Investigating Alzheimer’s Disease Progression Using a Radiomics Approach: The Hippocampal-Amygdala Border in FDG-Positron Emission Tomography Scans
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Bibliographic record
Abstract
Introduction: This study introduces a novel and simple radiomics approach to identify highly sensitive and interpretable imaging biomarkers for tracking Alzheimer's disease (AD) progression using FDG-positron emission tomography (PET) imaging. Our unique focus is on a custom-defined hippocampal-amygdala border region. We hypothesize that this specific small, anatomically and biologically critical, yet underexplored interface region can effectively capture subtle, early stage metabolic deterioration indicative of AD progression. METHODS: We leveraged 18F-FDG-PET scans from 513 participants across the AD spectrum (control normal [CN], mild cognitive impairment [MCI], AD) from the Alzheimer's Disease Neuroimaging Initiative database. Building on the established involvement of the hippocampus, amygdala, and entorhinal cortex, we innovatively defined the hippocampal-amygdala connecting region using a distance transform approach to specifically capture the metabolic interplay between these vital structures. A rigorous radiomics pipeline was then applied, involving systematic evaluation of eight feature selection techniques combined with six classification models to identify the most effective predictive framework. RESULTS: Our findings demonstrate the high discriminatory power of the hippocampal-amygdala border region for AD diagnosis and monitoring of disease progression. A concise set of radiomic features derived from this single, novel region of interest (ROI) exhibited high predictive accuracy across various diagnostic distinctions: two features (specifically, shape MeshVolume Right and gldm SmallDependenceLowGrayLevelEmphasis left) distinguished AD from CN with ROC AUC = 0.914; two distinct features predicted MCI from AD with ROC AUC = 0.796; and two other features (shape LeastAxisLength left and glszm LargeAreaEmphasis left) differentiated CN from MCI with ROC AUC = 0.691. Crucially, the mean values of these identified features consistently demonstrated statistically significant incremental deterioration (p < 0.05) across consecutive AD stages (CN to MCI, MCI to AD), underscoring their sensitivity to disease progression. CONCLUSION: This study establishes the clinical potential of radiomics in providing highly sensitive and interpretable biomarkers for monitoring AD progression, specifically by targeting the novel, biologically informed hippocampal-amygdala border ROI on FDG-PET. By identifying distinct, parsimonious sets of robust radiomic features for different disease stages, our approach offers an efficient, noninvasive, and clinically translatable tool that balances diagnostic power with interpretability, paving the way for its integration into existing clinical workflows for AD. .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it