{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:30:36Z","timestamp":1786595436619,"version":"build-2736575974"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Diabetic peripheral neuropathy (DPN) is a prevalent and disabling complication of type 2 diabetes mellitus (T2DM), yet detection remains constrained by limited accessibility of nerve conduction studies. This study developed and validated a dual-modality ultrasound radiomics model for individualized DPN classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In total, 253 T2DM patients from three centers were prospectively enrolled between June 2025 and February 2026, allocated to training (n = 122), internal test (n = 53), and external validation (n = 78) cohorts. Radiomics features were extracted from longitudinal B-mode ultrasound and shear wave elastography images of the tibial nerve. After reproducibility filtering, batch-effect correction, and elastic-net feature selection, four machine learning algorithms were compared and the best-performing was used to construct modality-specific radiomics scores (Rad-scores). A combined model integrating Rad-scores with independently associated clinical factors was developed. Discrimination, calibration, clinical usefulness, and SHapley Additive exPlanations (SHAP) interpretability were evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Multivariable analysis identified diabetes duration and minimum elastic modulus as independently associated factors, with both Rad-scores significantly associated with DPN status. The combined model demonstrated good discrimination across all cohorts (AUC 0.892 [95% CI 0.828\u20130.950], 0.824 [0.696\u20130.925], and 0.838 [0.735\u20130.926]), outperforming all other models (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.05), with adequate calibration in both training and internal test cohorts (\n                      <jats:italic>p<\/jats:italic>\n                      &amp;gt; 0.05). Decision curve analysis confirmed clinical benefit. SHAP analysis identified diabetes duration as the most influential variable, followed by the shear wave elastography Rad-score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The combined model demonstrates favorable diagnostic performance for individualized DPN risk stratification and holds promise as a noninvasive complement to nerve conduction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1900881","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:23:44Z","timestamp":1786595024000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-modality ultrasound radiomics model for classifying diabetic peripheral neuropathy in type 2 diabetes: a multicenter prospective study"],"prefix":"10.3389","volume":"17","author":[{"given":"Rong","family":"Xiao","sequence":"first","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanyan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Linquan County People\u2019s Hospital","place":["Fuyang, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenqian","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The Hefei BOE Hospital","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enfa","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, South Zone Branch","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danqing","family":"He","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, 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