Lung adenocarcinoma (LUAD) has a heterogeneous prognosis and controversial postoperative treatment protocols. We aim to develop and validate a multimodal analysis framework that integrates CT images with H&E-stained whole-slide images (WSIs) to enhance risk stratification and predict adjuvant chemotherapy benefit in LUAD patients. We retrospectively collected data from 1039 resectable LUAD patients (stage I-III) across four centres, forming a training dataset (n = 303), two testing datasets (n = 197 and n = 228) for survival analysis, and a feature testing dataset (n = 311) for interpretability analysis. We extracted 487 tumour/peritumour radiomics features from CT images and 783 multiscale pathomics features from WSIs, characterising the shape of tumour (CT) and cancer nuclei (WSIs), as well as the intensity and texture of tumour/peritumour regions (CT) and tumour regions/epithelium/stroma (WSIs). A survival support vector machine (SVM) was employed to establish a radiopathomics signature using the optimal set of multimodal features, including 2 tumour radiomics features, 3 peritumour radiomics features, and 4 nuclei heterogeneity pathomics features. The radiopathomics signature outperformed both radiomics and pathomics signatures in predicting disease-free survival (DFS) (C-index: training dataset, 0.744 vs. 0.734 and 0.692
testing dataset 1, 0.719 vs. 0.701 and 0.638
testing dataset 2, 0.711 vs. 0.689 and 0.684), demonstrating greater robustness compared to the state-of-the-art deep learning integration approaches. It provided additional prognostic information beyond clinical risk factors (C-index of clinical plus radiopathomics vs. clinical models: training dataset, 0.763 vs. 0.676
testing dataset 1, 0.739 vs. 0.676
testing dataset 2, 0.711 vs. 0.699, p <
0.001). Compared to low-risk patients categorised by the radiopathomics signature, high-risk patients achieved comparable DFS when receiving adjuvant chemotherapy (training dataset, HR = 1.53, 95 % CI 0.85-2.73, p = 0.153
testing dataset 1 and 2, HR = 1.62, 95 % CI 0.92-2.85, p = 0.096), but had significantly worse DFS when only observed after surgery (training dataset, HR = 4.46, 95 % CI 2.82-7.05, p <
0.001
testing datasets 1 and 2, HR = 3.52, 95 % CI 2.26-5.49, p <
0.001), indicating the predictive value of the radiopathomics signature for adjuvant chemotherapy benefit (interaction p <
0.05). Further interpretability analysis revealed that the radiopathomics signature was associated with various prognostic/treatment-related biomarkers, including differentiation, immune phenotypes, and EGFR status. The multimodal integration framework offered a cost-effective approach for LUAD characterisation by leveraging complementary information from radiological and histopathological imaging. The radiopathomics signature demonstrated robust prognostic capabilities, providing valuable insights for postoperative treatment decisions.