Simple SummaryIntegration of multimodality imaging (MMI) methods in head and neck squamous cell carcinomas (HNSCC) provides complementary information of the tumor and its microenvironment. Quantitative positron emission tomography (PET)/computed tomography (CT), DW- and DCE-MRI provide the functional information of tumor tissue based on metabolic process, diffusion of water molecules, and enhancement of water proton relaxation with a contrast agent, respectively. The present study aimed to investigate correlations at pre-treatment between quantitative imaging metrics derived from FDG-PET/CT(SUL), FMISO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) using a community detection algorithm (CDA) based on the “spin-glass model” and Spearman rank analysis in patients with HNSCC. Correlations between MMI-derived quantitative metrics evaluated using a CDA in addition to the Spearman analysis in a larger population may enable the identification of potential biomarkers for prognostication and management of patients with HNSCC.The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a “spin-glass model” coupled with the Spearman’s rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ −0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = −0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.
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