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　$\geq　8$　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.