A multicenter study reported an MRI-radiomics and machine-learning approach that predicts lymphovascular invasion and stratifies gastric cancer patients by disease-free survival risk before surgery. Separately, other AI imaging work in the corpus continued to push pre-procedure discrimination, with models integrating structured image features and clinical variables. In gastric cancer, lymphovascular invasion is a key prognostic factor and can influence staging and treatment intensity. The approach described combines MRI radiomics with clinical inputs to produce a before-operative signal, aiming to refine decisions earlier in the care pathway. These advances reflect a wider push toward interpretable, decision-ready imaging analytics that may reduce uncertainty at the point of surgical planning.