Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma

Lee, Joon Sang; Jain, Rajan; Khalil, Kamal; Griffith, Brent; Bosca, R; Rao, Ganesh; Rao, Arvind

doi:10.3174/ajnr.a4534

Cited by 66 publications

(53 citation statements)

References 23 publications

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“…Distinct from the commonly used univariate statistical features, our filters capture the spatial information of a voxel in its local neighborhood in DKI images, where our bar and edge filter could detect the enhanced vascular structure of tumor typing at different scales, and capture its local context cues. Our features also differ from another set of texture features using popular Grey-Level Concurrence Matrix (GLCM) and its associated statistics, which usually considered features at a single scale (e.g., on raw intensities) and sensitive to image resolutions 23 . Our study has also demonstrated the superiority of using texture features for determination of IDH mutation status, given the same number of biomarkers (e.g., we selected 4 biomarkers in our study), the AUC value of using texture biomarkers has improved by 49% (0.88 vs 0.59) compared with that of using first-order statistical biomarkers.…”

Section: Discussionmentioning

confidence: 99%

“…By doing so, our method could avoid redundancy between selected features (the redundancy refers to the case where the two features essentially carry the same information) but include features which has complementary information for tumor grade or mutation prediction. This can be treated as an advantage compared with the methods that rank features individually 23,25 .…”

Section: Discussionmentioning

confidence: 99%

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Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Bisdas

Shen

Thust

et al. 2018

Sci Rep

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We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Bisdas

Shen

Thust

et al. 2018

Sci Rep

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“…These results, obtained using shape features, are also complementary to previous studies, suggesting a link between phenotype texture and the survival of patients with GBM. 22,[35][36][37] This study is also related to recent work establishing a link between image features, based on spatial habitats 38 and texture (i.e. fractal texture analysis, histogram of oriented gradients, run length, local binary patterns and Haralick features) 39 and the 12-month survival status of patients with GBM.…”

Section: Discussionmentioning

confidence: 99%

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

Chaddad¹,

Desrosiers²,

Hassan³

et al. 2016

The British Journal of Radiology

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Objective: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM. Methods: The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T 1 weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal–Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan–Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM. Results: Our analysis using the Kruskal–Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm–Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan–Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival. Conclusion: Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM.

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“…a Information measure of correlation, compactness, and inverse correlation are texture features that have been previously applied to classify gliomas by degree of malignancy 54 and to predict survival outcomes in grade IV gliomas. 55 Detailed equations for these texture features are described elsewhere. 55,56 NeuroOncology combined model.…”

Section: Discussionmentioning

confidence: 99%

“…55 Detailed equations for these texture features are described elsewhere. 55,56 NeuroOncology combined model. This observation underscores the advantage of using a machine-learning algorithm to discover and integrate synergistic multimodal imaging features.…”

Section: Discussionmentioning

confidence: 99%

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

et al. 2016

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Background. High-grade gliomas with mutations in the isocitrate dehydrogenase (IDH) gene family confer longer overall survival relative to their IDH-wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. Methods. Preoperative MRIs were obtained for 120 patients with primary grades III (n = 35) and IV (n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH-mutated cohort. Cases were randomly assigned to either the training (n = 90) or validation cohort (n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype. Results. Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features. Conclusion. Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.

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Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma

Cited by 66 publications

References 23 publications

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

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