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However, early noninvasive differentiation remains challenging.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n With the development of total\u2010body PET\/CT, this study attempts to provide new insights into this challenge by analyzing and comparing inter\u2010organ and inter\u2010system metabolic connections across the total body in ADC and SCC patients.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Based on static and dynamic total\u2010body\n 18<\/jats:sup>\n F\u2010fluorodeoxyglucose (\n 18<\/jats:sup>\n F\u2010FDG) PET\/CT data from 12 ADC patients, 12 SCC patients, and 20 healthy controls (HCs), abnormal metabolic connection networks of ADC and SCC were constructed relative to HCs for individual and group analysis. An external patient cohort consisting of 7 ADC patients and 7 SCC patients was introduced to evaluate the generalizability of the analysis results by topological correlation. The metabolic correlations among organs\/systems were observed by Pearson correlation coefficient between standardized uptake values or\n K<\/jats:italic>\n i<\/jats:sub>\n values of 36 regions of interest (ROIs) across the total body. ROIs were also classified into 18 organs and 8 major systems. Specifically, for the individual analysis, the alteration in metabolic connections between the lungs (respiratory system), and other systems were investigated, as well as among the seven nonrespiratory systems, relative to HCs. For the group analysis, the abnormal metabolic connections across the total body of the ADC group and the SCC group were analyzed at the organ and system levels to explore the disease abnormal patterns of different LC subtypes.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n At the individual level, ADC and SCC patients exhibited distinct patterns of abnormal metabolic connectivity between the lungs (respiratory system) and other systems, highlighting subtype\u2010specific metabolic reorganization. Both subtypes consistently showed significant abnormalities in nervous\u2013motor and nervous\u2013digestive system connectivity among the seven nonrespiratory systems. At the group level, ADC patients showed significant metabolic connectivity abnormalities mainly between specific organs and systems, with most changes reflecting reduced connectivity as compared to HCs. In contrast, SCC patients demonstrated markedly enhanced connectivity across multiple organ and system pairs, consistent with their broader diffusion patterns in clinical practice. Notably, dynamic\n K<\/jats:italic>\n i<\/jats:sub>\n \u2010based analysis was more sensitive than static SUV\u2010based analysis in capturing these subtype\u2010specific systemic metabolic alterations. These findings were highly consistent across internal and external cohorts, as evidenced by the generally strong topological correlations (Pearson correlation coefficient\u00a0>\u00a00.9) at both the organ and system levels. Moreover, the raw SUV and\n K<\/jats:italic>\n i<\/jats:sub>\n analyses of organs among HC, ADC, and SCC groups provided complementary evidence to the metabolic network findings.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n The proposed abnormal metabolic networks are feasible to characterize the metabolic alterations between organs or systems in ADC and SCC patients based on static and dynamic total\u2010body PET\/CT relative to HCs. Dynamic\n K<\/jats:italic>\n i<\/jats:sub>\n \u2010based analysis sensitively captures the complex and heterogeneous metabolic network alterations in LC subtypes, with SCC showing more extensive systemic abnormalities than ADC. The findings have the potential to enhance understanding of LC's physiological mechanisms and inform precision medicine strategies for early diagnosis and tailored therapies.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mp.70147","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T03:45:57Z","timestamp":1763610357000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Metabolic network analysis of lung adenocarcinoma and squamous cell carcinoma via static and dynamic total\u2010body PET\/CT imaging"],"prefix":"10.1002","volume":"52","author":[{"given":"Haiyan","family":"Wang","sequence":"first","affiliation":[{"name":"Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau, Taipa Macau SAR China"},{"name":"Research Center for Medical AI Shenzhen Institute of Advanced Technology Chinese Academy of 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