Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification

The present study highlights the importance of considering multiple variables and their interactions to predict the prognosis of mortality and stage in GC patients through ML-based techniques. These findings suggest that the incorporation of molecular biomarkers may enhance patient prognosis compared to relying solely on clinical factors. Furthermore, they demonstrate the potential for personalized medicine in GC treatment by identifying high-risk patients for early intervention and optimizing therapeutic strategies. The partition analysis technique offers a practical tool for identifying cutoffs and prioritizing markers for clinical application. Additionally, providing Clinical Decision Support systems with predictive tools can assist clinicians and pathologists in identifying aggressive cases, thereby improving patient outcomes while minimizing unnecessary treatments. Overall, this study contributes to the ongoing efforts to improve patient outcomes by advancing our comprehension of the intricate nature of GC.