{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:45:50Z","timestamp":1792795550419,"version":"4.5.0"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"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. Neurol."],"abstract":"<jats:p>Alzheimer\u2019s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.<\/jats:p>","DOI":"10.3389\/fneur.2025.1536463","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T14:52:06Z","timestamp":1738162326000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["From pixels to prognosis: radiomics and AI in Alzheimer\u2019s disease management"],"prefix":"10.3389","volume":"16","author":[{"given":"Danting","family":"Peng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiju","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ren","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1002\/alz.12328","article-title":"2021Alzheimer's disease facts and figures","volume":"17","year":"2021","journal-title":"Alzheimers Dement"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1002\/alz.12638","article-title":"2022 Alzheimer's disease facts and figures","volume":"18","year":"2022","journal-title":"Alzheimers Dement"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1007\/s11682-015-9480-7","article-title":"Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment","volume":"10","author":"Zu","year":"2016","journal-title":"Brain Imaging Behav"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"105242","DOI":"10.1016\/j.cmpb.2019.105242","article-title":"Deep learning to detect Alzheimer's disease from neuroimaging: a systematic literature review","volume":"187","author":"Ebrahimighahnavieh","year":"2020","journal-title":"Comput Methods Prog Biomed"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/JBHI.2019.2914970","article-title":"Studying the manifold structure of Alzheimer's disease: a deep learning approach using convolutional autoencoders","volume":"24","author":"Martinez-Murcia","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"933","DOI":"10.3390\/diagnostics12040933","article-title":"Radiomics analysis of brain [(18)F]FDG PET\/CT to predict Alzheimer's disease in patients with amyloid PET positivity: a preliminary report on the application of SPM cortical segmentation","volume":"12","author":"Alongi","year":"2022","journal-title":"Pyradiomics and Machine-Learning Analysis Diagnostics (Basel)"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s12979-024-00445-0","article-title":"Neuroinflammation in Alzheimer's disease: insights from peripheral immune cells","volume":"21","author":"Zhang","year":"2024","journal-title":"Immun Ageing"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.3233\/JAD-160726","article-title":"Mitochondrial aspects of synaptic dysfunction in Alzheimer's disease","volume":"57","author":"Cai","year":"2017","journal-title":"J Alzheimers Dis"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.3233\/JAD-215139","article-title":"Synaptic mitochondria: an early target of amyloid-\u03b2 and tau in Alzheimer's disease","volume":"84","author":"Torres","year":"2021","journal-title":"J Alzheimers Dis"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/j.neuroimage.2011.05.047","article-title":"Associative memory deficits in mild cognitive impairment: the role of hippocampal formation","volume":"57","author":"Atienza","year":"2011","journal-title":"NeuroImage"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"bio056796","DOI":"10.1242\/bio.056796","article-title":"Molecular mechanisms of neurodegeneration in the entorhinal cortex that underlie its selective vulnerability during the pathogenesis of Alzheimer's disease","volume":"10","author":"Olajide","year":"2021","journal-title":"Biol Open"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"253","DOI":"10.3233\/JAD-2010-1237","article-title":"Conceptual evolution in Alzheimer's disease: implications for understanding the clinical phenotype of progressive neurodegenerative disease","volume":"19","author":"Jicha","year":"2010","journal-title":"J Alzheimers Dis"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1126\/science.abi5208","article-title":"Phenotyping Alzheimer's disease with blood tests","volume":"373","author":"Blennow","year":"2021","journal-title":"Science"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"393","DOI":"10.3233\/JAD-210496","article-title":"Predictive accuracy of blood-derived biomarkers for amyloid-\u03b2 brain deposition along with the Alzheimer's disease continuum: a systematic review","volume":"84","author":"Cianflone","year":"2021","journal-title":"J Alzheimers Dis"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"921","DOI":"10.3233\/JAD-150749","article-title":"Evaluation of a genetic risk score to improve risk prediction for Alzheimer's disease","volume":"53","author":"Chouraki","year":"2016","journal-title":"J Alzheimers Dis"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s13139-017-0512-7","article-title":"Radiomics as a quantitative imaging biomarker: practical considerations and the current standpoint in neuro-oncologic studies","volume":"52","author":"Park","year":"2018","journal-title":"Nucl Med Mol Imaging"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","article-title":"Radiomics: the process and the challenges","volume":"30","author":"Kumar","year":"2012","journal-title":"Magn Reson Imaging"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"2289","DOI":"10.1177\/15353702231211939","article-title":"Radiogenomic