GBMM: A Graph Neural Network-based Multimodal Mamba Model for Esophageal Cancer Prognosis Prediction

GBMM (Graph-based Multimodal Mamba) is a multimodal deep learning model that integrates graph neural networks (GNN) and the Mamba architecture. It is specifically designed to predict the prognosis of neoadjuvant immunochemotherapy for esophageal cancer, demonstrating the application potential of AI in precision medicine. The original project is maintained by PengPeixi, released on June 2, 2026, and sourced from GitHub.

Esophageal cancer is a common malignant tumor worldwide. Neoadjuvant immunochemotherapy is an important treatment for locally advanced patients, but patient responses vary widely (good/limited/severe side effects). The core problem is how to predict prognosis early to adjust strategies personalizedly. Traditional methods rely on experience and limited biomarkers, making it difficult to utilize the complex correlations in multimodal data.

GBMM combines three technologies: GNN (modeling multimodal feature relationships, using GAT to construct patient-feature, feature-feature, and time-series graphs), Mamba (efficiently processing long-sequence medical data), and multimodal fusion (hierarchical fusion: intra-modal, inter-modal, and decision fusion). The technical architecture includes a graph neural network layer, a Mamba sequence modeling layer, and a hierarchical fusion strategy.

The evaluation metrics for GBMM include prediction accuracy (AUC-ROC, AUC-PR, etc.), calibration, clinical utility (Decision Curve Analysis, DCA), and fairness. Expected performance: AUC-ROC > 0.85, sensitivity > 80%, specificity >75%.

Application scenarios: pre-treatment risk assessment, dynamic monitoring during treatment, patient screening for clinical trials, personalized treatment recommendations. Model advantages: handling multimodal heterogeneous data (imaging, pathology, genetics, etc.), interpretability design (feature importance, attention visualization, etc.), uncertainty quantification (point estimation, confidence intervals, risk stratification).

Challenge 1: Data scarcity → Pre-training + fine-tuning, self-supervised learning, transfer learning, data augmentation; Challenge 2: Class imbalance → Focal Loss, resampling, cost-sensitive learning; Challenge3: Multicenter data heterogeneity → Federated learning, domain adaptation, standardized preprocessing; Challenge4: Real-time requirements → Mamba efficient inference, model quantization, edge deployment.

GBMM is a cutting-edge direction in medical AI, combining GNN, Mamba, and multimodal learning to solve clinical problems and promote precision medicine. Limitations: single cancer type application, reliance on high-quality data, high model complexity. Future directions: multi-cancer expansion, federated learning, continuous learning, causal inference, multilingual support. Ethical privacy considerations are needed (data desensitization, algorithm fairness, human-machine collaboration).