[Predicting marker genes and postoperative outcomes in nasal polyps using an artificial intelligence model based on digital pathology and transcriptomics].

Objective: To evaluate HE2Signature for predicting inflammatory gene expression and postoperative outcomes in chronic rhinosinusitis with nasal polyps (CRSwNP) directly from whole slide images (WSIs). Methods: In an independent external cohort of 178 CRSwNP patients, HE2Signature was employed to analyze WSIs to predict expression of 33 inflammatory marker genes. Post-operative control was assessed (EPOS 2020). Predicted gene expression was correlated with clinical indicators and compared across control groups. LASSO regression was used to build a prediction model for post-operative outcome, compared with ControlNet. Results: The predicted expression levels of 18 of the 33 marker genes showed significant correlations with clinical indicators (P<0.05). The predicted expression of type 2 genes (e.g., POSTN, FCER2, IL-13) were significantly and positively correlated with eosinophil-related metrics and disease burden (r=0.160-0.244). Conversely, predicted expression of non-type 2 genes (e.g., CSF3, SAA1) was positively associated with tissue neutrophil counts. The uncontrolled disease group was characterized by a significant upregulation of predicted Type 2 inflammatory genes. A model based on the predicted gene signature achieved an area under the receiver operating characteristic curve (AUC) of 0.776 for discriminating uncontrolled status and 0.800 for discriminating control status. This performance was not statistically different from that of the ControlNet model (P>0.05). Conclusion: The HE2Signature model effectively predicts marker gene expression and postoperative outcomes in CRSwNP from routine histology, offering a scalable and intelligent pathway for precision medicine without requiring molecular assays.