landscape: assessment of specific phagocytosis regulators in lower-grade gliomas","volume":"248","author":"Maimaiti","year":"2023","journal-title":"Exp Biol Med (Maywood)"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.jacr.2006.09.020","article-title":"Quality--a radiology imperative: interpretation accuracy and pertinence","volume":"4","author":"Lee","year":"2007","journal-title":"J Am Coll Radiol"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3978\/j.issn.2223-4292.2011.09.01","article-title":"The impact of quantitative imaging in medicine and surgery: charting our course for the future","volume":"1","author":"Wang","year":"2011","journal-title":"Quant Imaging Med Surg"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"120","DOI":"10.2174\/0115672050314171240527064514","article-title":"hdWGCNA and cellular communication identify active NK cell subtypes in Alzheimer's disease and screen for diagnostic markers through machine learning","volume":"21","author":"Song","year":"2024","journal-title":"Curr Alzheimer Res"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"618","DOI":"10.2174\/0115672050280894231214063023","article-title":"Uncovering the impact of Aggrephagy in the development of Alzheimer's disease: insights into diagnostic and therapeutic approaches from machine learning analysis","volume":"20","author":"Xu","year":"2023","journal-title":"Curr Alzheimer Res"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"e17454","DOI":"10.1016\/j.heliyon.2023.e17454","article-title":"Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing","volume":"9","author":"Zhao","year":"2023","journal-title":"Heliyon"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","volume":"79","author":"van der Velden","year":"2022","journal-title":"Med Image Anal"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"6807","DOI":"10.1038\/ncomms7807","article-title":"Direct neuronal glucose uptake heralds activity-dependent increases in cerebral metabolism","volume":"6","author":"Lundgaard","year":"2015","journal-title":"Nat Commun"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"425","DOI":"10.3389\/fnagi.2018.00425","article-title":"(18)F-FDG-PET detects drastic changes in brain metabolism in the Tg4-42 model of Alzheimer's disease","volume":"10","author":"Bouter","year":"2018","journal-title":"Front Aging Neurosci"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3389\/fmed.2019.00071","article-title":"(18)F-FDG-PET in mouse models of Alzheimer's disease","volume":"6","author":"Bouter","year":"2019","journal-title":"Front Med (Lausanne)"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1016\/j.acra.2022.12.033","article-title":"(18)F-FDG-PET Radiomics based on white matter predicts the progression of mild cognitive impairment to Alzheimer disease: a machine learning study","volume":"30","author":"Peng","year":"2023","journal-title":"Acad Radiol"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/s00259-015-3228-x","article-title":"Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis","volume":"43","author":"Morris","year":"2016","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.jalz.2015.05.018","article-title":"[(18)F]flutemetamol amyloid positron emission tomography in preclinical and symptomatic Alzheimer's disease: specific detection of advanced phases of amyloid-\u03b2 pathology","volume":"11","author":"Thal","year":"2015","journal-title":"Alzheimers Dement"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1007\/s12021-022-09587-2","article-title":"Comparison of three automated approaches for classification of amyloid-PET images","volume":"20","author":"Nai","year":"2022","journal-title":"Neuroinformatics"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"8096","DOI":"10.1038\/s41598-023-35389-w","article-title":"Deep learning application for the classification of Alzheimer's disease using (18)F-flortaucipir (AV-1451) tau positron emission tomography","volume":"13","author":"Park","year":"2023","journal-title":"Sci Rep"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.2967\/jnumed.123.267273","article-title":"Advancing tau PET quantification in Alzheimer disease with machine learning: introducing THETA, a novel tau summary measure","volume":"65","author":"Gebre","year":"2024","journal-title":"J Nucl Med"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s00415-018-9016-3","article-title":"Magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment","volume":"266","author":"Chandra","year":"2019","journal-title":"J Neurol"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1093\/brain\/awaa137","article-title":"Development and validation of an interpretable deep learning framework for Alzheimer's disease classification","volume":"143","author":"Qiu","year":"2020","journal-title":"Brain"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1111\/jon.13063","article-title":"Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: a systematic review","volume":"33","author":"Warren","year":"2023","journal-title":"J Neuroimaging"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","article-title":"Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review","volume":"13","author":"Grueso","year":"2021","journal-title":"Alzheimers Res Ther"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"e222441","DOI":"10.1148\/radiol.222441","article-title":"MRI-based deep learning assessment of amyloid, tau, and neurodegeneration