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The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1\/FVC) and radiologists\u2019 visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson\u2019s correlation coefficients and receiver operating characteristics (ROC) analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>SWES correlated more strongly with the visual scores than LAV950 (<jats:italic>r<\/jats:italic>\u2009=\u20090.78 vs. <jats:italic>r<\/jats:italic>\u2009=\u20090.41, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, <jats:italic>p<\/jats:italic>\u2009=\u20090.007). SWES correlated more strongly with FEV1\/FVC than either LAV950 or sum-visual in the full cohort (<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.69 vs. <jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.49\/<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.64, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001\/<jats:italic>p<\/jats:italic>\u2009=\u20090.007), in the emphysema cohort (<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.77 vs. <jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.69\/<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.65, <jats:italic>p<\/jats:italic>\u2009=\u20090.03\/<jats:italic>p<\/jats:italic>\u2009=\u20090.002), and in the random sample (<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.39 vs. <jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.26\/<jats:italic>r<\/jats:italic>\u2009=\u2009\u2009\u2212\u20090.25, <jats:italic>p<\/jats:italic>\u2009=\u20090.001\/<jats:italic>p<\/jats:italic>\u2009=\u20090.007).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists\u2019 visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists\u2019 visual scores.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical relevance statement<\/jats:title>\n                <jats:p>The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950).<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-023-09985-3","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T10:02:20Z","timestamp":1691488940000},"page":"39-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1346-1450","authenticated-orcid":false,"given":"Mats","family":"Lid\u00e9n","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Spahr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Hjelmgren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Bendazzoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josefin","family":"Sundh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magnus","family":"Sk\u00f6ld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00f6ran","family":"Bergstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunliang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Per","family":"Thunberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"9985_CR1","unstructured":"The top 10 causes of death. 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Not related to this project, MS has received consultancies from Roche, Boehringer, Ingelheim, Novartis, Pfizer, AstraZeneca, GlaxoSmithKline, and Chiesi.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors (ML) has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Concerning machine learning CT emphysema assessment, the validation cohort has not been previously reported. Related to the present work, the subjects in the development cohort have previously been reported by Vikgren et al [] and Lid\u00e9n et al [].","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Prospective inclusion\u2022 Cross-sectional\u2022 Single inclusion site","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}