Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas

SIMPLE SUMMARY: Integration 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 (K(1), k(3), TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [K(trans), v(e), and τ(i)]) 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. ABSTRACT: The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including (18)F-FDG-PET/CT, (18)F-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 (K(1), k(3), TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [K(trans), v(e), and τ(i)]) were investigated using the CDA based on a “spin-glass model” coupled with the Spearman’s rank, ρ, analysis. Mean MRI T(2) weighted tumor volumes and SUL(mean) values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SUL(mean) (ρ ≤ −0.42, p < 0.03 for both). K(1) and K(trans) were positively correlated (ρ = 0.48, p = 0.01). In contrast, K(trans) and k(3max) were negatively correlated (ρ = −0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, K(trans), and K(1) 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.