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Lesion volume segmentation was manually performed by two radiologists, resulting in 426 lesions from the training set and 83 lesions from the test set. The radiomics model was constructed using a support vector machine (SVM) with pre-defined features, which was first selected using Chi-square test, followed by refining using binary least absolute shrinkage and selection operator (LASSO) regression. The deep learning model was established based on the DenseNet. Performance of the models was quantified by area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1-score.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A set of 8 most informative features was selected from 1049 features to train the SVM classifier. The AUCs of the radiomics model were 0.857 (95% confidence interval [CI] 0.816\u20130.888) for the training set and 0.879 (95% CI 0.779\u20130.935) for the test set. The deep learning method achieved AUCs of 0.838 (95% CI 0.799\u20130.871) for the training set and 0.717 (95% CI 0.601\u20130.814) for the test set. The performance difference between these two models was assessed by <jats:italic>t<\/jats:italic>-test, which showed the results in both training and test sets were statistically significant.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The deep learning based model can be trained end-to-end with little extra domain knowledge, while the radiomics model requires complex feature selection. However, this process makes the radiomics model achieve better performance in this study with smaller computational cost and more potential on model interpretability.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00946-8","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T14:03:24Z","timestamp":1671026604000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A comparative study between deep learning and radiomics models in grading liver tumors using hepatobiliary phase contrast-enhanced MR images"],"prefix":"10.1186","volume":"22","author":[{"given":"Lixin","family":"Du","sequence":"first","affiliation":[]},{"given":"Jianpeng","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Gan","sequence":"additional","affiliation":[]},{"given":"Zhigang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Pan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zujun","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"issue":"3","key":"946_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, Laversanne M, Soerjomataram I, Jemal A, Bray F. 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The need to obtain the informed consent was waived by the Ethical Committee because of de-identifcation data involving no potential risk to patients and no link between the patients and researchers.","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 they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"218"}}