Multisequence MRI-based radiomics analysis for early prediction of the risk of T790M resistance in new brain metastases.

Abstract

Predicting whether T790M emerges early is crucial to the adjustment of targeted drugs for non-small cell lung cancer (NSCLC) patients. This study aimed to evaluate the risk of T790M resistance in progressive new brain metastases (BMs) based on multisequence magnetic resonance imaging (MRI) radiomics. This retrospective study included 405 consecutive patients (training cohort: 294 patients; testing cohort: 111 patients) with proven NSCLC with disease progression of new BM. The radiomics features were separately extracted from T2-weighted imaging (T2WI), T2 fluid-attenuated inversion recovery (T2-FLAIR), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1-CE) sequence of baseline MRI. Then, we calculated radiomics scores (rad-score) of the 4 sequences respectively and established predictive models (lesion- or patient-level) to evaluate T790M resistance within up to 14 months using random forest classifier. Receiver operating characteristic (ROC) curves and F1 scores were used to validate the performance of two models in both the training and testing cohort. There were significant differences in rad-scores of the four sequences between T790M-positive and negative groups whether in the training or testing cohort (P<0.05). The lesion-level model consisting of rad-scores showed excellent discrimination, with an area under the curve (AUC) and F1-score of 0.879 and 0.798 in the training cohort, and 0.834 and 0.742 in the testing cohort, respectively. The patient-level model also showed a favorable discriminatory ability with an AUC and F1 score of 0.851 and 0.837, which was confirmed with an AUC and F1 score of 0.734 and 0.716 in the testing cohort. The MRI-based radiomics signatures may be new markers to identify patients at high risk of developing resistance in the early period.