A Risk Model for Predicting Central Lymph Node Metastasis of Papillary Thyroid Microcarcinoma Including Conventional Ultrasound and Acoustic Radiation Force Impulse Elastography

The aim of this prospective study was to propose a new rating system using a risk model including conventional ultrasound (US) and acoustic radiation force impulse (ARFI) elastography for predicting central lymph node metastasis (LNM) in patients with papillary thyroid microcarcinoma (PTMC). A total of 252 patients with PTMCs were enrolled, who were preoperatively evaluated by US and ARFI elastography including virtual touch tissue imaging (VTI) and virtual touch tissue quantification (VTQ). Risk factors of independent variables for central LNM were analyzed by univariate and multivariate analyses. A multivariate analysis was performed to create a predicting model and rating system. Of the 252 patients, 72 (28.6%) had central LNMs. Multivariate analysis revealed that rare internal flow (odds ratio [OR]: 4.454), multiple suspicious foci on US (OR: 5.136), capsule involvement (OR: 20.632), and VTI area ratio (VAR) > 1 (OR: 5.621) were independent risk factors for central LNM. The final predicting model was obtained and the risk score (RS) was defined as 1.5 x (if rare internal flow) + 1.6 x (if multiple suspicious foci on US) + 1.7 x (if VAR > 1) + 3.0 x (if capsule involvement). The rating system was divided into 5 stages. Stage I, <1.5; Stage II, 1.5 to 3.0; Stage III, 3.1 to 4.7; Stage IV, 4.8 to 6.3; and Stage V, 6.4 to 7.8. The risk rates of central LNM were 3.4% (2/59) in Stage I, 13.3% (13/98) in Stage II, 54.2% (39/72) in Stage III, 72.2% (13/18) in Stage IV, and 100% (5/5) in Stage V (P < 0.001). The results indicated that rare internal flow, multiple suspicious foci, capsule involvement on US, and VAR > 1 on ARFI elastography are the risk factors for predicting central LNM. The risk model developed in the study clearly predicts the risk of central LNM in patients with PTMC and thus has a potential to avoid unnecessary central compartment node dissection.