Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation

Abstract

Abstract. We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point (threshold ¼ 0.5) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point (threshold ¼ 0.166) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.

Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma

“…For example, enhanced T 1 WI could show the perfusion and extravasation characteristics of the contrast agent, while T 2 MRI sequences present edema as well as cell density, to a certain extent . T 1 WI spatial habitat diversity features have previously been demonstrated to be associated with clinical characteristics of epidermal growth factor receptor . This may also explain why more enhanced T 1 WI features were selected than the other two sequences in the combination radiomic features.…”

“…39 T 1 WI spatial habitat diversity features have previously been demonstrated to be associated with clinical characteristics of epidermal growth factor receptor. 40 This may also explain why more enhanced T 1 WI features were selected than the other two sequences in the combination radiomic features. Multiparametric MRI combines contrast sequences that might illuminate hidden characteristics and offer insight into molecular status related to tumor development.…”

Background, current role, and potential applications of radiogenomics

“…In a study of 48 image‐guided biopsies obtained in 13 tumors, Hu et al demonstrated correlations between conventional, diffusion tensor imaging (DTI), and dynamic susceptibility contrast (DSC) perfusion metrics, and commonly implicated alterations in EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53 ( P < 0.03)—with accuracies ranging from 87.5% for RB1 to 37.5% for TP53. A similar study of spatial diversity texture features was able to characterize local EGFR mutation status as well as patient survival in 65 glioblastomas . Because the various clonal populations are driven by unique genomic alterations, each with inherently different sensitivities to treatment, accurate characterization of tumor heterogeneity will become essential for success in the emerging era of targeted cancer therapies.…”

“…A similar study of spatial diversity texture features was able to characterize local EGFR mutation status as well as patient survival in 65 glioblastomas. 35 Because the various clonal populations are driven by unique genomic alterations, each with inherently different sensitivities to treatment, accurate characterization of tumor heterogeneity will become essential for success in the emerging era of targeted cancer therapies.…”

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…To predict outcomes, habitat images have been generated based on postcontrast T1 and FLAIR sequences. 191 An emerging area of interest considers the use of PET data in combination with CT or MRI to identify habitats with distinct combinations of metabolic activity (PET) with cellularity or perfusion (MRI). Then, FLAIR values were added, thus classifying the images into 5 habitats according to different combinations of blood flow and cellularity.…”