biomarker status across the Alzheimer disease Spectrum","volume":"309","author":"Lew","year":"2023","journal-title":"Radiology"},{"key":"ref39","first-page":"309","article-title":"Multimodal learning using convolution neural network and sparse autoencoder","author":"Vu","year":"2017"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.3390\/bioengineering10101120","article-title":"Application of deep learning for prediction of Alzheimer's disease in PET\/MR imaging","volume":"10","author":"Zhao","year":"2023","journal-title":"Bioengineering (Basel)"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1053\/j.semnuclmed.2018.02.011","article-title":"PET\/MRI Hybrid Systems","volume":"48","author":"Mannheim","year":"2018","journal-title":"Semin Nucl Med"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"0354","DOI":"10.34133\/research.0354","article-title":"Innovative multivariable model combining MRI Radiomics and plasma indexes predicts Alzheimer's disease conversion: evidence from a 2-cohort longitudinal study","volume":"7","author":"Yu","year":"2024","journal-title":"Research (Wash D C)"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"e0224365","DOI":"10.1371\/journal.pone.0224365","article-title":"Machine learning algorithm validation with a limited sample size","volume":"14","author":"Vabalas","year":"2019","journal-title":"PLoS One"},{"key":"ref44","first-page":"300","volume-title":"Reducing overfitting and improving generalization in training convolutional neural network (CNN) under limited sample sizes in image recognition. 2020 - 5th international conference on information technology (InCIT)","author":"Thanapol","year":"2020"},{"key":"ref45","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1109\/EMBC46164.2021.9629697","article-title":"Machine learning model validation for early stage studies with small sample sizes","volume":"2021","author":"Larracy","year":"2021","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref46","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1002\/mp.12041","article-title":"Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data","volume":"44","author":"Beichel","year":"2017","journal-title":"Med Phys"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"633176","DOI":"10.3389\/fonc.2021.633176","article-title":"Understanding sources of variation to improve the reproducibility of Radiomics","volume":"11","author":"Zhao","year":"2021","journal-title":"Front Oncol"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1080\/0284186X.2018.1445283","article-title":"Influence of inter-observer delineation variability on radiomics stability in different tumor sites","volume":"57","author":"Pavic","year":"2018","journal-title":"Acta Oncol"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"12688","DOI":"10.1038\/s41598-020-69534-6","article-title":"Radiomics feature reproducibility under inter-rater variability in segmentations of CT images","volume":"10","author":"Haarburger","year":"2020","journal-title":"Sci Rep"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"044505","DOI":"10.1117\/1.JMI.5.4.044505","article-title":"Variation in algorithm implementation across radiomics software","volume":"5","author":"Foy","year":"2018","journal-title":"J Med Imaging (Bellingham)"},{"key":"ref51","doi-asserted-by":"publisher","first-page":"111547","DOI":"10.1016\/j.ejrad.2024.111547","article-title":"Atherosclerotic carotid artery disease Radiomics: a systematic review with meta-analysis and radiomic quality score assessment","volume":"177","author":"Vacca","year":"2024","journal-title":"Eur J Radiol"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1161\/STROKEAHA.122.039732","article-title":"Texture features of magnetic resonance images predict Poststroke cognitive impairment: validation in a multicenter study","volume":"53","author":"Betrouni","year":"2022","journal-title":"Stroke"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1007\/s13042-013-0171-7","article-title":"A comparison of \u21131-regularizion, PCA, KPCA and ICA for dimensionality reduction in logistic regression","volume":"5","author":"Musa","year":"2013","journal-title":"Int J Mach Learn Cybern"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/ICACA.2016.7887916","article-title":"LASSO: A feature selection technique in predictive modeling for machine learning","volume-title":"2016 IEEE International Conference on Advances in Computer Applications (ICACA)","author":"Muthukrishnan","year":"2016"},{"key":"ref55","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1080\/00273171.2015.1036965","article-title":"Using Lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences","volume":"50","author":"McNeish","year":"2015","journal-title":"Multivar Behav Res"},{"key":"ref56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3389\/fninf.2019.00053","article-title":"A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images","volume":"13","author":"Chauhan","year":"2019","journal-title":"Front Neuroinform"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1536463\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T14:52:11Z","timestamp":1738162331000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2025.1536463\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":56,"alternative-id":["10.3389\/fneur.2025.1536463"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2025.1536463","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"article-number":"1536463"}}