Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression

Topic: Amyloid, tau and blood-test diagnosis

A single-timepoint ResNet3D model achieved modest performance (c-index ~ 0.70). Incorporating longitudinal MRI files or downstream survival models led to a pronounced prognostic improvement (c-index:0.80-0.90), but was not further improved by longitudinal radiomics data. Time-specific classification within two- and three-year and five-year windows after the last MRI acquisition showed high accuracy (AUC > 0.85). Several radiomics, including gray matter surface to volume and elongation, emerged as the most predictive features. Each SD change in the gray matter surface to volume change within the last visit was associated with an increased risk of developing AD (HR: 1.50; 95% CI: 1.25-1.79). These findings emphasize the value of structural MRI within the advanced DL architectures for predicting MCI-to-AD conversion. The approach may enable earlier risk stratification and targeted interventions for individuals most likely to progress. limitations in sample size and computational resources warrant larger, more diverse studies to confirm these observations and explore additional improvements.

Key findings

• A single-timepoint ResNet3D model achieved modest performance (c-index ~ 0.70).
• Incorporating longitudinal MRI files or downstream survival models led to a pronounced prognostic improvement (c-index:0.80-0.90), but was not further improved by longitudinal radiomics data.
• Time-specific classification within two- and three-year and five-year windows after the last MRI acquisition showed high accuracy (AUC > 0.85).
• Several radiomics, including gray matter surface to volume and elongation, emerged as the most predictive features.
• Each SD change in the gray matter surface to volume change within the last visit was associated with an increased risk of developing AD (HR: 1.50; 95% CI: 1.25-1.79).

Why it matters

Deep learning models using longitudinal structural MRI predicted conversion from mild cognitive impairment to Alzheimer's disease with c-index 0.80-0.90, better than a single-timepoint model. The authors note limitations in sample size and computational resources warrant larger, more diverse studies to confirm the findings.

Study details

• Study designlongitudinal study
• Effect sizeHR: 1.50
• Evidence strengthModerate

Read the original

• DOI: 10.1186/s13195-025-01827-2
• PubMed: 40775357
• Original source →
• Free full text: Alzheimer s Research & Therapy →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{aghajanian2025-40775357,
  author  = {Aghajanian, Sepehr and Mohammadifard, Fateme and Mohammadi, Ida and Firouzabadi, Shahryar Rajai and Bagheri, Ali Baradaran and Ghaffary, Elham Moases and Mirmosayyeb, Omid},
  title   = {{Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression}},
  journal = {Alzheimer s Research \& Therapy},
  year    = {2025},
  doi     = {10.1186/s13195-025-01827-2},
  pmid    = {40775357},
  url     = {https://doi.org/10.1186/s13195-025-01827-2}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease Alzheimer's & dementia : the journal of the Alzheimer's Association · 2011-04-21 · usefulness score 70/100 · Getting a diagnosis
• Cognitive Tests to Detect Dementia: A Systematic Review and Meta-analysis JAMA internal medicine · 2015-09-01 · usefulness score 70/100 · Getting a diagnosis
• Alzheimer's Disease Neuroimaging Initiative (ADNI): clinical characterization Neurology · 2009-12-30 · usefulness score 68/100 · Mild cognitive impairment
• Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review Alzheimer's research & therapy · 2021-09-28 · usefulness score 65/100 · Digital health tools
• Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study NeuroImage · 2005-10-01 · usefulness score 63/100 · Mild cognitive impairment
• Reversion from mild cognitive impairment to normal or near-normal cognition: risk factors and prognosis Neurology · 2012-09-26 · usefulness score 62/100 · Getting a diagnosis
• Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD Neurology · 2004-02-01 · usefulness score 59/100 · Brain scans and imaging
• Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer's disease (MCI and dementia) in primary care: a review and synthesis Journal of internal medicine · 2021-03-31 · usefulness score 58/100 · Brain tissue and pathology
• 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease Brain : a journal of neurology · 2007-05-28 · usefulness score 57/100 · Mild cognitive impairment
• Mapping scores onto stages: mini-mental state examination and clinical dementia rating The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2006-02-01 · usefulness score 52/100 · Cognitive tests and assessment
• OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease medRxiv · 2019-12-15 · usefulness score 16/100 · Genetics

Related studies in Mild cognitive impairment

• Deep Learning-Based Framework for Predicting Mild Cognitive Impairment Progression in Neurology Using Longitudinal MRI IEEE Access · 2025-01-01 · usefulness score 49/100
• Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features IEEE Access · 2025-01-01 · usefulness score 34/100
• Predicting mild cognitive impairment in patients with Parkinson’s disease by integrating striatal MRI radiomics with clinical features BMC Medical Imaging · 2025-11-14 · usefulness score 40/100
• A radiomics model predicts progression from mild cognitive impairment to alzheimer’s disease using structural MRI Scientific Reports · 2025-10-13 · usefulness score 43/100
• Predicting Alzheimer's disease progression using multi-modal deep learning approach Scientific reports · 2019-02-13 · usefulness score 66/100
• Predicting Mild Cognitive Impairment to Alzheimer's Disease Progression by Explainable Multi‐Modal Framework Using Deep and Machine Learning Hybrid Model International Journal of Imaging Systems and Technology · 2026-06-09 · usefulness score 35/100

Also tagged “classification”

835 studies in the library carry the topic tag “classification”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.

• Comparison of 7.0-T T₂*-magnetic resonance imaging of cerebral bleeds in post-mortem brain sections of Alzheimer patients with their neuropathological correlates Cerebrovascular diseases (Basel, Switzerland) · 2011-03-21 · usefulness score 35/100 · Brain scans and imaging
• Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia Neuroinformatics · 2016-07-01 · usefulness score 38/100 · Digital health tools
• Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence NeuroImage · 2025-07-16 · usefulness score 49/100 · Getting a diagnosis

Similar studies

• Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer\\\\\\\'s Disease Progression 2026 · Brain scans and imaging
• Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs 2022 · Long-term cohort studies
• A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks 2019 · Long-term cohort studies
• A longitudinal analysis of T1-weighted MRI features associated with progression from mild cognitive impairment to Alzheimer’s disease 2026 · Mild cognitive impairment
• Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging 2003 · Mild cognitive impairment
• Small-Data Deep Learning for Alzheimer-Spectrum Classification from Structural MRI: A Feasibility Study Using OASIS 2026 · Brain scans and imaging

More on Mild cognitive impairment

• A review that covers this ground Progression of grey and white matter brain damage in Parkinson's disease: a critical review of structural MRI literature Journal of neurology · 2020-05-06 · usefulness score 54/100
• Newest in this subject Paths of Aging Minds: Health Journeys of Older Adults with Mild Cognitive Impairment Research Square · 2026-11-01 · Preprint — not yet peer-reviewed · usefulness score 16/100
• Newest in this subject Neuroinflammation is an early pathological process in a mouse model of prodromal Parkinsons disease bioRxiv · 2026-10-30 · Preprint — not yet peer-reviewed · usefulness score 16/100

See all studies in Mild cognitive impairment →