使用双层频谱计算机断层扫描仪（DSCT）定量参数与典型影像学特征结合的模型来区分良性微小结节和甲状腺微小癌（TMC），并分析其预测效果。研究回顾342例进行DSCT检查的甲状腺微小结节（≤1cm）患者的资料（良性组：n = 170；恶性组：n = 172），测量并比较了典型影像学特征，包括微小钙化和增强模糊，以及DSCT定量参数，包括虚拟单能量图像（40 keV、70 keV和100 keV）的衰减、光谱HU曲线斜率（λHU）、归一化碘浓度（NIC）和动脉期（AP）和静脉期（VP）的归一化有效原子数（NZeff），在良性组和恶性组之间。利用多元Logistic回归（LR）分析比较显著的定量DSCT参数或结合DSCT参数以及典型影像学特征的模型的诊断性能，使用具有最高诊断性能的预测因素开发了一个诊断图表。 DSCT参数APλHU在识别TMC患者方面具有最大的诊断效率，ROC曲线下面积（AUC）为0.829，灵敏度和特异度分别为0.738和0.753。然后，将APλHU与两个影像学特征组合在一起构建DSCT-影像学诊断图表，AUC为0.858，灵敏度为0.791，特异度为0.800。诊断图表的校准曲线表明预测结果与实际观察结果相符合。决策曲线显示，与所有/无干预策略相比，诊断图表可以在所有阈值概率下产生更大的净收益。作为一种有效的可视化无创预测工具，DSCT-影像学诊断图表结合DSCT定量参数和影像学特征，显示出良好的预测效能，可用于识别良性和恶性甲状腺微小结节。©2023.作者们。

To analyse the predictive effect of a nomogram combining dual-layer spectral computed tomography (DSCT) quantitative parameters with typical radiological features in distinguishing benign micro-nodule from thyroid microcarcinoma (TMC).Data from 342 instances with thyroid micro-nodules (≤1 cm) who underwent DSCT (benign group: n = 170; malignant group: n = 172) were reviewed. Typical radiological features including micro-calcification and enhanced blurring, and DSCT quantitative parameters including attenuation on virtual monoenergetic images (40 keV, 70 keV and 100 keV), the slope of the spectral HU curve (λHU), normalized iodine concentration (NIC), and normalized effective atomic number (NZeff) in the arterial phase (AP) and venous phase (VP), were measured and compared between the benign and malignant groups. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of significant quantitative DSCT parameters or the models combining DSCT parameters respectively and typical radiological features based on multivariate logistic regression (LR) analysis. A nomogram was developed using predictors with the highest diagnostic performance in the above model, as determined by multivariate LR analysis.The DSCT parameter APλHU showed the greatest diagnostic efficiency in identifying patients with TMC, with an area under the ROC curve (AUC) of 0.829, a sensitivity and specificity of 0.738 and 0.753, respectively. Then, APλHU was combined with the two radiological features to construct the DSCT-Radiological nomogram, which had an AUC of 0.858, a sensitivity of 0.791 and a specificity of 0.800. The calibration curve of the nomogram demonstrated that the prediction result was in good agreement with the actual observation. The decision curve revealed that the nomogram can result in a greater net benefit than the all/none-intervention strategy for all threshold probabilities.As a valid and visual noninvasive prediction tool, the DSCT-Radiological nomogram incorporating DSCT quantitative parameters and radiological features shows favourable predictive efficiency for identifying benign and malignant thyroid micro-nodules.© 2023. The Author(s).