Added value of quantitative parameters derived from dual-layer spectral detector computed tomography for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid cancer

Objective To investigate the added value of quantitative parameters derived from dual-layer spectral detector computed tomography (SDCT) for diagnosing metastatic cervical lymph nodes (LNs) in patients with papillary thyroid cancer (PTC). Methods A total of 219 cervical LNs (121 non-metastatic and 98 metastatic) were enrolled from 73 patients with PTC. Conventional CT image features including enlarged size, abnormal enhancement, calcification, cystic change and extranodal extension were evaluated. SDCT-derived quantitative parameters including normalized iodine concentration (NIC), effective atomic number (Z eff-c ) and slope of energy spectrum curve (λ HU ) in both arterial phase and venous phase were measured and calculated. The χ 2 or Fisher's precision probability test was used to compare qualitative CT image features. Mann–Whitney U test was used to compare quantitative parameters. Multivariate logistic regression analysis was applied to build three models based on conventional features (model 1), quantitative parameters (model 2) and their combination (model 3). ROC curves analysis was used to assess and compare the diagnostic performances. Results Metastatic LNs demonstrated significantly higher NIC, Z eff-c , and λ HU in both arterial phase and venous phase than non-metastatic LNs (all P < 0.001). Model 1 = − 1.477 + 1.902 × abnormal enhancement + 2.414 × calcification. Model 2 = − 4.818 + 10.951 × arterial phase NIC + 0.836 × arterial phase λ HU . Model 3 = − 4.991 + 0.562 × abnormal enhancement + 2.380 × calcification + 10.624 × arterial phase NIC + 0.779 × arterial phase λ HU . Model 3 showed the best performance (AUC = 0.958), followed by model 2 (AUC = 0.954). Both these two models overperformed model 1 (AUC = 0.740) (both P < 0.001). Conclusion Compared with conventional CT image features alone, adding quantitative parameters derived from SDCT could improve the performance in diagnosing metastatic cervical LNs in patients with PTC.