Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction.

Accurate prediction of survival rates in esophageal cancer (EC) is crucial for guiding personalized treatment decisions. Deep learning-based survival models have gained increasing attention due to their powerful ability to capture complex embeddings in medical data. However, the primary limitation of current frameworks for predicting survival lies in their lack of attention to the contextual interactions between tumor and lymph node regions, which are vital for survival predictions. In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. Moreover, we found that retaining lymph nodes with major axis [Formula: see text]mm yields the best predictive results (C-index: 0.725), offering valuable guidance on choosing prognostic factors for esophageal cancer.For EC survival prediction using solely 3D CT images, integrating lymph node information with tumor information helps to improve the predictive performance of deep learning models.Clinical impact: The American Joint Committee on Cancer (TNM) classification serves as the primary framework for risk stratification, prognostic evaluation, and therapeutic decision-making in oncology. Nevertheless, this prognostic tool has demonstrated limited predictive accuracy in assessing long-term survival for esophageal carcinoma patients undergoing multimodal therapeutic regimens. Notably, even among those categorized within identical staging parameters, significant outcome heterogeneity persists, with survival trajectories diverging substantially across clinically matched populations. Our model serves as a complementary tool to the TNM staging system. By stratifying patients into distinct risk categories, this approach enables accurate prognosis assessment and provides critical guidance for postoperative adjuvant therapy decisions (such as whether to administer adjuvant radiotherapy or chemotherapy), thereby facilitating personalized treatment recommendations.