment":2.4849066497880004,"self_citation_contribution":0.37273599746820013,"citation_network_contribution":0.024102617869470825,"self_endowment_contribution":0.37273599746820013,"citer_contribution":0.024102617869470825,"corpus_percentile":53.972306026146825,"corpus_rank":5951,"citation_count":11,"citer_count":6,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5632,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":879706,"name":"Atul Kumar","orcid":"0000-0002-0166-0911","position":1,"is_corresponding":false},{"id":399192,"name":"Yasheng Chen","orcid":"0000-0002-1133-452X","position":2,"is_corresponding":false},{"id":1060784,"name":"Yelyzaveta Begunova","orcid":"0000-0003-0222-2347","position":3,"is_corresponding":false},{"id":905293,"name":"Madelynne Olexa","orcid":null,"position":4,"is_corresponding":false},{"id":921724,"name":"Ayush Prasad","orcid":"0009-0001-9834-1297","position":5,"is_corresponding":false},{"id":908620,"name":"Grace Carey","orcid":null,"position":6,"is_corresponding":false},{"id":1089589,"name":"Isabella Gonzalez","orcid":"0000-0003-3050-8078","position":7,"is_corresponding":false},{"id":908114,"name":"Kunal Bhatia","orcid":"0000-0003-4562-7758","position":8,"is_corresponding":false},{"id":1090047,"name":"Mohammad Hamed","orcid":null,"position":9,"is_corresponding":false},{"id":531618,"name":"Laura Heitsch","orcid":"0000-0002-8712-4123","position":10,"is_corresponding":false},{"id":470199,"name":"Shraddha Mainali","orcid":"0000-0002-9495-3843","position":11,"is_corresponding":false},{"id":464922,"name":"Nils Petersen","orcid":"0000-0001-9711-3340","position":12,"is_corresponding":false},{"id":310715,"name":"Jin‐Moo Lee","orcid":"0000-0002-3979-0906","position":13,"is_corresponding":false},{"id":337719,"name":"Rajat Dhar","orcid":"0000-0002-5167-5097","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T01:12:01.859977Z","pmid":"37095592","pmcid":"PMC10524672","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}