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

The standard treatment for stage I lung adenocarcinoma is surgical resection, in most cases without additional systemic adjuvant treatment. A significant proportion of stage I cases recur with a less than 50% 5-year survival rate. There are clinical data suggesting that adjuvant treatment may improve survival in such recurrent cases. However, previously evaluated predictors such as the IASLC grading system from histological sections and transcriptomic profiles have not been sufficiently accurate and consistent for risk stratification and to guide therapeutic interventions. We hypothesized that these previously investigated diverse diagnostic measurements carry complementary information that may provide higher prognostic power when combined. Here we describe a multimodal deep learning method, PATH-ORACLE. This biomarker is built on top of the prospectively validated transcriptomic-based ORACLE score with the addition of routine histological sections processed by pre-trained foundation models. PATH-ORACLE predicts recurrence with an accuracy of over 85% in two independent cohorts. Given further validation this predictor could be used to prioritize stage IB patients for adjuvant chemotherapy in a more consistent fashion. Furthermore, for stage IA cases, PATH-ORACLE, combined with liquid biopsy-based monitoring may help identify high-risk patients suitable for adjuvant targeted therapy. HighlightsO_LIMultimodal AI model (PATH-ORACLE) integrates histology and transcriptomics to predict stage I LUAD recurrence C_LIO_LIPATH-ORACLE outperforms IASLC grading and transcriptomic or image-based models alone C_LIO_LIModel achieves >85% recurrence prediction accuracy across independent international cohorts C_LIO_LIPATH-ORACLE refines risk stratification within both stage IA and IB lung adenocarcinoma C_LIO_LIBiomarker may guide adjuvant therapy selection and surveillance in early-stage disease C_LI