A multimodal fusion network based on variational autoencoder for distinguishing SCLC brain metastases from NSCLC brain metastases

In this study, we introduce the MFN-VAE, a new multimodal fusion network for differentiating small cell from non-small cell lung cancer brain metastases. Tested on a private dataset, MFN-VAE demonstrated high accuracy (ACC: 0.888; AUC: 0.920), effectively distinguishing between small cell lung cancer brain metastases (SCLC) and non-small cell lung cancer (NSCLC). The SHapley Additive explanation (SHAP) method was used to enhance model interpretability, providing clinicians with a reliable diagnostic tool. Overall, MFN-VAE shows great potential in improving the diagnosis and treatment of lung cancer brain metastases.