Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy

Ranjith, G.; Parvathy, R; Vikas, Vazhayil; Kesavadas, Chandrasekharan; Nair, Suresh

doi:10.1177/1971400915576637

Cited by 58 publications

(29 citation statements)

References 18 publications

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“…30 This approach can also distinguish low-grade from high-grade gliomas by MRI features from area under the receiver operating characteristic (AUROC) analysis. 31 Building a classifier involved growing multiple decision trees based on random selection of predictors (ie, MRI features) and random selection of data (ie, glioma cases). In this study, all 120 patients were randomly assigned to either the training cohort (n = 90) or validation cohort (n = 30).…”

Section: Classification Proceduresmentioning

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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Section: Classification Proceduresmentioning

confidence: 99%

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

et al. 2016

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“…ML algorithms have been intuitively applied to “data-rich” MRI sequences in an effort to quantitatively discern characteristic imaging features of gliomas, due to differences between tumor area and normal brain 32–36 . These methods have yielded ability to discern imaging features indicating the presence of MGMT methylation 35 , IDH1 mutation 37–39 and 1p/19q co-deletion 38 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Panesar

D'Souza

Yeh

et al. 2018

Preprint

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BackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

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“…After implementing several machine learning methods in this study, a sensitivity of 86.1% was achieved using the random forest method. The classification solutions in both these earlier works were based on the use of single voxel proton MR spectroscopy unlike the solution proposed in our work, where we used FLAIR-weighted MR images [18,19] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The classification methodology addressed in the study by Ranjith et al attempted to classify the samples into two classes: benign and malignant. In this study, the database utilized consisted of MR spectroscopy data [19]. After implementing several machine learning methods in this study, a sensitivity of 86.1% was achieved using the random forest method.…”

Section: Literature Reviewmentioning

confidence: 99%

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

et al. 2017

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Abstract:In recent years, Convolutional Neural Networks (ConvNets) have rapidly emerged as a widespread machine learning technique in a number of applications especially in the area of medical image classification and segmentation. In this paper, we propose a novel approach that uses ConvNet for classifying brain medical images into healthy and unhealthy brain images. The unhealthy images of brain tumors are categorized also into low grades and high grades. In particular, we use the modified version of the Alex Krizhevsky network (AlexNet) deep learning architecture on magnetic resonance images as a potential tumor classification technique. The classification is performed on the whole image where the labels in the training set are at the image level rather than the pixel level. The results showed a reasonable performance in characterizing the brain medical images with an accuracy of 91.16%.

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Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy

Abstract: The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences.

Cited by 58 publications

References 18 publications

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

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

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks

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