{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T17:58:50Z","timestamp":1780595930288,"version":"3.54.1"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"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. Med."],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>We proposed a novel deep learning radiomics (DLR) method to distinguish cognitively normal adults at risk of Alzheimer\u2019s disease (AD) from normal control based on T1-weighted structural MRI images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we selected MRI data from the Alzheimer\u2019s Disease Neuroimaging Initiative Database (ADNI), which included 417 cognitively normal adults. These subjects were divided into 181 individuals at risk of Alzheimer\u2019s disease (preAD group) and 236 normal control individuals (NC group) according to standard uptake ratio &amp;gt;1.18 calculated by amyloid Positron Emission Tomography (PET). We further divided the preaAD group into APOE+ and APOE<jats:bold>\u2212<\/jats:bold> subgroups according to whether APOE \u03b54 was positive or not. All data sets were divided into one training\/validation group and one independent test group. The proposed DLR method included three steps: (1) the pre-training of basic deep learning (DL) models, (2) the extraction, selection and fusion of DLR features, and (3) classification. The support vector machine (SVM) was used as the classifier. In the comparative experiments, we compared our proposed DLR method with three existing models: hippocampal model, clinical model, and traditional radiomics model. Ten-fold cross-validation was performed with 100 time repetitions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The DLR method achieved the best classification performance between preAD and NC than other models with an accuracy of 89.85% \u00b1 1.12%. In comparison, the accuracies of the other three models were 72.44% \u00b1 1.37%, 82.00% \u00b1 4.09% and 79.65% \u00b1 2.21%. In addition, the DLR model also showed the best classification performance (85.45% \u00b1 9.04% and 92.80% \u00b1 2.61%) in the subgroup experiment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results showed that the DLR method provided a potentially clinical value to distinguish preAD from NC.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2022.894726","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T14:02:19Z","timestamp":1650549739000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer\u2019s Disease From Normal Control: An Exploratory Study Based on Structural 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