Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer\\\\\\\'s Disease Progression

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer\\\'s disease (AD), the leading cause of dementia worldwide, affects more than 55 million individuals and gen-erates annual healthcare costs exceeding two trillion USD [14]. A substantial proportion (30–40% per year) of pa-tients with mild cognitive impairment (MCI) progress to AD [2], making early and accurate prognostication essential for timely intervention, trial enrichment, and resource allocation. This paper presents a comprehensive review of a re-cent longitudinal MRI-based study by Aghajanian et al. [1], which integrates three-dimensional (3D) convolutional neural networks (CNNs), time-aware long short-term memory (T-LSTM) networks with attention mechanisms, and radiomics features to predict MCI-to-AD conversion using structural MRI. The cohort comprises 228 ADNI MCI participants with at least three T1-weighted MRI scans over an 18-month window (684 scans in total) [1]. A 3D Res-Net-18 backbone [9] extracts volumetric features, fed into a T-LSTM incorporating inter-scan intervals and attention mechanisms [10]. The best longitudinal model achieves a concordance index (c-index) of 0.90, with time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year conversion prediction, respectively, and an approximate 11-fold hazard ratio between high- and low-risk groups [1]. This review analyzes the methodology, highlights its strengths and weaknesses, and discusses key implications for clinical translation.

Key findings

• This paper presents a comprehensive review of a re-cent longitudinal MRI-based study by Aghajanian et al. [1], which integrates three-dimensional (3D) convolutional neural networks (CNNs), time-aware long short-term memory (T-LSTM) networks with attention mechanisms, and radiomics features to predict MCI-to-AD conversion using structural MRI.
• The cohort comprises 228 ADNI MCI participants with at least three T1-weighted MRI scans over an 18-month window (684 scans in total) [1].
• A 3D Res-Net-18 backbone [9] extracts volumetric features, fed into a T-LSTM incorporating inter-scan intervals and attention mechanisms [10].
• The best longitudinal model achieves a concordance index (c-index) of 0.90, with time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year conversion prediction, respectively, and an approximate 11-fold hazard ratio between high- and low-risk groups [1].
• This review analyzes the methodology, highlights its strengths and weaknesses, and discusses key implications for clinical translation.

Why it matters

A reviewed longitudinal MRI study using 3D CNNs, T-LSTM networks, and radiomics predicted MCI-to-AD conversion with a c-index of 0.90 and time-specific AUCs of 0.96, 0.94, and 0.89 for 2-, 3-, and 5-year prediction. This is a prediction and prognostication tool, not a treatment, cure, or prevention.

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Study details

• Study designlongitudinal study
• Evidence strengthModerate

Read the original

• DOI: 10.5281/zenodo.18620943
• Original source →
• Free full text: Zenodo (CERN European Organization for Nuclear Research) →

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

@article{pawar2026-4f4d861f,
  author  = {Pawar, Diksha and Boaddh, Jayshree and Patidar, Prof. Rahul},
  title   = {{Longitudinal Structural MRI-Based Deep Learning And Radiomics Features For Predicting Alzheimer's Disease Progression}},
  journal = {Zenodo (CERN European Organization for Nuclear Research)},
  year    = {2026},
  doi     = {10.5281/zenodo.18620943},
  url     = {https://doi.org/10.5281/zenodo.18620943}
}

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