Yan Luo ¹ Pengcheng zheng ² Bo Wang ² Ran Duan ² Feng Tong ^3*

• ¹ The second people's Hospital of Xindu District, Chengdu, Sichuan Province, China
• ² First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan Province, China
• ³ Southern Medical University, Guangzhou, China

Disease-related malnutrition is a prevalent issue among cancer patients, affecting approximately 40-80% of those undergoing treatment. This condition is associated with numerous adverse outcomes, including extended hospitalization, increased morbidity and mortality, delayed wound healing, compromised muscle function and reduced overall quality of life. Moreover, malnutrition significantly impedes patients' tolerance of various cancer therapies, such as surgery, chemotherapy, and radiotherapy, resulting in increased adverse effects, treatment delays, postoperative complications, and higher referral rates. At present, numerous countries and regions have developed objective assessment models to predict the risk of malnutrition in cancer patients. As advanced technologies like artificial intelligence emerge, new modeling techniques offer potential advantages in accuracy over traditional methods. This article aims to provide an exhaustive overview of commonly employed and recently developed models for predicting malnutrition risk in cancer patients, offering valuable guidance for healthcare professionals during clinical decision-making and serving as a reference for the development of more efficient risk prediction models in the future.