Prognostic analysis of oral cancer based on deep learning

TNM (tumor node metastasis) classification is a common way to evaluate the prognosis of patients with oral cancer; however, many years of application have proven this method to be confined merely in clinical and pathological data and it cannot be adapted to the development of modern medicine. Deep learning (DL) has been widely used in various aspects of human life, has advantages for conducting efficient and intelligent searches and can explore and analyze substantial medical information well. Additionally, the application of DL to medical practice is quickly increasing. In the field of oral cancer prognosis, DL can efficiently process and analyze the pathological, radiographic and molecular data of oral cancer patients represented by lymphocytes, gray level cooccurrence matrix (GLCM) and gene maps and make accurate prognostic judgments accordingly. By assisting physicians in optimizing treatment plans, DL can effectively improve patients' survival. Although DL lacks sufficient data and practical clinical application in prognostic studies, it has shown good clinical application prospects.