e20083 Background: While immunohistochemistry (IHC) slides are commonly used to assess the presence of actionable biomarkers, the recent advances in artificial intelligence (AI) focus exclusively on hematoxylin and eosin (H&amp;E) slides. Nonetheless, many IHC slides, potentially rich in prognostic information, remain underexplored for risk stratification and outcome prediction. Our study aims to assess the complementarity of H&amp;E and IHC slides through the development of a multimodal prognostic AI model for lung adenocarcinoma patients. Methods: We collected 1 H&amp;E slide, 8 IHC slides (PD-L1, FOXP3, HELA2, CD3, CD4, CD8, CD163, KI67), and 2 double-stained slides (PDL1-CD163 and CD3-CD163) from 245 resected patients with lung adenocarcinoma at two different hospitals (median OS = 54.0 months [11.3 - 138.0]). Patients underwent various adjuvant treatments (chemotherapy n=92; immunotherapy n=4; other n=36; no treatment n=152). We cross-validated 11 AI models (one model per staining) to predict overall survival in these patients. We used Harell’s C-index and the 70-month cumulative AUC to evaluate the performance of our models. To assess their stratification power, each model’s predictions were split into high-risk and low-risk populations to compute a Hazard Ratio and a log-rank test p-value. Subsequently, a Cox proportional hazards model aggregated the predictions of the 11 models to compute a final risk score. We performed a grid search to find the combination of markers yielding the highest C-index. We calculated the hazard ratios to determine each biomarker's predictive value. Results: The H&amp;E model was the best unimodal model, with a C-index of 0.69 [0.67 - 0.71]. The best-performing multivariate model was obtained by combining the risk scores from univariate models in a Cox regression using H&amp;E, CD8, PD-L1, HELA2, and FOXP3 slides, reaching a C-index of 0.71 [0.69 - 0.73] (Table). We found that the predictions from H&amp;E (HR = 1.39; P = 0.03) and PD-L1 (HR = 1.17; P = 0.04) were significantly associated with survival. Conclusions: By significantly improving patient stratification with non-metastatic lung adenocarcinoma, our multivariate model proves that some IHC markers contain complementary proteomic information to H&amp;E slides. Consequently, this study paves the way for developing more comprehensive risk assessment tools from H&amp;E and IHC stainings. [Table: see text]

Multimodal risk stratification of non-metastatic lung adenocarcinoma using AI on histology and immunohistochemistry slides.