A Radiogenomic Deep Ensemble Learning Model for Identifying Radionecrosis Following Brain Metastases (BM) Stereotactic Radiosurgery in Patients With Non-small Cell Lung Cancer BM.

Purpose: Stereotactic radiosurgery (SRS) is widely used for brain metastases (BM), but the risk of radionecrosis poses a challenge in post-SRS management. Given the lack of noninvasive imaging methods for distinguishing radionecrosis from recurrence, we aimed to design a deep ensemble learning model that integrates patient clinical features and genomic profiles to identify radionecrosis in patients with BM with post-SRS radiographic progression. Methods and Materials: We studied 90 BMs from 62 patients with non-small cell lung cancer, with 27 biopsy-confirmed post-SRS local recurrences. Clinical features and molecular features were collected. A deep neural network (DNN) was trained for radionecrosis/recurrence prediction using the 3-month post-SRS T1+c magnetic resonance imaging. Preceding the binary prediction output, latent variables were extracted as 1024 deep features. An ensemble learning model was then developed, comprising 2 submodels that fused deep features with clinical (" Results: The deep ensemble model showed satisfactory performance on the test set, with the area under the receiver operating characteristic curve (ROC Conclusions: The deep ensemble model achieved the best performance among the models evaluated in this study for distinguishing BM radionecrosis from recurrence using 3-month post-SRS T1+c MR images, clinical features, and genomic features. This highlights the potential of artificial intelligence in clinical decision-making for BM management, warranting further investigation into its clinical applications.