{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T06:15:59Z","timestamp":1788156959359,"version":"build-2803163510"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD), the most common neurodegenerative disorder, is a leading cause of cognitive impairment and dementia in older adults. This study aimed to develop an interpretable machine learning model using multimodal MRI radiomics for the diagnosis of Alzheimer's disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      A total of 110 subjects (48 AD, 62 healthy control subjects) underwent 3D T1WI, DWI, and T2WI scans. Radiomics features were extracted from eight AD-related brain regions and selected using a three-step approach: variance thresholding, independent\n                      <jats:italic>t<\/jats:italic>\n                      -test, and LASSO\u2013all conducted strictly within the training cohort. Logistic regression (LR) and random forest (RF) models were constructed for single-sequence and combined-sequence data. Model performance was evaluated using ROC analysis, calibration curves, and decision curve analysis. SHapley Additive exPlanations (SHAP) was applied for interpretability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sixteen core radiomics features were retained. Combined-sequence models outperformed single-sequence models, achieving test AUCs of 0.989 and 0.970 for LR and RF, respectively. The LR combined-sequence model achieved an accuracy of 0.882, sensitivity of 0.800, and specificity of 0.947. SHAP analysis identified texture features from the parietal lobe as key contributors. A nomogram integrating radiomics and clinical factors (homocysteine, triglycerides) demonstrated excellent calibration and clinical net benefit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multimodal MRI radiomics combined with interpretable machine learning provides an accurate and explainable tool for AD diagnosis, with the combined LR model exhibiting superior performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1876233","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:41:44Z","timestamp":1788154904000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable machine learning model based on multimodal MRI radiomics for Alzheimer's disease diagnosis"],"prefix":"10.3389","volume":"18","author":[{"given":"Nuerbiya","family":"Keranmu","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Xinjiang Medical University","place":["\u00dcr\u00fcmqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dilireba","family":"Aizezi","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Xinjiang Medical University","place":["\u00dcr\u00fcmqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyong","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Xinjiang Medical University","place":["\u00dcr\u00fcmqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longtao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha","place":["Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Xinjiang Medical University","place":["\u00dcr\u00fcmqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha","place":["Hunan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.3233\/JAD-220498","article-title":"The parietal lobe in Alzheimer's disease and blindness","volume":"89","author":"Ahull\u00f3-Fuster","year":"2022","journal-title":"J. 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