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Early prediction of ARDS is crucial for timely supportive therapy to reduce morbidity and mortality. The objective of this study was to develop and test a machine learning\u2013based method for the early prediction of ARDS derived from the first computed tomography scan of polytraumatized patients after admission to the hospital.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>One hundred twenty-three patients (86 male and 37 female, age 41.2 \u00b1 16.4) with an injury severity score (ISS) of 16 or higher (31.9 \u00b1 10.9) were prospectively included and received a CT scan within 1\u00a0h after the accident. The lungs, including air pockets and pleural effusions, were automatically segmented using a deep learning\u2013based algorithm. Subsequently, we extracted radiomics features from within the lung and trained an ensemble of gradient boosted trees (GBT) to predict future ARDS.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Cross-validated ARDS prediction resulted in an area under the curve (AUC) of 0.79 for the radiomics score compared to 0.66 for ISS, and 0.68 for the abbreviated injury score of the thorax (AIS-thorax). Prediction using the radiomics score yielded an f1-score of 0.70 compared to 0.53 for ISS and 0.57 for AIS-thorax. The radiomics score achieved a sensitivity and specificity of 0.80 and 0.76.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This study proposes a radiomics-based algorithm for the prediction of ARDS in polytraumatized patients at the time of admission to hospital with an accuracy that competes and surpasses conventional scores despite the heterogeneous, and therefore more realistic, scanning protocols.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:italic>\u2022 Early prediction of acute respiratory distress syndrome in polytraumatized patients is possible, even when using heterogenous data.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Radiomics-based prediction resulted in an area under the curve of 0.79 compared to 0.66 for the injury severity score, and 0.68 for the abbreviated injury score of the thorax.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Highlighting the most relevant lung regions for prediction facilitates the understanding of machine learning\u2013based prediction.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-020-07635-6","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T18:02:53Z","timestamp":1616004173000},"page":"5443-5453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Radiomics score predicts acute respiratory distress syndrome based on the initial CT scan after trauma"],"prefix":"10.1007","volume":"31","author":[{"given":"Sebastian","family":"R\u00f6hrich","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Hofmanninger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Negrin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-6873","authenticated-orcid":false,"given":"Georg","family":"Langs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helmut","family":"Prosch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"7635_CR1","first-page":"179","volume":"13","author":"M Elmali","year":"2007","unstructured":"Elmali M, Baydin A, Nural MS et al (2007) Lung parenchymal injury and its frequency in blunt thoracic trauma: the diagnostic value of chest radiography and thoracic CT. 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The previous study reported on the usefulness of a follow-up chest CT scan for manual volumetry of lung parenchymal injuries compared to the initial scan at admission whereas the present study evaluated the possibility of deep learning\u2013based prediction of ARDS in the initial scan.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 prospective\u2022 diagnostic or prognostic study\u2022 performed at one institution","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}