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This study aims to explore the value of multimodal positron emission tomography (PET) and magnetic resonance imaging (MRI) radiomics to improve diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate hippocampus\u2010based multimodal PET\/MRI radiomics models for AD classification and exploratory five\u2010class classification, and to compare early fusion (EF) with multi\u2010criteria decision\u2010making (MCDM)\u2010based decision\u2010level fusion.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>A total of 758 patients who underwent both PET ([18F]FDG and Amyloid\u2010\u03b2) and MRI (3DT1, T1WI, T2WI, T2\u2010FLAIR) were retrospectively included as the primary dataset in this study. 945 radiomic features (RFs) per sequence were extracted from the bilateral hippocampus. Models were evaluated on the primary cohort using a five\u2010fold stratified cross\u2010validation at the subject level (80% training and 20% testing in each fold). Feature selection was performed\u00a0independently\u00a0for each sequence using the Least Absolute Shrinkage and Selection Operator (LASSO). For multimodal PET\/MR models, features from all six sequences were concatenated, and feature selection was applied (EF). Six classifiers were used to construct classification models for two tasks, including (1) AD\/non\u2010AD classification, and (2) exploratory five\u2010class classification among AD, FTD, VaD, MCI, and other dementia. In addition, an MCDM\u2010based strategy was implemented to fuse multimodal RFs and multi\u2010classifiers. Modality\u2010matched external validation was performed in an independent cohort of 45 patients with 3DT1 and A\u03b2 PET available. Decision curve analysis (DCA) was employed to assess the clinical usefulness of the models. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the EF\u2010based model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among the 758 patients in the primary dataset, AD, FTD, VaD, and MCI were present in 481 (63.5%), 34 (4.5%), 15 (2.0%), 136 (17.9%) cases, with 92 (12.1%) classified as the other dementia subtypes. The MCDM\u2010based models achieved the highest area under the receiver operating characteristic curves (AUCs) for both tasks on the primary dataset (Task 1: AUC\u00a0=\u00a00.848; Task 2: AUC\u00a0=\u00a00.797), significantly higher than EF\u2010based models in Task 2 (AUC\u00a0=\u00a00.782,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) and showing comparable performance in Task 1 (AUC\u00a0=\u00a00.839,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.629). On the external dataset, EF and MCDM models achieved AUCs of 0.741 and 0.761, respectively, for Task 1. The MCDM model showed a higher AUC than EF. DCA suggested potential net\u2010benefit advantages for the multimodal models across selected threshold ranges. SHAP analysis identified wavelet\u2010transformed texture features as the key predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Hippocampus\u2010based PET\/MRI radiomics models showed promising performance for binary AD classification, with further improvement achieved by the MCDM strategy. The five\u2010class findings should be regarded as exploratory. External validation provides preliminary support for the reduced 3DT1\u2013A\u03b2 PET model and does not establish the generalizability of the complete six\u2010modality framework.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70690","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T04:52:16Z","timestamp":1791262336000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal PET\/MRI radiomics model for the classification of Alzheimer's disease and other dementia subtypes: A multicenter study"],"prefix":"10.1002","volume":"53","author":[{"given":"Bingjie","family":"Wang","sequence":"first","affiliation":[{"name":"Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau  Taipa Macau SAR China"},{"name":"Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences  Shenzhen China"},{"name":"Key Laboratory of Biomedical Imaging Science and System State Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Bi","sequence":"additional","affiliation":[{"name":"Department of Radiology &amp; Nuclear Medicine Xuanwu Hospital, Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyin","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology &amp; Nuclear Medicine Xuanwu Hospital, Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hairong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences  Shenzhen China"},{"name":"Key Laboratory of Biomedical Imaging Science and System State Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[{"name":"Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of Sciences  Shenzhen China"},{"name":"Key Laboratory of Biomedical Imaging Science and System State Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greta S. 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