Predicting the spread through air spaces in lung adenocarcinoma from preoperative 18F-FDG PET/CT radiomics

Objective This study aimed to develop an effective radiomics-clinical model to preoperatively discriminate the spread through air spaces (STAS) in lung adenocarcinoma (ADC). Methods Data from 192 ADC patients were enrolled, with 2/3 ( n = 128) allocated as the training cohort and the remaining 1/3 ( n = 64) designated as the validation cohort. A total of 2212 radiomics features were extracted from PET/computed tomography (PET/CT) images. The least absolute shrinkage and selection operator regression method was applied to select features. Logistic regression was used to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve analysis. Results The area under the ROC curve (AUC) of the radiomics-clinical model was 0.924 (95% confidence interval, 0.878–0.969) in the training cohort and 0.919 (0.833–1.000) in the validation cohort. The AUC of the radiomics model was 0.885 (0.825–0.945) in the training cohort and 0.877 (0.766–0.988) in the validation cohort. The AUC of the clinical model was 0.883 (0.814–0.951) in the training cohort and 0.896 (0.7706–1.000) in the validation cohort. The decision curve analysis indicated its clinical usefulness. Conclusion The PET/CT-based radiomics-clinical model achieved satisfactory performance in discriminating the STAS in ADC preoperatively.