Radiomics nomogram: distinguishing benign and malignant pure ground-glass nodules based on dual-layer spectral detector CT.

AIM

To investigate the value of the combined model based on spectral quantitative parameters, radiomics features, imaging and clinical features to distinguish the benign and malignant pure ground-glass nodules (pGGNs).

MATERIALS AND METHODS

A retrospective analysis of 113 patients with single pGGNs who underwent non-contrast enhancement examination of the chest on dual-layer spectral detector CT (SDCT) with two weeks before surgery was performed in our hospital. These patients were randomized into training and testing cohorts. Regions of interest based on the conventional 120 kVp poly energetic image of SDCT were outlined. Then the optimal features were extracted and selected to construct radiomic model. A combined model combining vacuole sign, electron density (ED) value and the rad score of radiomics model was built by logistic regression analysis. A nomogram was built in a training cohort and the performance of the models was evaluated in the training and testing cohorts by receiver operating characteristic curves, calibration curves and decision curve analysis.

RESULTS

ED value [Odds Ratio (OR):1.100; 95% confidence interval (CI):1.027-1.166)] and vacuole sign (OR:3.343; 95% CI:0.881-12.680) were independent risk factors for the malignant pGGNs in the training cohort. A combined model was constructed using radiomics features, ED value and vacuole sign. And the AUC was 0.910 (95% CI, 0.825-0.997) and 0.850 (95% CI, 0.714-0.981) in the training and testing cohorts, respectively.

CONCLUSION

The combined model based on SDCT has high specificity and sensitivity for distinguishing the benign and malignant pGGNs, suggesting the model can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.