BMC medical imaging · 2026
Multi-organ metabolic connectivity mapping in advanced pulmonary arterial hypertension: a group-level 18 F-FDG PET-controlled study.
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Rey Marc-Antoine, Varet Suzanne, Roche Anne, Ghidaglia Jérôme, Mas Remy, Jentreau Yannick, Montani David, Onephandara Stéphane, Humbert Marc, Amoin Aude, Savale Laurent, Faure Sylvain, Besson Florent L
Аннотация
BACKGROUND: To explore potential systemic alterations in carbohydrate metabolism associated with advanced pulmonary arterial hypertension (PAH) using group-level PET-based metabolic connectivity mapping.
METHODS: This retrospective, controlled study analysed 18F-FDG PET-CT scans from 75 individuals (29 with PAH, 46 controls). Eleven major organs were segmented using AI-based tools and voxel-level PET data were extracted. Inter-organ metabolic profiles in PAH and control groups were evaluated using a Maximum mean discrepancy (MMD) framework with extensive permutation testing (1,000,000 permutations) to assess intra-group homogeneity and detect between-group distributional differences. Upon confirmation of within-group homogeneity, organ-level metabolic connectomes were derived from Spearman correlation matrices, with Holm-adjusted multiple testing correction and significance filtering (t-test, p < 0.05). Between-group comparisons were similarly performed using MMD, flowed by post-hoc organ-pair analyses and construction of differential connectomes to localize statistical metabolic networks divergence.
RESULTS: Standard SUV analysis revealed no significant intergroup differences across most organs except for increased uptake in the right heart in PAH patients. MMD testing confirmed intra-group homogeneity in both controls (0,0048) and PAH (0,0465), with no rejection of H0 at α = 0.05, while demonstrating significant between-group differences (H0 rejected). Spearman-based PET connectomes, retaining only significant correlations (𝜌 ≠ 0; p < 0.05) revealed perturbated metabolic network in PAH. This altered network involved the heart, adipose tissue, liver, spleen, muscle, and bone marrow.
CONCLUSION: Group-level whole-body 18F-FDG PET connectivity analysis may provide additional insights into systemic metabolic alterations in advanced PAH that are not readily captured by conventional regional SUV assessments. These findings suggest that PET-based connectomics could complement existing methods for assessing metabolic involvement in PAH.
METHODS: This retrospective, controlled study analysed 18F-FDG PET-CT scans from 75 individuals (29 with PAH, 46 controls). Eleven major organs were segmented using AI-based tools and voxel-level PET data were extracted. Inter-organ metabolic profiles in PAH and control groups were evaluated using a Maximum mean discrepancy (MMD) framework with extensive permutation testing (1,000,000 permutations) to assess intra-group homogeneity and detect between-group distributional differences. Upon confirmation of within-group homogeneity, organ-level metabolic connectomes were derived from Spearman correlation matrices, with Holm-adjusted multiple testing correction and significance filtering (t-test, p < 0.05). Between-group comparisons were similarly performed using MMD, flowed by post-hoc organ-pair analyses and construction of differential connectomes to localize statistical metabolic networks divergence.
RESULTS: Standard SUV analysis revealed no significant intergroup differences across most organs except for increased uptake in the right heart in PAH patients. MMD testing confirmed intra-group homogeneity in both controls (0,0048) and PAH (0,0465), with no rejection of H0 at α = 0.05, while demonstrating significant between-group differences (H0 rejected). Spearman-based PET connectomes, retaining only significant correlations (𝜌 ≠ 0; p < 0.05) revealed perturbated metabolic network in PAH. This altered network involved the heart, adipose tissue, liver, spleen, muscle, and bone marrow.
CONCLUSION: Group-level whole-body 18F-FDG PET connectivity analysis may provide additional insights into systemic metabolic alterations in advanced PAH that are not readily captured by conventional regional SUV assessments. These findings suggest that PET-based connectomics could complement existing methods for assessing metabolic involvement in PAH.