Study on the Nomogram Model Based on Dual-energy Computed Tomography for Predicting Lymph Node Metastasis in Papillary Thyroid Carcinoma

Objective: This study aimed to evaluate the effectiveness of a nomogram model based on dual-energy computed tomography (CT) quantitative parameters combined with clinical information for distinguishing metastatic from non-metastatic lymph nodes in patients with papillary thyroid carcinoma (PTC). Methods: This retrospective study was conducted in 103 patients with PTC from 2021 to 2023. Each patient underwent preoperative neck dual-energy-enhanced CT, and postoperative pathological results revealed 105 lymph nodes as metastatic and 136 as non-metastatic. Parameters, such as iodine concentration (IC), in the arterial phase, lymph node shape, short diameter, margin, degree of enhancement, cystic changes, calcification, arterial phase enhancement pattern, and thyroid nodule capsule integrity were analyzed. Univariate and multivariate logistic regression analyses identified independent risk factors for lymph node metastasis that were incorporated into the nomogram. The model performance was evaluated using a calibration curve. Results: Multivariate logistic regression analysis showed IC in the arterial phase ≥ 2.6 mg/mL, significant arterial phase enhancement, uneven arterial phase enhancement pattern, diameter ≥10 mm, unclear margins, irregular shape, and incomplete thyroid nodule capsule as independent risk factors for lymph node metastasis. The nomogram achieved an area under the curve of 0.996 (95% confidence interval (CI); 0.993~0.996) with a cutoff value of 0.09, sensitivity of 99%, and specificity of 95%. Conclusion: The dual-energy CT-based nomogram demonstrated a significant clinical value in the preoperative assessment of lymph node metastasis in patients with PTC, potentially assisting in the formulation of individualized surgical plans.