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Radiomics features were extracted from whole lesions and from intralesional subregions generated by clustering analysis. Radiologists\u2019 assessments were performed according to the established MRI grading system. Feature selection was conducted using intraclass correlation coefficient analysis, Pearson correlation filtering, and least absolute shrinkage and selection operator regression. Habitat radiomics, conventional radiomics, and clinicoradiological models were developed using multiple machine learning classifiers. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Radiologist performance was assessed with and without model assistance. Model interpretability was explored using SHapley Additive exPlanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The habitat radiomics model achieved a test-cohort AUC of 0.838, which was numerically higher than those of the conventional radiomics and clinicoradiological models, although the differences were not statistically significant. Radiologist AUCs ranged from 0.632 to 0.828 without model assistance and from 0.828 to 0.928 with habitat radiomics assistance, with significant improvements mainly observed among junior radiologists (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MRI-based habitat radiomics showed feasible performance for differentiating indeterminate Grade 3 nasopharyngeal lesions, although its standalone performance was not statistically superior to comparator models. It may provide complementary assistance for junior radiologists in challenging cases.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12885-026-16524-y","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:35:12Z","timestamp":1784075712000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MR habitat radiomics for differentiating indeterminate (Grade 3) nasopharyngeal lesions: lymphoid hyperplasia versus early-stage nasopharyngeal carcinoma"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2472-3102","authenticated-orcid":false,"given":"Zhenhuan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5848-7195","authenticated-orcid":false,"given":"Xuezhao","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7436-0440","authenticated-orcid":false,"given":"Qikui","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6271-3793","authenticated-orcid":false,"given":"Dandan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8897-3406","authenticated-orcid":false,"given":"Tingyu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3669-568X","authenticated-orcid":false,"given":"Yueming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"16524_CR1","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s11547-017-0785-z","volume":"122","author":"ML Wang","year":"2017","unstructured":"Wang ML, Wei XE, Yu MM, Li WB. 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