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Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in isocitrate dehydrogenase (IDH)-wildtype GBM patients, by combining conventional and deep learning methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. Support vector machine (SVM) classifiers were trained on the radiomic features in the discovery cohort (n = 404) to categorize patient groups of high-risk (OS

Background. Multi-omic data, i.e., clinical measures, radiomic, and genetic data, capture multi-faceted tumor characteristics, contributing to a comprehensive patient risk assessment. Here, we investigate the additive value and independent reproducibility of integrated diagnostics in prediction of overall survival (OS) in newly diagnosed, treatment-naïve, IDH-wildtype GBM patients, by combining conventional and deep learning methods.Methods. Conventional radiomics and deep learning features were extracted from pre-operative multi-parametric MRI of 516 GBM patients. SVM classifiers were trained on the discovery cohort (n=404) to categorize patient groups of high-risk (OSradiomics) was produced. Multivariate Cox-PH models were generated for the replication cohort, first based on clinical measures solely, and then by layering on radiomics and molecular information.Results. Evaluation of the high-risk and low-risk classifiers in the discovery/replication cohorts revealed AUCs of 0.78 (95%CI:0.70–0.85)/0.75 (95%CI:0.64–0.79) and 0.75 (95%CI: 0.65–0.84)/0.63 (95%CI: 0.52–0.71), respectively. Cox-PH modeling showed a concordance index of 0.65 (95%CI:0.6–0.7) for clinical data, 0.70 (95%CI:0.65–0.75) for clinical and radiomics, 0.72 (95%CI:0.68–0.77) for clinical, MGMT methylation, and radiomics, and 0.75 (95%CI:0.72–0.79) for the combination of all omics, i.e., clinical, MGMT methylation, radiomics, and genomics.Conclusions. This study signifies the value of integrated diagnostics for improved prediction of OS in GBM. Our multi-omic survival prediction tool is easily scalable and can be used for more effective clinical trial stratification.

PURPOSE Multi-omics data integration captures tumor characteristics at multiple scales [i.e., microscopic (genomics and epigenetics), macroscopic (radiomics), clinical manifestation], provides a more comprehensive assessment of patient’s risk, and facilitates personalized therapies. In this work, we investigated the synergistic value of such multiple data sources for risk stratification and prediction of overall survival in IDH-wildtype glioblastoma tumors. METHODS Quantitative conventional and deep radiomics were extracted from pre-operative multi-parametric structural MRI (T1, T1Gd, T2, T2-FLAIR) of 501 patients with newly diagnosed glioblastoma. 389/501 and 112/501 patients formed our discovery and replication cohorts, respectively. Conventional radiomics were extracted from CaPTk, and deep radiomics from a pre-trained VGG-19 model. Multivariate SVM classification was performed on the discovery cohort to stratify patients in high, medium, and low-risk groups, using recursive feature elimination and 5-fold cross-validation. This model was independently tested on the replication cohort, and a radiomic-based survival prediction index (SPIradiomics) was calculated for each patient. Multi-stage integration of omics data, i.e., clinical (age, gender, extent of resection (EOR)), SPIradiomics, epigenetics (MGMT promoter methylation), and genomics (27 clinically relevant gene mutations via next-generation sequencing (NGS)), was performed using multivariate Cox proportional hazards (Cox-PH) model for stratification of the risk in the replication cohort. RESULTS Cox-PH modeling resulted in a concordance index (c-index) of 0.65 (95% CI:0.6–0.7) for clinical data, 0.67 (95% CI:0.62–0.72) for clinical and epigenetics, 0.70 (95% CI:0.65–0.75) for clinical and radiomics, 0.72 (95% CI:0.68–0.77) for clinical, epigenetics, and radiomics, and 0.75 (95% CI:0.71 – 0.78) for the multi-omics combination of all data; highlighting the added value of each layer of information in prediction of the patient’s risk. CONCLUSION Our results reinforce the synergistic value of integrated diagnostic methods for improving risk assessment of patients with glioblastoma that may pave the path towards a more personalized treatment planning.