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The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Methods<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>This was a retrospective study of eligible patients with cirrhosis (<jats:inline-formula><jats:alternatives><jats:tex-math>$$n=90$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>n<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn>90<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient\u2019s condition at time of scan: MELD score, MELD score <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>\u2265<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 9 (median score of the cohort), MELD score <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ge $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mo>\u2265<\/mml:mo>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Results<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>\n                           <jats:bold>Conclusions<\/jats:bold>\n                        <\/jats:title>\n                <jats:p>We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-020-02295-9","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T06:02:52Z","timestamp":1614578572000},"page":"457-466","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["MRI-based radiomic feature analysis of end-stage liver disease for severity stratification"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5219-2532","authenticated-orcid":false,"given":"Jennifer","family":"Nitsch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jordan","family":"Sack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael W.","family":"Halle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan H.","family":"Moltz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"April","family":"Wall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna E.","family":"Rutherford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ron","family":"Kikinis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hans","family":"Meine","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"2295_CR1","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12916-014-0145-y","volume":"12","author":"AA Mokdad","year":"2014","unstructured":"Mokdad AA, Lopez AD, Shahraz S, Lozano R, Mokdad AH, Stanaway J, Murray CJL, Naghavi M (2014) Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. 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