Predictive Value of Arterial Enhancement Fraction Derived from Dual-Layer Spectral Computed Tomography for Thyroid Microcarcinoma

<b>Background/Objectives:</b> Accurately distinguishing malignancy in thyroid micronodules (≤10 mm) is crucial for clinical management, yet it is challenging due to the limitations of conventional ultrasonography-guided biopsy. This study aims to evaluate the predictive value of dual-layer spectral computed tomography (DSCT)-derived arterial enhancement fraction (AEF) in diagnosing thyroid microcarcinomas. <b>Methods:</b> In the study, 321 pathologically confirmed thyroid micronodules (benign = 131, malignant = 190) from Chongqing General Hospital underwent preoperative DSCT. Quantitative parameters of DSCT, including the normalized iodine concentration (NIC), normalized effective atomic number (NZ_eff), and slope of the spectral Hounsfield unit curve (<i>λ</i>HU_(40-100)), were assessed. Both single-energy CT (SECT)-derived AEF (AEF_S) and DSCT-derived AEF (AEF_D) were calculated. Conventional image features included microcalcifications and enhancement blurring. Correlation between AEF_D and AEF_S was determined using Spearman's correlation coefficient. Diagnostic performance was evaluated by calculating the area under the curve (AUC) using receiver operating characteristic (ROC) analysis. <b>Results:</b> Malignant micronodules exhibited significantly lower AEF_D (0.958 vs. 1.259, <i>p</i> < 0.001) and AEF_S (0.964 vs. 1.436, <i>p</i> < 0.001) versus benign nodules. Arterial phase parameters-AP<i>λ</i>HU_(40-100), APNIC, APNZ_eff-differed significantly between groups (all <i>p</i> < 0.001), whereas venous phase parameters (VP<i>λ</i>HU_(40-100), VPNIC, VPNZ_eff) showed no differences (all <i>p</i> > 0.05). Multivariate analysis revealed that <i>λ</i>HU_(40-100) as an independent predictor of malignancy, with an odds ratio (OR) of 0.600 (95% confidence interval (CI): 0.437-0.823; <i>p</i> = 0.002) and an AUC of 0.752 (95% CI: 0.698-0.806). A significant positive correlation was identified between AEF_D and AEF_S (<i>r</i> = 0.710; <i>p</i> < 0.001). For diagnosing malignancy, AEF_D demonstrated superior overall performance (AUC: 0.794; sensitivity: 70.5%; specificity: 81.7%; accuracy: 75.1%) to AEF_S (0.753; 71.1%; 74.0%; 72.3%), AP<i>λ</i>HU_(40-100) (0.752; 68.9%; 75.6%; 71.7%), and calcification (0.573; 21.6%; 92.4%; 50.5%). Clinically, AEF_D reduced the unnecessary biopsy rate to 18.3%, preventing 107 procedures in our cohort. <b>Conclusions:</b> AEF_D and AEF_S demonstrated strong correlation and comparable diagnostic performance in the evaluation of thyroid micronodules. Furthermore, AEF_D showed favorable diagnostic efficacy compared to both spectral parameters and conventional imaging feature. More importantly, the application of AEF_D significantly reduced unnecessary biopsy rates, highlighting its clinical value in optimizing patient management.