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

Abstract

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.