Co-graph Attention Reasoning Based Imaging and Clinical Features Integration for Lymph Node Metastasis Prediction.

Lymph node metastasis (LNM) is the most critical prognosis factor in esophageal squamous cell carcinoma (ESCC). Effective and adaptive integration of preoperative CT images and multi-sourced non-imaging clinical factors is a challenging issue. In this work, we propose a graph-based reasoning model to learn new representations from multi-categorical clinical parameters for LNM prediction. Given CT, general, diagnostic, pathological, and hematological clinical information, we firstly propose a graph construction strategy with category-wise contextual attention to embed multi-categorical features as graph node attributes. Secondly, we introduce a co-graph attention layer composed of a conventional graph attention network (con-GAT) and a correlation-based GAT (corr-GAT) to learn new representations. Corr-GAT complements con-GAT by difference-based correlations across image regions in global spectral space. Experimental results of ablation studies and comparison with others over 924 lymph nodes demonstrated improved performance and contributions of our major innovations. Our model has the potential to foster early prognosis and personalized surgery or radiotherapy planning in ESCC patients.