Imaging findings include sinonasal opacification, ocular and other soft-tissue masses, osseous erosion, airway narrowing, lymphadenopathy, and salivary gland infiltration. In the head and neck, granulomatous disease may affect the orbits, sinonasal cavities, salivary glands, aerodigestive tract, temporal bone, or skull base. Infectious causes include tuberculosis, cat-scratch disease, syphilis, leprosy, actinomycosis, rhinoscleroma, and fungal infections. Granulomatous diseases with head and neck manifestations include granulomatosis with polyangiitis, Churg-Strauss syndrome, Behçet disease, chronic granulomatous disease, and sarcoidosis. Granulomatous diseases often have systemic manifestations that affect organs throughout the body. The unifying factor in these diseases is the formation of granulomas, which histologically are mononuclear inflammatory cells or macrophages surrounded by lymphocytes. Granulomatous diseases have a varied etiology that includes autoimmune, infectious, idiopathic, and hereditary causes. Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10-7 m/s), respectively. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. Values (min-1, estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and CFM simulation was performed with the finite element method in COMSOL Multiphysics software. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases.

Pathological lymph node