ArticleFrontiers in medicine2026
Unsupervised clustering of PET/CT features in fever of unknown origin (FUO) and inflammation of unknown origin (IUO).
Article in Frontiers in 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
Aim: The aim of this study was to classify fever of unknown origin (FUO) patients based on PET/CT imaging features using unsupervised clustering, and to explore whether these patterns could provide insights into underlying etiologies. Materials and methods: We conducted a retrospective cohort study of adult FUO patients who underwent 18F-FDG PET/CT. For each patient, data were extracted on involvement of reticuloendothelial organs, parenchymal organs, and large vessels, including the maximum SUVmax within each involved system and the uptake pattern (focal, diffuse, or mixed). Patients were clustered based on these PET/CT features using four unsupervised algorithms (hierarchical clustering, hierarchical density-based spatial clustering of applications with noise, spectral clustering, K-prototypes clustering), and a consensus clustering approach was applied to derive robust final clusters. Cluster assignments were compared with final clinical diagnoses using the Adjusted Rand Index (ARI) and Mutual Information (MI) to explore clinical relevance. Results: The study included 289 FUO patients with a mean age of 59 ± 17 years, comprising 128 (44.3%) women and 161 (55.7%) men. Consensus clustering of PET/CT features in FUO patients identified four distinct clusters: Cluster 0 with combined reticuloendothelial, parenchymal, and large artery involvement, Cluster 1 with prominent reticuloendothelial and parenchymal involvement, Cluster 2 with mild reticuloendothelial involvement, and Cluster 3 with selective parenchymal involvement. Infections were the predominant etiologic category across all clusters, while rheumatologic diseases also contributed substantially, particularly in Cluster 2. Comparison with final diagnoses showed low concordance (ARI 0.006, NMI 0.047; Chi-square 32.46, Conclusion: Consensus clustering of PET/CT features in FUO patients revealed distinct imaging phenotypes, but these clusters were not effective in directly identifying final clinical diagnoses, underscoring PET/CT's complementary role in evaluation.
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