Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.

Objectives: Glioblastoma (GBM) is a highly malignant brain tumor with complex molecular mechanisms. Histopathological images provide valuable morphological information of tumors. This study aims to evaluate the predictive potential of quantitative histopathological image features (HIF) for molecular characteristics and overall survival (OS) in GBM patients by integrating HIF with multi-omics data. Methods: We included 439 GBM patients with eligible histopathological images and corresponding genetic data from The Cancer Genome Atlas (TCGA). A total of 550 image features were extracted from the histopathological images. Machine learning algorithms were employed to identify molecular characteristics, with random forest (RF) models demonstrating the best predictive performance. Predictive models for OS were constructed based on HIF using RF. Additionally, we enrolled tissue microarrays of 67 patients as an external validation set. The prognostic histopathological image features (PHIF) were identified using two machine learning algorithms, and prognosis-related gene modules were discovered through WGCNA. Results: The RF-based OS prediction model achieved significant prognostic accuracy (5-year AUC = 0.829). Prognostic models were also developed using single-omics, the integration of HIF and single-omics (HIF + genomics, HIF + transcriptomics, HIF + proteomics), and all features (multi-omics). The multi-omics model achieved the best prediction performance (1-, 3- and 5-year AUCs of 0.820, 0.926 and 0.878, respectively). Conclusion: Our study indicated a certain prognostic value of HIF, and the integrated multi-omics model may enhance the prognostic prediction of GBM, offering improved accuracy and robustness for clinical application.