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Purpose <p>This study presents a parsimonious and interpretable radiomic framework for the classification of Alzheimer’s Disease (AD) and its clinical stages using hippocampal MRI. The objective is to evaluate whether compact bivariate models can provide reliable diagnostic performance while maintaining clinical transparency.</p> Methods <p>Structural T1-weighted MRI data from 406 subjects (313 healthy controls and 93 AD patients) were obtained from the OASIS-1 dataset. Automated hippocampal segmentation was followed by the extraction of 25 radiomic features per hemisphere in accordance with Image Biomarker Standardisation Initiative (IBSI) guidelines. All possible two-feature combinations were evaluated using Support Vector Machines with radial basis function kernels. Model training employed stratified 8-fold cross-validation with inverse-frequency class weighting, and final performance was assessed on an independent held-out test set.</p> Results <p>Bivariate models achieved robust discriminative performance. The combination of hippocampal Volume and Surface Area reached an AUC-ROC of 0.887 in the right hemisphere. In early-stage AD (CDR 0.5), texture-based features (Energy and Sum Entropy) in the left hippocampus provided the most balanced early-stage performance (AUC-ROC = 0.872). In mild-to-moderate stages (CDR <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\ge\)</EquationSource> </InlineEquation> 1), morphological features dominated classification, achieving AUC-ROC values above 0.90.</p> Conclusion <p>A compact radiomic framework based on two hippocampal features provides an effective and interpretable approach for AD classification. The findings indicate a stage-dependent diagnostic pattern in which textural heterogeneity is more sensitive to early disease, whereas macroscopic atrophy becomes the dominant marker in later stages.</p>

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A Parsimonious IBSI-Compliant Radiomic Framework for Interpretable Detection of Alzheimer’s Disease Across Clinical Stages

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Anabel Mayorga-Ruiz,
  • Eduardo Cañete-Carmona,
  • Aurora Sáez

摘要

Purpose

This study presents a parsimonious and interpretable radiomic framework for the classification of Alzheimer’s Disease (AD) and its clinical stages using hippocampal MRI. The objective is to evaluate whether compact bivariate models can provide reliable diagnostic performance while maintaining clinical transparency.

Methods

Structural T1-weighted MRI data from 406 subjects (313 healthy controls and 93 AD patients) were obtained from the OASIS-1 dataset. Automated hippocampal segmentation was followed by the extraction of 25 radiomic features per hemisphere in accordance with Image Biomarker Standardisation Initiative (IBSI) guidelines. All possible two-feature combinations were evaluated using Support Vector Machines with radial basis function kernels. Model training employed stratified 8-fold cross-validation with inverse-frequency class weighting, and final performance was assessed on an independent held-out test set.

Results

Bivariate models achieved robust discriminative performance. The combination of hippocampal Volume and Surface Area reached an AUC-ROC of 0.887 in the right hemisphere. In early-stage AD (CDR 0.5), texture-based features (Energy and Sum Entropy) in the left hippocampus provided the most balanced early-stage performance (AUC-ROC = 0.872). In mild-to-moderate stages (CDR \(\ge\) 1), morphological features dominated classification, achieving AUC-ROC values above 0.90.

Conclusion

A compact radiomic framework based on two hippocampal features provides an effective and interpretable approach for AD classification. The findings indicate a stage-dependent diagnostic pattern in which textural heterogeneity is more sensitive to early disease, whereas macroscopic atrophy becomes the dominant marker in later stages.