A novel approach of [18F]FDG PET-based individual metabolic radiomics network to predict cognitive impairment in multiple system atrophy

作者：Daoyan Hu, Xiaofeng Dou, Jing Wang, Chentao Jin, Ke Liu, Rui Zhou, Xiaohui Zhang, Congcong Yu, Yan Zhong, Mei Tian, Hong Zhang · 发表于：NeuroImage · 年份：2025 · DOI：10.1016/j.neuroimage.2025.121433 · 被引用次数：1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Neurological Disease Mechanisms and Treatments、Cerebrovascular and genetic disorders

PURPOSE: F]FDG PET imaging to investigate brain metabolic connectivity patterns of MSA and validated the usefulness of IMRN-based predictive model for MSA-related cognitive impairment. METHODS: F]FDG PET/CT scans. IMRN was constructed by extracting non-redundant radiomics features from each brain region and computing pairwise Pearson correlation coefficients among these features. The validation of IMRN included assessments of small-world properties, test-retest reliability, and metabolic-genetic correlations. Connectome-based predictive modeling (CPM) was implemented to predict Mini Mental State Examination (MMSE) scores, while network-based statistics (NBS) were compared between MSA patients with cognitive impairment (MSA-CI, n = 58; MMSE < 27) and those with normal cognition (MSA-NC, n = 57; MMSE ≥ 27). A support vector machine (SVM) classifier for detecting MSA-CI was developed using discriminative IMRN edges. RESULTS: IMRN showed small-world properties (σ > 1), high reliability (average edge ICC = 0.754), and a significant correlation with gene expression (r = 0.44, P < 0.001). CPM significantly predicted cognitive scores through IMRN edges (positive network: r = 0.27, P = 0.03; negative network: r = 0.28, P = 0.02). NBS revealed decreased cerebellar-cortical connectivity (73 edges) and increased intra-cerebellar/limbic connectivity (24 edges) in MSA-CI compared to MSA-NC. The IMRN-based SVM outperformed SUVR-based SVM in classifying MSA-CI (accuracy: 73.91% vs 62.61%; AU...