NeuroImage · 2025
A novel approach of [18F]FDG PET-based individual metabolic radiomics network to predict cognitive impairment in multiple system atrophy.
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Hu Daoyan, Dou Xiaofeng, Wang Jing, Jin Chentao, Liu Ke, Zhou Rui, Zhang Xiaohui, Yu Congcong, Zhong Yan, Tian Mei, Zhang Hong
Аннотация
PURPOSE: Many efforts have been tried to evaluate multiple system atrophy (MSA)-related cognitive impairment, however, there is still lacking of effective approach. In this study, for the first time, we developed the individual metabolic radiomics networks (IMRN) using [18F]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: In this retrospective study, we recruited 115 MSA patients with [18F]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%; AUC: 0.80 vs 0.69).
CONCLUSION: This study established a novel approach of IMRN for assessing whole brain metabolic connectivity, uncovering distinct cerebellar connectivity patterns in MSA-CI, which held promise for facilitating personalized cognitive evaluations in MSA.
METHODS: In this retrospective study, we recruited 115 MSA patients with [18F]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%; AUC: 0.80 vs 0.69).
CONCLUSION: This study established a novel approach of IMRN for assessing whole brain metabolic connectivity, uncovering distinct cerebellar connectivity patterns in MSA-CI, which held promise for facilitating personalized cognitive evaluations in MSA.