Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study

Fan Yang; Jiehui Jiang; Ian Alberts; Min Wang; Taoran Li; Xiaoming Sun; Axel Rominger; Chuantao Zuo; Kuangyu Shi

doi:10.21037/atm-21-4349

Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer's disease: an exploratory radiomic analysis study

Ann Transl Med. 2022 May;10(9):513. doi: 10.21037/atm-21-4349.

Authors

Fan Yang^#¹, Jiehui Jiang², Ian Alberts^#³, Min Wang^#¹, Taoran Li⁴, Xiaoming Sun¹, Axel Rominger³, Chuantao Zuo^{5

6}, Kuangyu Shi^{3

7}

Affiliations

¹ Institute of Biomedical Engineering, School of Information and Communication Engineering, Shanghai University, Shanghai, China.
² Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China.
³ Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.
⁴ Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
⁵ PET Center, Huashan Hospital, Fudan University, Shanghai, China.
⁶ Human Phenome Institute, Fudan University, Shanghai, China.
⁷ Department of Informatics, Technische Universität München, Munich, Germany.

^# Contributed equally.

Abstract

Background: This study aimed to explore the potential of a combination of 18F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) and magnetic resonance imaging (MRI) to improve predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The predictive performances and specific associated biomarkers of these imaging techniques used alone (single-modality imaging) and in combination (dual-modality imaging) were compared.

Methods: This study enrolled 377 patients with MCI and 94 healthy control participants from 2 medical centers. Enrolment was based on the patients' brain MRI and PET images. Radiomic analysis was performed to evaluate the predictive performance of dual-modality ¹⁸F-FDG PET and MRI scans. Regions of interest (ROIs) were determined using an a priori brain atlas. Radiomic features in these ROIs were extracted from the MRI and ¹⁸F-FDG PET scan data. These features were either concatenated or used separately to select features and construct Cox regression models for prediction in each modality. Harrell's concordance index (C-index) was then used to assess the predictive accuracies of the resulting models, and correlations between the MRI and ¹⁸F-FDG PET features were evaluated.

Results: The C-indices for the two test datasets were 0.77 and 0.80 for dual-modality ¹⁸F-FDG PET/MRI, 0.75 and 0.73 for single-modality ¹⁸F-FDG PET, and 0.74 and 0.76 for single-modality MRI. In addition, there was a significant correlation between the crucial image signatures of the different modalities.

Conclusions: These results indicate the value of imaging features in monitoring the progress of MCI in populations at high risk of developing AD. However, the incremental benefit of combining ¹⁸F-FDG PET and MRI is limited, and radiomic analysis of a single modality may yield acceptable predictive results.

Keywords: Cox model; Mild cognitive impairment (MCI); magnetic resonance imaging (MRI); positron emission tomography (PET); radiology.

Grants and funding

U01 AG024904/AG/NIA NIH HHS/United States