A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma

Background

Surgical resection is the standard treatment for stage I lung adenocarcinoma, in most cases without additional systemic adjuvant treatment. A substantial proportion of stage I cases recur, with a 5-year survival rate <50%. Clinical data suggest that adjuvant treatment, including immune checkpoint inhibitor therapy, may improve survival in such recurrent cases. Previously evaluated predictors, including the International Association for the Study of Lung Cancer (IASLC) grading system applied to histological sections and transcriptomic profiles, have not been sufficiently accurate or consistent for risk stratification or to guide therapeutic intervention. We hypothesized that these diverse diagnostic measurements carry complementary information that may provide higher prognostic power when combined.