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

Abstract

Background 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. Methods 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. Results 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). Conclusions 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.

Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study

“…Firstly, the CT thickness utilized was 5 mm due to the extended time span. Despite previous studies indicating that different CT section thicknesses do not affect the discriminatory ability of radiomics models [ 29 , 30 ], prospective studies could be conducted in the future, with a focus on radiomics analysis using thin-slice CT scans. However, on the premise of ensuring the stability and accuracy of the model, we chose 5 mm CT thickness, which is consistent with other reports [ [31] , [32] , [33] , [34] ], and can reduce the workload of tumor delineation and improve operability.…”

Study on radiomics to predict the aggressiveness of multiple primary invasive lung adenocarcinomas

“…Aerts et al 23 demonstrated that radiomics could differentiate the prognosis of different pathological subtypes of lung adenocarcinomas. Wang et al 24 showed that chest contrast-enhanced CT imaging could be used to identify lung adenocarcinoma. In this study, the models for the prediction of multiple primary invasive lung adenocarcinomas were established based on CT radiomics.…”