Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

Abstract

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

Fig. 1. Radiomic heatmap.

a Unsupervised clustering of patients with gliomas is shown on the x-axis, and radiomic features selected by LASSO for prediction of IDH mutation, 1p/19q codeletion, and TERT promoter mutation status are shown on the y-axis, revealing clusters of patients with similar radiomic expression patterns. b Correspondence of radiomic feature groups with the clustered expression patterns.

Fig. 2. The nomograms and calibration curves.

(a) Combined nomogram incorporating the predictive molecular groups and WHO grade for predicting PFS; (b-c) Calibration curves of the nomogram (a) in the training and validation datasets, respectively; (d) Combined nomogram incorporating the predictive molecular groups and WHO grade for predicting OS; (e-f) Calibration curves of the nomogram (d) in the training and validation datasets, respectively.

Fig. 3. Schematic diagram of the proposed radiomic workflow for molecular subtyping and survival prediction.

The study design contains five main phases: image preprocessing, image segmentation, feature extraction, feature selection, and radiomic analysis.