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However, accuracy of visual evaluation is impaired by inter- and intra-reader variability, artifacts in the posterior fossa and limited sensitivity for subtle density shifts. We propose a machine learning approach for detecting early ischemic changes in pc-ASPECTS regions (Posterior circulation Alberta Stroke Program Early CT Score) based on admission NCCTs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The retrospective study includes 552 pc-ASPECTS regions (144 with infarctions in follow-up NCCTs) extracted from pre-therapeutic early hyperacute scans of 69 patients with basilar artery occlusion that later underwent successful recanalization. We evaluated 1218 quantitative image features utilizing random forest algorithms with fivefold cross-validation for the ability to detect early ischemic changes in hyperacute images that lead to definitive infarctions in follow-up imaging. Classifier performance was compared to conventional readings of two neuroradiologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Receiver operating characteristic area under the curves for detection of early ischemic changes were 0.70 (95% CI [0.64; 0.75]) for cerebellum to 0.82 (95% CI [0.77; 0.86]) for thalamus. Predictive performance of the classifier was significantly higher compared to visual reading for thalamus, midbrain, and pons (<jats:italic>P<\/jats:italic>value\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Quantitative features of early hyperacute NCCTs can be used to detect early ischemic changes in pc-ASPECTS regions. The classifier performance was higher or equal to results of human raters. The proposed approach could facilitate reproducible analysis in research and may allow standardized assessments for outcome prediction and therapy planning in clinical routine.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s00415-020-09859-4","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T10:03:42Z","timestamp":1589191422000},"page":"2632-2641","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans"],"prefix":"10.1007","volume":"267","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5258-2370","authenticated-orcid":false,"given":"Helge C.","family":"Kniep","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter B.","family":"Sporns","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Broocks","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andr\u00e9","family":"Kemmling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawed","family":"Nawabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thilo","family":"Rusche","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Fiehler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uta","family":"Hanning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"key":"9859_CR1","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1001\/jamaoncol.2016.2631","volume":"2","author":"HJ Aerts","year":"2016","unstructured":"Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. 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Fiehler: research support: German Ministry of Science and Education (BMBF), German Ministry of Economy and Innovation (BMWi), German Research Foundation (DFG), European Union (EU), Hamburgische Investitions- und F\u00f6rderbank (IFB), Medtronic, Microvention, Philips, Stryker. Consultant for: Acandis, Boehringer Ingelheim, Cerenovus, Covidien, Medtronic, Microvention, Penumbra, Stryker. All other authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This single center retrospective study was approved by the Ethics Committee of the University of Muenster, and the requirement for informed consent was waived (2017-233-f-S). All study protocols and procedures were conducted in accordance with the Declaration of Helsinki. The data used for training and validation of algorithms in this study are available from the corresponding author upon reasonable request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}