PURPOSE: Cancer is increasingly recognized not just as a localized disease but as a systemic condition with profound impacts on metabolism at both cellular and whole-body levels. This study seeks to unveil the systemic metabolic disruptions in early-stage, untreated lung cancer patients, specifically adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), using a novel network-based approach with total-body static and dynamic PET/CT imaging. By analyzing inter-organ metabolic dependencies, we aim to uncover how lung cancer induces whole-body metabolic reprogramming, providing insights into potential biomarkers for monitoring disease progression and treatment response. METHODS: This retrospective study included 32 early-stage untreated lung cancer patients and 20 healthy volunteers. Static and dynamic total-body PET/CT scans were performed to assess glucose consumption across the body. Twenty-five regions of interest (ROIs) representing major organs were selected, and metabolic status was quantified using the average SUV and Ki values for each ROI. Inter-organ metabolic dependencies were quantified using mutual information (MI), which measures the amount of shared information between two variables, capturing both linear and nonlinear relationships, followed by Bonferroni correction to control for multiple comparisons. Population-level metabolic networks were constructed to visualize alterations in interregional connectivity for ADC, SqCC, and healthy cohorts. Furthermore, to capture personalized metabolic deviations, individual networks were constructed for each cancer patient. RESULTS: The analysis revealed distinct metabolic network patterns in ADC and SqCC patients compared to healthy controls. ADC patients exhibited selective enhancements in metabolic connectivity, particularly between the central nervous system and peripheral organs such as the adrenal glands and pancreas, suggesting activation of compensatory neuroendocrine mechanisms. In contrast, SqCC patients showed widespread reductions in metabolic connectivity, indicative of a systemic metabolic breakdown associated with disease progression. Individual-level network analysis highlighted personalized metabolic deviations. CONCLUSION: Total-body PET/CT combined with network-based methods facilitates the quantitative visualization of systemic metabolic alterations in lung cancer. ADC and SqCC exhibit unique metabolic profiles that may offer insights into disease progression and the identification of potential biomarkers for therapeutic monitoring. Larger, longitudinal studies are required to validate these findings and further explore their clinical relevance for early diagnosis and treatment stratification in lung cancer. CLINICAL TRIAL NUMBER: Not applicable.