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Objective <p>Reliable assessment of cerebral amyloid-β (Aβ) deposition is essential for the diagnosis and management of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of integrating radiomics-enhanced <sup>18</sup>F-florbetapir positron emission tomography/magnetic resonance imaging (<sup>18</sup>F-AV45 PET/MRI) features for Aβ status evaluation and to further explore their potential for continuous Centiloid prediction in AD.</p> Materials and methods <p>Ninety-four subjects who underwent (<sup>18</sup>F-AV45 PET/MRI (60 Aβ-positive, 34 Aβ-negative) were retrospectively included. Standardized uptake value ratio (SUVr) features were extracted from seven cortical regions (frontal, temporal, parietal, occipital, insular, cingulate, and white matter), and corresponding T1-weighted images' radiomics features were computed. Three feature sets (PET, radiomics, and combined) were analyzed using logistic regression (LR), k-nearest neighbor (kNN), and linear discriminant analysis (LDA) with 10-fold cross-validation. The best performing model was further interpreted using SHapley Additive exPlanations (SHAP) analysis. Additionally, Centiloid regression was performed using random forest, ElasticNet, and ExtraTrees regressors.</p> Results <p>The combined feature achieved the best performance with the LR model, with area under the receiver operating characteristic curve = 0.9373, accuracy = 0.8723, F1-score = 0.898). SHAP analysis identified biologically meaningful features derived from both radiomics and PET modalities, showing clear inter-group separation. In Centiloid regression, the ExtraTrees model achieved strong agreement with measured values.</p> Conclusion <p>This framework provides an interpretable and quantitative solution for amyloid evaluation, enabling both categorical Aβ status discrimination and continuous Centiloid estimation from routine PET/MRI data. This approach represents a proof-of-concept for supporting <sup>18</sup>F-AV45 PET-based assessment in AD.</p> Relevance statement <p>This study demonstrates that radiomics-enhanced PET/MR features can reliably predict both Aβ status and Centiloid values without specialized processing platforms, offering a clinically deployable, standardized, and interpretable approach to improve AD diagnosis and monitoring.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Radiomics-enhanced <sup>18</sup>F-AV45 PET/MRI enabled quantitative Aβ evaluation in AD.</p> </ItemContent> <ItemContent> <p>Radiomics-enhanced <sup>18</sup>F-AV45 PET/MRI provided a noninvasive and interpretable assessment to improve clinical confidence.</p> </ItemContent> <ItemContent> <p>Radiomics-enhanced <sup>18</sup>F-AV45 PET/MRI allowed Centiloid estimation without specialized platforms for wider clinical use.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Radiomics-enhanced 18F-AV45 PET/MRI for integrative assessment and centiloid estimation of amyloid-β burden in Alzheimer’s disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Zengbei Yuan,
  • Jianzhou Zhang,
  • Zirong Zhou,
  • Xing Chen,
  • Na Qi,
  • Weilun Wang,
  • Xinwei Cheng,
  • Yingkang Lin,
  • Jun Zhao

摘要

Objective

Reliable assessment of cerebral amyloid-β (Aβ) deposition is essential for the diagnosis and management of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of integrating radiomics-enhanced 18F-florbetapir positron emission tomography/magnetic resonance imaging (18F-AV45 PET/MRI) features for Aβ status evaluation and to further explore their potential for continuous Centiloid prediction in AD.

Materials and methods

Ninety-four subjects who underwent (18F-AV45 PET/MRI (60 Aβ-positive, 34 Aβ-negative) were retrospectively included. Standardized uptake value ratio (SUVr) features were extracted from seven cortical regions (frontal, temporal, parietal, occipital, insular, cingulate, and white matter), and corresponding T1-weighted images' radiomics features were computed. Three feature sets (PET, radiomics, and combined) were analyzed using logistic regression (LR), k-nearest neighbor (kNN), and linear discriminant analysis (LDA) with 10-fold cross-validation. The best performing model was further interpreted using SHapley Additive exPlanations (SHAP) analysis. Additionally, Centiloid regression was performed using random forest, ElasticNet, and ExtraTrees regressors.

Results

The combined feature achieved the best performance with the LR model, with area under the receiver operating characteristic curve = 0.9373, accuracy = 0.8723, F1-score = 0.898). SHAP analysis identified biologically meaningful features derived from both radiomics and PET modalities, showing clear inter-group separation. In Centiloid regression, the ExtraTrees model achieved strong agreement with measured values.

Conclusion

This framework provides an interpretable and quantitative solution for amyloid evaluation, enabling both categorical Aβ status discrimination and continuous Centiloid estimation from routine PET/MRI data. This approach represents a proof-of-concept for supporting 18F-AV45 PET-based assessment in AD.

Relevance statement

This study demonstrates that radiomics-enhanced PET/MR features can reliably predict both Aβ status and Centiloid values without specialized processing platforms, offering a clinically deployable, standardized, and interpretable approach to improve AD diagnosis and monitoring.

Key Points

Radiomics-enhanced 18F-AV45 PET/MRI enabled quantitative Aβ evaluation in AD.

Radiomics-enhanced 18F-AV45 PET/MRI provided a noninvasive and interpretable assessment to improve clinical confidence.

Radiomics-enhanced 18F-AV45 PET/MRI allowed Centiloid estimation without specialized platforms for wider clinical use.

Graphical Abstract