Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer - Wang, Hua; Chen, Yong-Zi; Li, Wan-Hu; Han, Ying; Li, Qi; Ye, Zhaoxiang (Frontiers in Genetics, 2022)
Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC). Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who diagnosed BM at baseline examination (n=16) or within one year’s follow-up (n=14), and BM- were those without BM followed up for at least one year (n=47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model. Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736 - 0.921), and while combining with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845 - 0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram. Conclusions: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within one year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients.