Prediction of Core Signaling Pathway by Using Diffusion- and Perfusion-based MRI Radiomics and Next-generation Sequencing in Isocitrate Dehydrogenase Wild-type Glioblastoma.

Background Next-generation sequencing (NGS) enables highly sensitive cancer genomics analysis, but its clinical implications for therapeutic options from imaging-based prediction have been limited. Purpose To predict core signaling pathways in isocitrate dehydrogenase (IDH) wild-type glioblastoma by using diffusion and perfusion MRI radiomics and NGS. Materials and Methods The radiogenomics model was developed by using retrospective patients with glioma who underwent NGS and anatomic, diffusion-, and perfusion-weighted imaging between March 2017 and February 2019. For testing model performance in predicting core signaling pathway, patients with IDH wild-type glioblastoma from a retrospective analysis from a registry (ClinicalTrials.gov NCT02619890) were evaluated. Radiogenomic feature selection was performed by using t tests, least absolute shrinkage and selection operator penalization, and random forest. Combining radiogenomic features, age, and location, the performance of predicting receptor tyrosine kinase (RTK), tumor protein p53 (P53), and retinoblastoma 1 pathways was evaluated by using the area under the receiver operating characteristic curve (AUC). Results There were 120 patients (52 years ± 13 [standard deviation]; 61 women) who were evaluated. Eighty-five patients (51 years ± 13; 43 men) were in the training set and 35 patients with IDH wild-type glioblastoma (56 years ± 12; 19 women) were in the validation set. Radiogenomics model identified 71 features in the RTK, 17 features in P53, and 35 features in the retinoblastoma pathway. The combined model showed better performance than anatomic imaging-based prediction in the RTK (P = .03) and retinoblastoma (P = .03) and perfusion imaging-based prediction in the P53 pathway (P = .04) in the training set. AUC values of the combined model for the prediction of core signaling pathways were 0.88 (95% confidence interval [CI]: 0.74, 1) for RTK, 0.76 (95% CI: 0.59, 0.92) for P53, and 0.81 (95% CI: 0.64, 0.97) for retinoblastoma in the validation set. Conclusion A diffusion- and perfusion-weighted MRI radiomics model can help characterize core signaling pathways and potentially guide targeted therapy for isocitrate dehydrogenase wild-type glioblastoma. © RSNA, 2019 Online supplemental material is available for this article.