Graph convolutional network-derived pathomics and clinical integration for predicting chemoimmunotherapy response in advanced lung squamous cell carcinoma: a multicenter study.

backgroundChemoimmunotherapy (CIT) represents the standard first-line regimen for advanced lung squamous cell carcinoma (LUSC). However, treatment responses are heterogeneous, and conventional biomarkers are insufficient for precise patient stratification. This study aimed to develop and validate a deep learning-based model that integrated graph convolutional network (GCN)–derived pathomics signatures from whole-slide images (WSIs) with clinical features to predict the objective response rate (ORR) to CIT and achieve clinically meaningful risk stratification.

methodsThis retrospective study included 436 patients with pathologically confirmed LUSC and available WSIs from three centers, comprising 343 cases from the phase III AK105-302 trial and two external CIT cohorts (n = 63 and n = 30). Patch-level features were extracted using an Inception_v3-derived convolutional neural network (CNN) and aggregated at the patient level using a GCN to generate a pathomics signature. This signature was subsequently combined with clinical variables to construct the final predictive model. Model performance was assessed using the area under the curve (AUC), calibration, and decision curve analysis. Patients were stratified into high- and low-risk groups based on the Youden index derived from the combined model, and survival outcomes were evaluated using Kaplan-Meier and Cox regression analyses.

resultsThe combined pathomics–clinical model demonstrated robust discrimination in predicting ORR across the training cohort (AUC 0.964), validation cohort (AUC 0.876), and two external cohorts (AUC 0.738 and 0.846, respectively), outperforming clinical-only, ensemble, and multiple-instance learning (MIL) models. In the risk stratification analysis, low-risk patients treated with CIT exhibited a significantly higher response rate than those treated with chemotherapy alone (95.6% vs. 53.7%; p < 0.001) and achieved superior progression-free survival (PFS; hazard ratio [HR] 0.35, 95% confidence interval [CI] 0.25–0.49; p < 0.001) and overall survival (OS; HR 0.35, 95% CI 0.22–0.55; p < 0.001). A significant treatment-by-risk interaction for PFS (interaction p = 0.019) and OS (interaction p < 0.001) indicated that the benefit of CIT was concentrated in the low-risk group. These findings were validated in both external CIT cohorts, while model performance and risk stratification were less effective in the chemotherapy-only and non-lung-origin cohorts.

conclusionsThe GCN-based pathomics–clinical model accurately predicted ORR to CIT and demonstrated robust, externally validated performance, suggesting its potential utility for response enrichment and risk stratification in advanced LUSC.