Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast-enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma.

A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast-enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance.

Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast-enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05).

The radiomic analysis of contrast-enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast-enhanced scan phase did not affect the discriminating ability in the radiomics models.