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. 2015 Nov;42(11):6725-35.
doi: 10.1118/1.4934373.

Evaluation of Tumor-Derived MRI-texture Features for Discrimination of Molecular Subtypes and Prediction of 12-month Survival Status in Glioblastoma

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Free PMC article

Evaluation of Tumor-Derived MRI-texture Features for Discrimination of Molecular Subtypes and Prediction of 12-month Survival Status in Glioblastoma

Dalu Yang et al. Med Phys. .
Free PMC article

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.

Figures

FIG. 1.
FIG. 1.
A flow chart showing the preprocessing and feature extraction steps. Parts of a SFTA feature vector are shown on the bottom left for illustration purpose. A detailed description of feature extraction algorithms is in Sec. 2.C.
FIG. 2.
FIG. 2.
SFTA feature extraction. A tumor ROI is decomposed to 16 binary images by applying the two-threshold binary decomposition algorithm. For each binary image, three features are computed: fractal dimension, area, and the average gray level of the corresponding pixels in the original image. The 48 features are combined to form the SFTA feature descriptor. “I” refers to the gray-level representation of the image. tn and tn+1 represent the upper and lower limits of the nth intensity bin.
FIG. 3.
FIG. 3.
Computing the RLM. The original tumor ROI is reduced to a 4-bit grayscale image (16 different intensities). RLM is constructed by counting the number of runs with each gray level and run-lengths. Eleven features can be computed by weighting and summing different entries of the matrix.
FIG. 4.
FIG. 4.
Computing LBP. In step 1, for each pixel in the tumor ROI, a 3 × 3 patch covering the pixel is extracted. In step 2, we compare the pixel intensity of the surrounding pixels with the center pixel. The comparison results are either 1 (the surrounding pixels are more intense) or 0 (the surrounding pixels are less intense) and are recorded in a 3 × 3 table. In step 3, the results in the table form in the indicated direction a binary string, which is then converted to a decimal number. Eventually, each pixel has a decimal number describing its binary pattern. We use the normalized histogram of these decimal numbers as the LBP texture features.
FIG. 5.
FIG. 5.
Computing HOG features. To ensure that the final HOG features had the same dimension, we resized all the original tumor ROIs to 40 × 40. Then, the gradient map is computed and divided into 25 8 × 8 cells. In each cell, we compute the histogram of the gradient intensities in each of the nine directions. The histograms are illustrated by the short white lines in each cell. In each 2 × 2 blocks (such as the one in red full lines and the one in black dotted lines), the histograms are normalized via the L2-Hys scheme.
FIG. 6.
FIG. 6.
Computing Haralick features. The tumor ROI was reduced to a 4-bit grayscale image (16 different intensities). The entry (i, j) of a GLCM can be computed by counting the total number of pixel pairs that have a gray level i and j in the image at the specific distance. For each GLCM, 13 Haralick features can be computed by weighting and summing different entries of the matrix. Finally, we found a total of 52 Haralick features for each tumor ROI (by concatenating the 13 features across different distances).
FIG. 7.
FIG. 7.
Performance of five texture feature descriptors in classifying different subtypes of glioblastoma. The AUCs are averaged over both modalities (postcontrast T1-weighted and T2-weighted fluid-attenuated inversion recovery) and all anatomic planes (axial, sagittal, and coronal). The * sign on each bar indicates that the average AUC for that feature set is significantly higher than random classification (AUC = 0.5). Abbreviations: SFTA, segmentation-based fractal texture analysis; HOG, histogram of oriented gradients; RLM, run-length matrix; LBP, local binary patterns; and HARALICK, Haralick texture features.
FIG. 8.
FIG. 8.
Performance of classifiers for predicting glioblastoma subtypes using five feature descriptors extracted from scans of two magnetic resonance imaging modalities: postcontrast T1-weighted (T1-post-Gd) and T2-weighted FLAIR. The AUCs are averaged over all subtypes and anatomic planes (axial, sagittal, and coronal). Abbreviations: SFTA, segmentation-based fractal texture analysis; HOG, histogram of oriented gradients; RLM, run-length matrix; LBP, local binary patterns; and HARALICK, Haralick texture features. The * sign on each bar indicates that the average AUC for that feature set is significantly higher than random classification (AUC = 0.5).
FIG. 9.
FIG. 9.
Performance of classifiers predicting glioblastoma subtypes using five texture feature descriptors extracted from scans of different image planes. The AUCs are averaged over all subtypes and both modalities (postcontrast T2-weighted and T1-weighted fluid-attenuated inversion recovery magnetic resonance imaging scans). The * sign on each bar indicates that the average AUC for that feature set if significantly higher than random classification (AUC = 0.5). Abbreviations: SFTA, segmentation-based fractal texture analysis; HOG, histogram of oriented gradients; RLM, run-length matrix; LBP, local binary patterns; and HARALICK, Haralick texture features.

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