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Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Estimating progression of acute ischemic brain lesions \u2013 or biological lesion age - holds huge practical importance for hyperacute stroke management. The current best method for determining lesion age from non-contrast computerised tomography (NCCT), measures Relative Intensity (RI), termed Net Water Uptake (NWU). We optimised lesion age estimation from NCCT using a convolutional neural network \u2013 radiomics (CNN-R) model trained upon chronometric lesion age (Onset Time to Scan: OTS), while validating against chronometric and biological lesion age in external datasets (<jats:italic>N<\/jats:italic>\u2009=\u20091945). Coefficients of determination (R<jats:sup>2<\/jats:sup>) for OTS prediction, using CNN-R, and RI models were 0.58 and 0.32 respectively; while CNN-R estimated OTS showed stronger associations with ischemic core:penumbra ratio, than RI and chronometric, OTS (\u03c1<jats:sup>2<\/jats:sup>\u2009=\u20090.37, 0.19, 0.11); and with early lesion expansion (regression coefficients &gt;2x for CNN-R versus others) (all comparisons: <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Concluding, deep-learning analytics of NCCT lesions is approximately twice as accurate as NWU for estimating chronometric and biological lesion ages.<\/jats:p>","DOI":"10.1038\/s41746-024-01325-z","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T10:01:55Z","timestamp":1733479315000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT"],"prefix":"10.1038","volume":"7","author":[{"given":"Adam","family":"Marcus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Mair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-8164","authenticated-orcid":false,"given":"Charles","family":"Hallett","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia Ghezzou","family":"Cuervas-Mons","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dylan","family":"Roi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-7010","authenticated-orcid":false,"given":"Paul","family":"Bentley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"1325_CR1","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1161\/STROKEAHA.118.021484","volume":"49","author":"A Vagal","year":"2018","unstructured":"Vagal, A. et al. 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