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Intraaortic blood was segmented using a spherical volume of interest of 1\u00a0cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001 to\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.032). The top 3 features showed high correlation to hemoglobin values (\n                      <jats:italic>p<\/jats:italic>\n                      ) and minimal collinearity (r) to the top ranked feature Median (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), Energy (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.002, r\u2009=\u20090.387), Minimum (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.032, r\u2009=\u20090.437). Median (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and Minimum (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (\n                      <jats:italic>p<\/jats:italic>\n                      (AUC)\u2009=\u20090.015,\n                      <jats:italic>p<\/jats:italic>\n                      (precision)\u2009=\u20090.017,\n                      <jats:italic>p<\/jats:italic>\n                      (accuracy)\u2009=\u20090.612) in the predictive performance employing random forest analysis. A Median HU value\u2009\u2264\u200936.5 indicated moderate-to-severe anemia (accuracy\u2009=\u20090.90, precision\u2009=\u20090.80).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.<\/jats:p>\n                    <jats:p>\n                      <jats:italic>Trial registration<\/jats:italic>\n                      Retrospectively registered.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-021-00654-9","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:03:43Z","timestamp":1628748223000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans"],"prefix":"10.1186","volume":"21","author":[{"given":"Scherwin","family":"Mahmoudi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon S.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Ackermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yauheniya","family":"Zhdanovich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ina","family":"Koch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas J.","family":"Vogl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moritz H.","family":"Albrecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lukas","family":"Lenga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Bernatz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"issue":"2","key":"654_CR1","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. 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All methods were carried out in accordance with relevant guidelines and regulations.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Scherwin Mahmoudi, nothing to declare. Simon S. Martin, nothing to declare. J\u00f6rg Ackermann, nothing to declare. Yauheniya Zhdanovich, nothing to declare. Ina Koch, nothing to declare. Thomas J. Vogl, nothing to declare. Moritz H. Albrecht received speaker fees from Siemens and Bracco, no conflict of interest related to the current study. Lukas Lenga, nothing to declare. Simon Bernatz, nothing to declare.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"123"}}