Gastric Cancer

Gastric cancer research is intensely focused on improving early detection and treatment strategies, driven by its high mortality rate. Current efforts leverage machine learning, particularly deep learning models like Vision Transformers and convolutional neural networks, to analyze various data modalities including endoscopic videos, CT scans, and histopathology images, aiming to identify predictive biomarkers (e.g., gut microbiota composition) and improve diagnostic accuracy. These advancements hold significant promise for enhancing the speed and accuracy of diagnosis, potentially leading to earlier interventions and improved patient outcomes. Furthermore, research is exploring the integration of multimodal data and the development of more robust and efficient algorithms for improved treatment response prediction and survival analysis.