Using MRI Radiomics to Predict the Efficacy of Immunotherapy for Brain Metastasis in Patients with Non-small Cell Lung Cancer

Abstract

Despite immune checkpoint inhibitors (ICIs) has been proven effective in patients with advanced NSCLC, controversial therapeutic responses are seen in brain metastatic lesions. There are no robust biomarkers that predict benefit from this regimen. We evaluated the utility of novel imaging biomarkers (radiomics) to distinguish NSCLC patients with brain metastases (BMs) who will benefit from ICIs from those likely to progress despite therapy. One-hundred and seventy-four NSCLC Patients with BMs treated with ICIs from June 2019 - June 2022 were identified. We collected patient clinical outcomes and pre-treatment MRI images. Images were split into training and test sets. Brain metastatic lesions were contoured on ITK-SNAP software and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. The primary endpoint of this study was intracranial progression-free survival (iPFS) and the secondary objective were progression-free survival (PFS) and overall survival (OS). We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to build the radiomic signature for ORR. Based on the support vector machine (SVM) model, we construct the Clinical-radiomics nomogram (CRN). Multivariable Cox regression analysis were performed to evaluate the effect of each factor on iPFS. We performed Kaplan-Meier survival analysis and log-rank tests to assess prognostic value of the features. We identified 174 patients who fit our criteria with available MRI images. 122 patients treated in our center were divided into a training set and 52 patients treated in another center were divided into a test set. The intratumoral radiomic signatures(IRS), peritumoral radiomic signatures(PRS) and CRN showed favorable predictive effects for ORR with the area under the receiver operating curve (AUC) of (IRS: 0.845 (95% CI: 0.776-0.914); PRS: 0.799 (95% CI:0.720-0.879); CRN:0.907 (95% CI: 0.855-0.959))in the training set and (IRS: 0.809 (95% CI: 0.693-0.926); PRS: 0.749 (95% CI:0.616-0.883); CRN:0.888(95% CI:0.798-0.979)) in the test set. Kaplan-Meier analyses showed a significantly longer iPFS in the high-CRN group versus the low-CRN group (P < 0.001). The CRNs were also found significantly associated with PFS (P < 0.001), but not OS. Radiomic biomarkers from pre-treatment MRI images in NSCLC patients with BMs were predictive of iPFS to ICIs. Pre-treatment radiomics may allow early prediction of benefit and expedite more aggressive treatment for high-risk patients. Additional validation of these imaging biomarkers is warranted.