{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T14:15:27Z","timestamp":1789654527784,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop an interpretable radiomics model based on habitat for predicting lymph node metastasis in oral cancer using Synthetic MRI (SyMRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective study of 101 oral cancer patients with pathologically confirmed lymph node status was performed, dividing them into a training cohort (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200971) and a test cohort (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200930). All patients underwent MRI, including the MAGiC (SyMRI) sequence. Intra\u2010tumoral volumes of interest (VOIs) were clustered into three spatial habitats using the K\u2010means algorithm. Radiomics models were developed for the entire tumor and clustered intra\u2010tumor regions, based on features from synthetic quantitative T1 and T2 maps. The Shapley Additive Explanations (SHAP) method was used to interpret predictions. A radiomics nomogram was constructed by integrating independent variables, including the radiomics score and MRI\u2010reported status. Predictive performance was evaluated using AUC, DeLong's test, calibration curves, and decision curve analysis (DCA).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The habitat with the highest T1 and a moderately low T2 values within the tumor showed the best predictive performance, compared to whole\u2010tumor radiomics (training: 0.87 vs. 0.82; test: 0.74 vs. 0.62). The radiomics nomogram, combining radiomics features and independent clinical variables, outperformed clinical diagnosis alone in both the training cohort (0.92 vs. 0.80;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0023) and test cohort (0.84 vs. 0.73;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0362).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The habitat\u2010based radiomics signature, integrating SyMRI\u2010derived quantitative T1 and T2 maps, provides enhanced predictive accuracy for preoperative lymph node metastasis in oral cancer compared to clinical diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71798","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:43:02Z","timestamp":1776404582000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Interpretable Machine Learning Model With Synthetic\n                    <scp>MRI<\/scp>\n                    \u2010Based Habitat Radiomics for Predicting Lymph Node Metastasis in Oral Cancer"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-8611","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Radiology Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiliang","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"MR Research, GE Healthcare  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Shi","sequence":"additional","affiliation":[{"name":"MR Research, GE Healthcare  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8472-0456","authenticated-orcid":false,"given":"Xiaofeng","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_2_14_2_1","unstructured":"International Agency for Research on Cancer.\u201cGlobal Cancer Observatory. 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