Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings

Abstract

Objective: Despite 90 % of glioblastoma (GBM) recurrences occurring in the peritumoral brain zone (PBZ), its contribution in patient survival is poorly understood. The current study leverages computerized texture (i.e. radiomic) analysis to evaluate the efficacy of PBZ features from pre-operative MRI in predicting long- (>18 months) versus short-term (<7 months) survival in GBM.

Methods: Sixty-five patient examinations (29 short-term, 36 long-term) with gadolinium-contrast T1w, FLAIR and T2w sequences from the Cancer Imaging Archive were employed. An expert manually segmented each study as: enhancing lesion, PBZ and tumour necrosis. 402 radiomic features (capturing co-occurrence, grey-level dependence and directional gradients) were obtained for each region. Evaluation was performed using threefold cross-validation, such that a subset of studies was used to select the most predictive features, and the remaining subset was used to evaluate their efficacy in predicting survival.

Results: A subset of ten radiomic 'peritumoral' MRI features, suggestive of intensity heterogeneity and textural patterns, was found to be predictive of survival (p = 1.47 × 10-5) as compared to features from enhancing tumour, necrotic regions and known clinical factors.

Conclusion: Our preliminary analysis suggests that radiomic features from the PBZ on routine pre-operative MRI may be predictive of long- versus short-term survival in GBM.

Key points: • Radiomic features from peritumoral regions can capture glioblastoma heterogeneity to predict outcome. • Peritumoral radiomics along with clinical factors are highly predictive of glioblastoma outcome. • Identifying prognostic markers can assist in making personalized therapy decisions in glioblastoma.

Keywords: Glioblastoma multiforme; Peritumoral; Radiomics; Survival; Texture.

Figures

Figure 1

Annotations of necrotic core and enhancing tumour as delineated by an expert for a representative Gd-T1w MRI slice, are outlined in green and red respectively, while the annotations for PBZ as delineated on FLAIR are shown in yellow.

Figure 2

Kaplan Meier Survival curves obtained using radiomic features to predict short-term (shown in red curve) from long-term survivors (shown in blue curve). Top row shows the survival curves obtained by using the top 10 features from the tumour necrosis region alone across T1w, T2w, FLAIR, and multi-parametric MRI. Middle row shows the corresponding survival curves obtained by using the top 10 features from the enhancing lesion alone. Similarly, the bottom row shows the survival curves obtained by using the top 10 peritumoral radiomic features, for T1w, T2w, FLAIR, and multi-parametric MRI respectively.

Figure 3

Kaplan Meier Survival curves for classification to predict short-term (shown in red curve) from long-term survivors (shown in blue curve) using clinical features like (a) age, (b) gender, (c) Karnofsky Performance Score (KPS), (d) combination of clinical features (age, gender, KPS) and top 10 peritumoral radiomic features across multi-parametric MRI sequences, as compared to the Kaplan Meier survival curve obtained from the “ground truth” labels.

Figure 4

A single 2-dimensional Gd-T1w MRI slice for two different patients with STS (a) and LTS (g) respectively. Expert annotated region bounded in green is necrosis; region bounded in orange is enhancing tumour, while the region bounded in black is oedema. The corresponding per-voxel representations of 3 Haralick descriptors are shown for entropy (d, j), Correlation (e, k), and Sum Entropy features (f, l).