# Subregional limbic radiomics on FDG-PET provides accurate early detection of Alzheimer’s disease

## TL;DR

This study uses FDG-PET radiomics to detect Alzheimer's disease early by analyzing specific brain regions like the hippocampus and amygdala.

## Contribution

The study introduces subregional limbic radiomics as a novel method for early and accurate detection of Alzheimer's disease using FDG-PET imaging.

## Key findings

- The MLP model with LASSO achieved high accuracy in distinguishing Alzheimer's patients from healthy individuals and MCI cases.
- Key subregions like the accessory basal nucleus and presubiculum head were identified as critical biomarkers for early AD detection.
- Radiomic features such as GLRLM and GLDM captured subtle metabolic changes useful for diagnosing and staging Alzheimer's disease.

## Abstract

To investigate the radiomics features of the hippocampus and the amygdala subregions in FDG-PET images that can best differentiate Mild Cognitive Impairment (MCI), Alzheimer’s Disease (AD), and healthy patients.

Baseline FDG-PET data from 555 participants in the ADNI dataset were analyzed, comprising 189 cognitively normal (CN) individuals, 201 with MCI, and 165 with AD. We extracted 120 quantitative features from finely and automatically parcellated subregions (hippocampal n = 38, amygdala n = 18) using a probabilistic atlas. To identify the most effective classification model, we applied four feature selection techniques, ANOVA, PCA, LASSO, and Chi-square, combined with nine different classifiers, resulting in 36 unique model combinations. This comprehensive evaluation enabled the selection of a high-performing machine learning pipeline.

The Multi-Layer Perceptron (MLP) model combined with LASSO demonstrated excellent classification performance: ROC AUC of 0.957 for CN vs. AD, ROC AUC of 0.867 for MCI vs. AD, and ROC AUC of 0.782 for CN vs. MCI. Key regions, including the accessory basal nucleus, presubiculum head, and CA4 head, were identified as critical biomarkers. Features including GLRLM (Long Run Emphasis) and Small Dependence Emphasis (GLDM) showed strong diagnostic potential, reflecting subtle metabolic and microstructural changes often preceding anatomical alterations.

Specific hippocampal and amygdala subregions and their four radiomic features were found to have a significant role in the early diagnosis of AD, its staging, and its severity assessment by capturing subtle shifts in metabolic patterns. Furthermore, these features offer potential insights into the disease’s underlying mechanisms and model interpretability.

The online version contains supplementary material available at 10.1186/s12880-026-02168-8.