Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma

Abstract

Background: The purpose of this study was to analyze the potential of radiomics for disease stratification beyond key molecular, clinical, and standard imaging features in patients with glioblastoma.

Methods: Quantitative imaging features (n = 1043) were extracted from the multiparametric MRI of 181 patients with newly diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and a validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI test-retest cohort) and selected for analysis. A penalized Cox model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS and OS). The incremental value of a radiomic signature beyond molecular (O6-methylguanine-DNA methyltransferase [MGMT] promoter methylation, DNA methylation subgroups), clinical (patient's age, KPS, extent of resection, adjuvant treatment), and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox models (performance quantified with prediction error curves).

Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation sets) beyond the assessed molecular, clinical, and standard imaging parameters (P ≤ 0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (compared with 29% and 27%, respectively, with molecular + clinical features alone). The radiomic signature was-along with MGMT status-the only parameter with independent significance on multivariate analysis (P ≤ 0.01).

Conclusions: Our study stresses the role of integrating radiomics into a multilayer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

Fig. 1

Radiomics image postprocessing workflow. Left: different tumors have different shapes and intensities, as shown on representative slices on the left (tumor segmentations in red), with the volume-rendered 3D segmentations on the right. Right: workflow from tumor segmentation to analysis. (I) Different MRI sequences are skull-stripped and coregistered to each other. (II) Image intensities are normalized into a common parameter space that allows referencing across different subjects. (III) Multiple radiomic features are automatically calculated from intensity-normalized images using 3D segmentations, including first-order, volume/shape, and texture features. (IV) The large number of radiomic feature parameters are then subjected to machine learning–based classification algorithms to identify radiomic-based imaging signatures that are related to an outcome of interest. Finally, integrative assessment of radiomic signatures with molecular and clinical characteristics aims to improve stratification of patients.

Fig. 2

Prediction error curves for stratifying PFS (upper row) and OS (lower row) based on a single layer (left column)—ie, either molecular (including MGMT promoter methylation status + global DNA methylation glioblastoma subtypes) or clinical (patient’s age + KPS, EOR, adjuvant treatment) information or standard imaging parameters (tumor volumes from contrast enhancement, necrosis, and edema) or the radiomic signature—or (right column) combining the information from multiple layers. Prediction error rates are given in brackets (including the percentage reduction compared with the null model with no explanatory value). Combining the information from multiple layers (right column) allowed reduction of the prediction error beyond every single layer model (left column). The identified radiomic signature reduced the prediction error beyond molecular and clinical features and combining molecular + clinical information and the radiomic signature yielded the highest accuracy, with a reduction of the prediction error by 36% for PFS and 37% for OS (compared with 29% and 27% for a model without the radiomic signature that includes only molecular and clinical information).

Fig. 3

Progression-free and overall survival in the discovery and validation sets stratified based on key molecular characteristics (MGMT promoter methylation status + global DNA methylation glioblastoma subtypes), clinical characteristics (patient’s age + KPS, EOR, adjuvant treatment), and the radiomic signature. Survival curves were derived computing nearest-neighbor estimate of bivariate distribution of survival and linear predictor levels. For illustration purposes, we show estimates for low, medium, and high levels of predictors.

Fig. 4

Multivariate Cox regression model for PFS and OS with key molecular parameters (MGMT promoter methylation status, molecular glioblastoma subtypes), clinical features, and the radiomic signature as explanatory variables. Independent significance for both PFS and OS in both discovery and validation sets was only retained for the radiomic signature and MGMT promoter methylation status. MES = mesenchymal; RT = radiation therapy.