Abstract
This study was aimed at differentiating brain metastases (BMs) from non-small cell lung cancer (NSCLC) vs. small cell lung cancer (SCLC), and the adenocarcinoma (AD) vs. non-adenocarcinoma (NAD) subtypes, according to radiomics features derived from multiparametric magnetic resonance imaging (MRI). A total of 276 patients with BMs, including 98 with SCLC and 178 with NSCLC, were randomly divided into training (193 cases) and test (83 cases) datasets in a 7:3 ratio. Of the 178 patients with NSCLC, 155 had primary AD, and 23 had NAD; those patients were also randomly divided into training (124 cases) and test (54 cases) datasets. Logistic regression analysis was used to construct classification models based on the radiomics features extracted from contrast-enhanced T1-weighted imaging (T1CE), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion-weighted imaging (DWI) images. Diagnostic efficiency was evaluated with the area under the receiver operating characteristic curve (AUC) through Delong's test, calibration curves through the Hosmer-Lemeshow test and Brier score, precision-recall curves, and decision curve analysis. Compared with radiomics features derived from a single sequence, multiparametric combined-sequence MRI radiomics features based on T1CE, T2-FLAIR, and DWI images exhibited greater specificity in distinguishing BMs originating from various lung cancer subtypes. In the training and test datasets, the AUCs of the model for the classification of SCLC and NSCLC BMs were 0.765 (95% CI 0.711, 0.822) and 0.762 (95% CI 0.671, 0.845), respectively, whereas the AUCs of the prediction models combining the three sequences in differentiating AD from NAD BMs were 0.861 (95% CI 0.756, 0.951) and 0.851 (95% CI 0.649, 0.984), respectively. The radiomics classification method based on the combination of multiple MRI sequences can be used for differentiating various lung cancer BMs.