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Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n\u2009=\u2009133, MR: n\u2009=\u200978) and a test dataset (CT: n\u2009=\u200957, MR: n\u2009=\u200934). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC\u2009=\u20090.917 and ACC\u2009=\u20090.882 when the threshold was set as ICG-R15\u2009=\u200910%. When ICG-R15\u2009=\u200920%, classifier Random Forest performed best with AUC\u2009=\u20090.979 and ACC\u2009=\u20090.882. When ICG-R15\u2009=\u200930%, classifier XGBoost performed best with AUC\u2009=\u20090.961 and ACC\u2009=\u20090.941. For CT groups, the classifier XGBoost performed best when ICG-R15\u2009=\u200910% with AUC\u2009=\u20090.822 and ACC\u2009=\u20090.842. When ICG-R15\u2009=\u200920%, classifier SVM performed best with AUC\u2009=\u20090.860 and ACC\u2009=\u20090.842. When ICG-R15\u2009=\u200930%, classifier XGBoost performed best with AUC\u2009=\u20090.938 and ACC\u2009=\u20090.965.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-023-01050-1","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T14:02:24Z","timestamp":1689602544000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image"],"prefix":"10.1186","volume":"23","author":[{"given":"Ling","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengzhan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"issue":"3","key":"1050_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, et al. 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The approval number of the Ethics Review was QYFY-WZLL-27465. The need for informed consent was waived by the Ethics Committee of the Affiliated Hospital of Qingdao University due to its retrospective nature. The methods of this study were carried out in the case of meeting relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that their study was performed without any financial or commercial relation that may be interpreted as a potential conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"94"}}