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Noncontrast MRI is suitable for early screening and long\u2010term monitoring in clinical practice. However, due to the lack of contrast\u2010enhanced details, and given the continuous progression of liver fibrosis and the subtle differences between stages, the use of noncontrast MRI for LFS requires further\u00a0exploration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Here, we develop and evaluate a fine\u2010grained deep learning pipeline using noncontrast MRI for precise and improved\u00a0diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To reflect variations across progression stages, we propose a fine\u2010grained diagnostic model to capture ordinal representation from noncontrast MRI in 450 cases. We employ a multi\u2010scale learning strategy and a learnable attention mechanism to enhance information utilization across noncontrast MRI sequences. Furthermore, we propose a novel hybrid contrastive triplet learning method and a weighted strategy to address the imbalance across progression stages and improve diagnostic\u00a0performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed fine\u2010grained diagnostic model achieved an area under the curve (AUC) of 0.877, outperforming the existing LFS model, deep learning baselines, and general MRI\u2010based diagnostic models. For identifying cirrhosis as the endpoint of fibrosis, the AUC further increased to 0.930. The combined use of UMAP clustering and gradient\u2010based attribution methods revealed meaningful feature patterns and predictive mechanisms, demonstrating the potential of the proposed approach for fine\u2010grained\u00a0diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed fine\u2010grained deep learning model effectively leverages multisequence noncontrast MRI for precise LFS and demonstrates improved diagnostic\u00a0performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70504","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T10:44:16Z","timestamp":1779965056000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning ordinal representation across MRI sequences for liver fibrosis staging"],"prefix":"10.1002","volume":"53","author":[{"given":"Jinhao","family":"Huo","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutao","family":"Wang","sequence":"additional","affiliation":[{"name":"Ningbo No.9 Hospital Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruixue","family":"Zhang","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Ningbo University Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Universal Medical Imaging Diagnostic Center Shanghai University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jin","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2023.03.017"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0168-8278(02)00429-4"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25147873"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2015.02.039"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12102229"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.21037\/tgh.2020.04.01"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.35841\/biomedicalresearch.30-19-107"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s43162-024-00283-y"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.10.004"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5499-7"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-023-01704-8"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07475-4"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-012-0179-7"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-023-00255-6"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24536"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics9030107"},{"issue":"18","key":"e_1_2_9_18_1","first-page":"6609","article-title":"Diffusion\u2010weighted imaging of the liver in assessing chronic liver disease: effects of fat and iron deposition on ADC values","volume":"26","author":"Kahraman A","year":"2022","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-016-0913-6"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2023.12.006"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"NishimuraY ImamuraK KobayashiS.MRI Hepatic Fibrosis Stage Diagnosis Using Exchange Learning. 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