Prediction of Preoperative Lymph Node Metastasis of Esophageal Cancer by Self-coding Fusion Model Based on Multi-scale Features

Esophageal cancer is one of the eight most common malignant tumors, with high incidence and mortality rates. Most patients are already in the advanced or late stage at the time of diagnosis, missing the best treatment period and resulting in extremely poor prognosis. Factors affecting the prognosis of esophageal cancer include clinical staging, lymph node status, and pathological type. Early diagnosis and personalized treatment plans, as well as controlling the risk factors for esophageal cancer, can effectively improve the prognosis of patients. Therefore, early diagnosis and treatment of esophageal cancer have become extremely important. This paper proposes a model that comprehensively predicts lymph node metastasis status through multi-level image features. It utilizes sparse self-encoded feature fusion networks to process high-dimensional features from different levels, including machine vision features, imaging genomics features, and perceptual features. The model is constructed using statistical methods and experimentally verified for its discriminative ability, identification capability, and clinical practicality.