A radiomics model predicts progression from mild cognitive impairment to alzheimer’s disease using structural MRI

Topic: Amyloid, tau and blood-test diagnosis

The aim of this study is to build and validate a model based on structural magnetic resonance imaging (sMRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). A total of 343 patients with MCI were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database as study subjects. Among them, 154 patients progressed to AD during the 48-month follow-up. All subjects were randomly divided into a training set (n = 240) and a validation set (n = 103) in a 7:3 ratio according to enrollment time. The baseline T1-weighted (T1W) structural MR images of each patient were automatically segmented into whole-brain three-dimensional (3D) white and gray matter images based on the training set data. In addition, radiomics signatures were extracted from each structural image. Baseline neuropsychological scores were combined with the radiomics signatures to construct a prediction model using machine learning. The diagnostic accuracy and reliability of the model were evaluated using the receiver operating characteristic (ROC) curve analysis in both the training and validation sets. Stepwise logistic regression analysis showed that clinical dementia rating (CDR), Alzheimer's Disease Assessment Scale (ADAS-cog) and radiomics markers were independent predictors of progression from MCI to AD. ROC curve showed that the AUC values of CDR, ADAS-cog and radiomics markers in the training set and validation set were 0.895 and 0.882, respectively. The sensitivity was 0.933 and 0.977, and the specificity was 0.669 and 0.661, respectively. DeLong test showed that the diagnostic efficacy of the comprehensive model was significantly different from that of the independent predictors (P = 0.023). The integrated model, based on structural analysis of magnetic resonance images, can accurately identify and predict individuals with MCI at high risk of progressing to AD.

@article{li2025-41083541,
  author  = {Li, Yifei and Yi, Pengcheng and Jin, Mingmin and Li, Yifan and Chen, Wei},
  title   = {{A radiomics model predicts progression from mild cognitive impairment to alzheimer’s disease using structural MRI}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-19586-3},
  pmid    = {41083541},
  url     = {https://doi.org/10.1038/s41598-025-19586-3}
}

Key findings

• Stepwise logistic regression analysis showed that clinical dementia rating (CDR), Alzheimer's Disease Assessment Scale (ADAS-cog) and radiomics markers were independent predictors of progression from MCI to AD.
• ROC curve showed that the AUC values of CDR, ADAS-cog and radiomics markers in the training set and validation set were 0.895 and 0.882, respectively.
• DeLong test showed that the diagnostic efficacy of the comprehensive model was significantly different from that of the independent predictors (P = 0.023).

Why it matters

A model combining clinical dementia rating, Alzheimer's Disease Assessment Scale scores and radiomics markers from structural MRI predicted progression from mild cognitive impairment to Alzheimer's disease. It is a prediction model only and does not test any treatment, cure or prevention.

Study details

• Participants240
• Evidence strengthLow

Read the original

• DOI: 10.1038/s41598-025-19586-3
• PubMed: 41083541
• Original source →
• Free full text: Scientific Reports →

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Studies this one cites

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

• Risk factors for predicting progression from mild cognitive impairment to Alzheimer's disease: a systematic review and meta-analysis of cohort studies Journal of neurology, neurosurgery, and psychiatry · 2015-05-22 · usefulness score 75/100 · Mild cognitive impairment
• Mild cognitive impairment: long-term course of four clinical subtypes Neurology · 2006-12-01 · usefulness score 69/100 · Getting a diagnosis
• The diagnosis of dementia due to 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 61/100 · Getting a diagnosis
• The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging‐Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease Alzheimer s & Dementia · 2011-04-22 · usefulness score 61/100 · Mild cognitive impairment
• Biomarker-based prediction of progression in MCI: Comparison of AD signature and hippocampal volume with spinal fluid amyloid-β and tau Frontiers in aging neuroscience · 2013-10-11 · usefulness score 47/100 · Mild cognitive impairment

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• 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
• 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
• Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification PloS one · 2016-02-22 · usefulness score 55/100
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Also tagged “classification”

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• In vivo staging of regional amyloid deposition Neurology · 2017-10-18 · usefulness score 70/100 · How common it is
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• From healthy aging to early Alzheimer's disease: in vivo detection of entorhinal cortex atrophy Annals of the New York Academy of Sciences · 2000-06-01 · usefulness score 36/100 · Hippocampus size on brain scans

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