Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

Citak-Er, Fusun; Fırat, Zeynep; Kovanlıkaya, İlhami; Türe, Uğur; Öztürk-Işık, Esin

doi:10.1016/j.compbiomed.2018.06.009

Cited by 62 publications

(38 citation statements)

References 40 publications

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“…The prediction rate in our study performed better (65% of specificity, 79% sensitivity) than the one reported by Kickingereder et al [8] in a single-centre trial despite the overall heterogeneous nature of our large cohort in terms of molecular subgroups and DSC acquisition protocols. Similar machine-assisted techniques for tumour grade prediction demonstrated accuracy between 73 and 85% [40][41][42][43]. While our algorithm performance appears slightly inferior to the aforementioned results, we note that in all these studies patients are recruited from one centre, while we considered patients data from six centres.…”

Section: Discussionmentioning

confidence: 83%

“…Using a different simulation approach might however improve accuracy. Citak-Er et al [43] have proposed the use of a linear kernel support vector machine (SVM) with ten-fold cross-validation based on features extracted from conventional and advanced MRI data (DTI, DWI, DSC perfusion and MR-Spectroscopy), and reported 73.3% of overall sensitivity from their multiparametric MR imaging. For the purpose of our study we chose random-forest algorithm as an established method for three classes or more classification.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Sudre

Panovska‐Griffiths

Sanverdi

et al. 2020

BMC Med Inform Decis Mak

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Background: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon twosample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1).

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Sudre

Panovska‐Griffiths

Sanverdi

et al. 2020

BMC Med Inform Decis Mak

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“…Using a different simulation approach might however improve accuracy. Citak-Er et al 43 certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Discussionmentioning

confidence: 99%

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Sudre

Panovska‐Griffiths

Sanverdi

et al. 2019

Preprint

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Background: Machine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. Our study assess the diagnostic value of such machine learning for DSC-MRI radiomics in classifying treatment-naive gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: 333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Features from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1. Conclusion: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading. Keywords: diagnostic machine learning, glioma stratification, Isocitrate dehydrogenase; MRI imaging

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“…They obtained a 93.94% accuracy rate with their proposed model. Citak et al [8] mentioned and they had been utilized three various computer aided techniques to classify the tumor. The algorithms used are Support Vector Machine, logistic regression, and multilayer perceptron.…”

Section: Related Workmentioning

confidence: 99%

Classification of Brain Tumors Using Convolutional Neural Network over Various SVM Methods

Sajja¹,

Kalluri²

2020

ISI

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A computer-based method is presented in this paper to define brain tumor using MRI images. The main classification motive is to identify a brain into a healthy brain or classify a brain with a tumor when a patient's MRI images are given. Magnetic Resonance Imaging (MRI) is an important one among the common imaging treatments, which presents more detailed brain tumor identification information and provides detailed pictures of inside your body other than computed tomography (CT). Currently, CNNs is a famous technique to deal with most of the problems with image classification as they provide greater accuracy compared to other classifiers. Hbridized CNN has been used in this work. It consists of three convolution layers and three max pooling layers which could provide outrated performance. Images from open databases such as BRATS were tested on brain MRI images. The proposed model has given the improved performance over the existing model with an accuracy of 96.15%.

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Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

Cited by 62 publications

References 40 publications

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

Classification of Brain Tumors Using Convolutional Neural Network over Various SVM Methods

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