This article discusses the use of a random forest model to predict the depth of invasion in early gastric cancer. The model uses color metrics measured in real-time from endoscopic images to improve diagnostic accuracy. Results show that lesions with a larger color difference from surrounding tissues have a higher risk of deeper infiltration. Other predictive features include lesion length and location in the upper portion of the stomach.
