Purpose 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). Methods 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. Results 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. Conclusion 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.

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