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Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading... Full description

Journal Title: Oncotarget July 18, 2017, Vol.8(29), pp.47816-47830
Main Author: Zhang, Xin
Other Authors: Yan, Lin-Feng , Hu, Yu-Chuan , Li, Gang , Yang, Yang , Han, Yu , Sun, Ying-Zhi , Liu, Zhi-Cheng , Tian, Qiang , Han, Zi-Yang , Liu, Le-De , Hu, Bin-Quan , Qiu, Zi-Yu , Wang, Wen , Cui, Guang-Bin
Format: Electronic Article Electronic Article
Language: English
Subjects:
Mri
ID: E-ISSN: 1949-2553 ; DOI: 10.18632/oncotarget.18001
Link: http://search.proquest.com/docview/1908428070/?pq-origsite=primo
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recordid: proquest1908428070
title: Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
format: Article
creator:
  • Zhang, Xin
  • Yan, Lin-Feng
  • Hu, Yu-Chuan
  • Li, Gang
  • Yang, Yang
  • Han, Yu
  • Sun, Ying-Zhi
  • Liu, Zhi-Cheng
  • Tian, Qiang
  • Han, Zi-Yang
  • Liu, Le-De
  • Hu, Bin-Quan
  • Qiu, Zi-Yu
  • Wang, Wen
  • Cui, Guang-Bin
subjects:
  • Adult–Diagnostic Imaging
  • Brain Neoplasms–Pathology
  • Female–Diagnostic Imaging
  • Glioma–Pathology
  • Humans–Methods
  • Image Interpretation, Computer-Assisted–Methods
  • Machine Learning–Methods
  • Magnetic Resonance Imaging–Methods
  • Male–Methods
  • Middle Aged–Methods
  • Neoplasm Grading–Methods
  • Reproducibility of Results–Methods
  • Mri
  • Attribute Selection
  • Glioma Grading
  • Machine Learning
  • Support Vector Machine (Svm)
ispartof: Oncotarget, July 18, 2017, Vol.8(29), pp.47816-47830
description: Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying...
language: eng
source:
identifier: E-ISSN: 1949-2553 ; DOI: 10.18632/oncotarget.18001
fulltext: fulltext
issn:
  • 19492553
  • 1949-2553
url: Link


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titleOptimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
creatorZhang, Xin ; Yan, Lin-Feng ; Hu, Yu-Chuan ; Li, Gang ; Yang, Yang ; Han, Yu ; Sun, Ying-Zhi ; Liu, Zhi-Cheng ; Tian, Qiang ; Han, Zi-Yang ; Liu, Le-De ; Hu, Bin-Quan ; Qiu, Zi-Yu ; Wang, Wen ; Cui, Guang-Bin
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identifierE-ISSN: 1949-2553 ; DOI: 10.18632/oncotarget.18001
subjectAdult–Diagnostic Imaging ; Brain Neoplasms–Pathology ; Female–Diagnostic Imaging ; Glioma–Pathology ; Humans–Methods ; Image Interpretation, Computer-Assisted–Methods ; Machine Learning–Methods ; Magnetic Resonance Imaging–Methods ; Male–Methods ; Middle Aged–Methods ; Neoplasm Grading–Methods ; Reproducibility of Results–Methods ; Mri ; Attribute Selection ; Glioma Grading ; Machine Learning ; Support Vector Machine (Svm)
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descriptionCurrent machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying...
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