Fig. 2.

Illustration of the newly proposed algorithm for predicting EGFR mutation status of brain metastases from NSCLC based on magnetic resonance imaging (MRI). a Targeted lesions were annotated on one sequence and registered to other sequences. b Radiomics features were extracted and standardized for each type of sequence. c Multisequential radiomics features were fused based on the attention mechanism. d Graph convolutional network (GCN) learned, and output predicted classes on lesion-wise which were further used for patient-wise classification. DWI Diffusion-weighted imaging, EGFR Epidermal growth factor receptor, FLAIR Fluid-attenuated inversion-recovery, NSCLC Non-small cell lung cancer, ROI Region of interest, Tanh Hyperbolic tangent function, T1-CE T1-weighted contrast-enhanced sequence, w1-4 Weight 1–4, WT Wild-type