ArticleCommunications medicine2026
Personalized mapping of body homeostasis using whole-body PET connectomics and routine FDG PET imaging.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundImmuno-inflammation and systemic alterations are key features of chronic diseases. While PET molecular imaging is widely used in precision medicine, conventional analyses are lesion-centric, focusing on detection, localization, and quantification. Such approaches overlook disease-induced homeostatic changes occurring at the whole-body level. Recently, PET connectomics has emerged as a graph-based method to characterize metabolic crosstalk between organs. In this study, we introduce a framework for generating individualized PET-based connectomes, enabling robust assessment of personalized systemic homeostasis.
methodsWe analyzed routine PET imaging data from a tertiary care center, including patients with advanced systemic disease (N = 22 highly selected patients with Group I advanced pulmonary arterial hypertension) and 46 matched controls. Our computational framework captures the voxel-wise distributional profile of radiotracer uptake within organs, rather than relying on summary measures. Pairwise metabolic distances between organ distributions were used to construct subject-specific, whole-body metabolic networks - termed connectomes. Machine learning and statistical modeling were applied to evaluate the ability of these networks to distinguish disease states and map multi-organ metabolic interactions.
resultsHere we show that this framework successfully generates stable, individualized metabolic networks from a single PET scan. A graph-based classifier differentiates patients from controls with 75% accuracy. Notably, metabolic connections involving the right heart emerge as the primary drivers of disease discrimination, consistent with the known pathophysiology of advanced pulmonary arterial hypertension. Group-level analyses corroborate these findings, revealing specific alterations in network connectivity.
conclusionsPersonalized PET-based connectomics can detect individual-level homeostatic perturbations using standard imaging protocols. This non-invasive approach offers a promising strategy to characterize the systemic impact of chronic diseases and represents a shift from population-level analyses toward truly personalized metabolic phenotyping.
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