Early postoperative recurrence is a major cause of treatment failure in patients with locally advanced gastric cancer (LAGC), yet current staging systems inadequately capture the biological heterogeneity that underlies recurrence risk. Here, we introduce a clinically interpretable multimodal prediction model, Recurrence Stratification and Assessment (RSA), which integrates deep learning–derived histopathological features from routine hematoxylin and eosin slides with conventional clinical variables. The model was developed using a retrospective multicenter cohort (n = 1,763) and rigorously validated across two internal cohorts, two geographically distinct external cohorts, and an exploratory post-hoc analysis of a prospective clinical trial population (NCT01516944), demonstrating robust and generalizable performance (area under the curves ranging from 0.843 to 0.887). Shapley Additive Explanations-based interpretation identifies key histological features contributing to recurrence risk. To explore biological underpinnings, we perform transcriptomic sequencing and immune profiling on tumor specimens, revealing immune-enriched microenvironments and elevated checkpoint gene expression in the RSA-defined low-risk group. These findings suggest differential immunological activity may influence recurrence dynamics. This study demonstrates the application of digital pathology–based artificial intelligence for recurrence risk prediction in LAGC, offering not only a high-performance and biologically informed tool, but also a transparent framework for clinical deployment. The RSA model may support risk-adapted postoperative surveillance and provides a biologically informed framework for exploring the potential utility of immune checkpoint inhibitors.

Accurate prediction of early postoperative recurrence in locally advanced gastric cancer (LAGC) remains challenging due to tumor heterogeneity and limitations of traditional clinicopathological factors. This study aims to develop and validate an interpretable multimodal model for precise recurrence prediction. 1580 LAGC patients are enrolled from six Chinese medical centers and a multimodal fusion Risk Stratification Assessment (RSA) model integrating clinical, radiomic, and pathomic data is developed. Model performance is evaluated using internal, external, prospective, and public dataset validations. Transcriptome sequencing is conducted to elucidate biological mechanisms underlying recurrence. The RSA model significantly outperforms clinical‐only, radiomic‐only, and pathomic‐only models in predicting early recurrence, achieving area under the curve (AUC) values of 0.903 in the training cohort, 0.902 in internal validation, and ranging from 0.884 to 0.889 in external validations. Stratification by the RSA model consistently identifies high‐risk patients with significantly poorer five‐year survival across all cohorts (all P<0.001). Transcriptomic analysis reveals that high‐risk patients exhibit significant immune cell infiltration, increased expression of immune checkpoint molecules, and activation of immune‐related pathways, including interferon signaling and the IL‐6/JAK/STAT3 pathway. The integrated multimodal RSA model effectively predicts recurrence risk and prognosis in LAGC, enabling precise patient stratification and individualized postoperative management.

Gefitinib is one of the most extensively utilized epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) for treating advanced lung adenocarcinoma (LUAD) patients harboring EGFR mutation. However, the emergence of drug resistance significantly compromised the clinical efficacy of EGFR-TKIs. Gaining further insights into the molecular mechanisms underlying gefitinib resistance holds promise for developing novel strategies to overcome the resistance and improve the prognosis in LUAD patients. Here, we identified that the inhibitory efficacy of gefitinib on EGFR-mutated LUAD cells was partially dependent on the induction of ferroptosis, and ferroptosis protection resulted in gefitinib resistance. Among the ferroptosis suppressors, aldo-keto reductase family 1 member C1 (AKR1C1) exhibited significant upregulation in gefitinib-resistant strains of LUAD cells and predicted poor progression-free survival (PFS) and overall survival (OS) of LUAD patients who received first-generation EGFR-TKI treatment. Knockdown of AKR1C1 partially reversed drug resistance by re-sensitizing the LUAD cells to gefitinib-mediated ferroptosis. The decreased expression of miR-338-3p contributed to the aberrant upregulation of AKR1C1 in gefitinib-resistant LUAD cells. Furthermore, upregulated long non-coding RNA (lncRNA) nuclear paraspeckle assembly transcript 1_1 (NEAT1_1) sponged miR-338-3p to neutralize its suppression on AKR1C1. Dual-luciferase reporter assay and miRNA rescue experiment confirmed the NEAT1_1/miR-338-3p/AKR1C1 axis in EGFR-mutated LUAD cells. Gain- and loss-of-function assays demonstrated that the NEAT1_1/miR-338-3p/AKR1C1 axis promoted gefitinib resistance, proliferation, migration, and invasion in LUAD cells. This study reveals the effects of NEAT1_1/miR-338-3p/AKR1C1 axis-mediated ferroptosis defence in gefitinib resistance in LUAD. Thus, targeting NEAT1_1/miR-338-3p/AKR1C1 axis might be a novel strategy for overcoming gefitinib resistance in LUAD harboring EGFR mutation.