MSCM-Net: Multi-scale CNN-Mamba Network for Pathological Complete Response Prediction of Lung Cancer

Accurate prediction of pathological complete response (pCR) is useful for clinical precision treatment of lung cancer. However, most existing pCR prediction methods are based on either convolutional neural networks (CNNs) or Transformers, which cannot effectively capture 3D global information or fuse features. Therefore, this study proposes a novel multi-scale CNN-Mamba network (MSCM-Net) to achieve accurate pCR prediction on CT scans of lung cancer patients. In each stage of the hybrid encoder, after channel splitting for parameter reduction, we design the CNN branch and Mamba branch to extract local and global features. Specifically, in the Mamba branch, we propose a novel intra-slice and inter-slice scanning mechanism to implement 8-way 3D scanning, thereby effectively capturing 3D global information. Furthermore, to better fuse CNN and Mamba features, we design a novel multi-scale aware feature fusion module with channel-level and multi-scale spatial level fusion. The proposed method is evaluated on a private dataset including 108 lung cancer patients who underwent neoadjuvant chemoimmunotherapy. Experimental results demonstrate that MSCM-Net achieves the best accuracy of 83.33% and an area under curve of 84.08%. Furthermore, results on the esophageal cancer and stroke prognosis prediction tasks validate the generalizability of MSCM-Net.