Evaluation of tumor‐derived MRI‐texture features for discrimination of molecular subtypes and prediction of 12‐month survival status in glioblastoma. Issue 11 (29th October 2015)

• Record Type: Journal Article
• Authors: Yang, Dalu; Rao, Ganesh; Martinez, Juan; Veeraraghavan, Ashok; Rao, Arvind
• Is Part Of: Medical physics. Volume 42:Issue 11(2015)
• Page Start: 6725
• Page End: 6735
• Publication Date: 2015-10-29
• Subjects: biomedical MRI -- cancer -- feature extraction -- fractals -- image segmentation -- medical image processing -- tumours; Segmentation -- Clinical applications -- Cancer; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; glioblastoma -- texture features -- imaging genomics -- molecular subtypes; Cancer -- Magnetic resonance imaging -- Brain -- Fractals -- Medical image segmentation -- Three dimensional image processing -- Image analysis -- Rotation invariant pattern recognition -- Genomics; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4934373
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer. Four molecular subtypes of GBM have been described but can only be determined by an invasive brain biopsy. The goal of this study is to evaluate the utility of texture features extracted from magnetic resonance imaging (MRI) scans as a potential noninvasive method to characterize molecular subtypes of GBM and to predict 12‐month overall survival status for GBM patients. Methods: The authors manually segmented the tumor regions from postcontrast T1 weighted and T2 fluid‐attenuated inversion recovery (FLAIR) MRI scans of 82 patients with de novo GBM. For each patient, the authors extracted five sets of computer‐extracted texture features, namely, 48 segmentation‐based fractal texture analysis (SFTA) features, 576 histogram of oriented gradients (HOGs) features, 44 run‐length matrix (RLM) features, 256 local binary patterns features, and 52 Haralick features, from the tumor slice corresponding to the maximum tumor area in axial, sagittal, and coronal planes, respectively. The authors used an ensemble classifier called random forest on each feature family to predict GBM molecular subtypes and 12‐month survival status (a dichotomized version of overall survival at the 12‐month time point indicating if the patient was alive or not at 12 months). The performance of the prediction was quantified and compared using receiver operating characteristic (ROC) curves. Results: With the appropriate combination of texture feature set, image plane (axial, coronal, or sagittal), and MRI sequence, the area under ROC curve values for predicting different molecular subtypes and 12‐month survival status are 0.72 for classical (with Haralick features on T1 postcontrast axial scan), 0.70 for mesenchymal (with HOG features on T2 FLAIR axial scan), 0.75 for neural (with RLM features on T2 FLAIR axial scan), 0.82 for proneural (with SFTA features on T1 postcontrast coronal scan), and 0.69 for 12‐month survival status (with SFTA features on T1 postcontrast coronal scan). Conclusions: The authors evaluated the performance of five types of texture features in predicting GBM molecular subtypes and 12‐month survival status. The authors' results show that texture features are predictive of molecular subtypes and survival status in GBM. These results indicate the feasibility of using tumor‐derived imaging features to guide genomically informed interventions without the need for invasive biopsies.