Machine learning in predicting gastric cancer survival Presenting a novel decision support system model.

Background: Gastric cancer is the 4th most frequent cause of cancer-related deaths, with a 5-year survival rate of less than 40%. In recent years, many artificial intelligence applications have been used in the field of gastric cancer through their effective computing and learning ability. In this study, we aim to develop a software that can accurately detect overall survival in gastric cancer cases with the help of artificial intelligence and machine learning.

Preventive role of superoxide dismutase on radiation-induced periprosthetic capsule development.

Background: Capsule fibrosis is the most important and annoying complication of breast implant surgery. Radiotherapy (RT) used in the local treatment of breast cancer has an increasing effect on the existing fibrous capsule; this is called radiation-induced fibrosis (RIF). In this randomized controlled experimental study, we aim to investigate the reduction effect of superoxide dismutase (SOD) on RIF.