Share：
Share this content in WeChat
X
[Chinese][PDF]94280
• Original Article •
Application of radiomics in the grading of brain tumor
YANG Zhi-wei  GUO Tian  XIE Hai-bin  YANG Guang 

DOI:10.12015/issn.1674-8034.2018.06.008.


[Abstract] Objective: Using radiomics and conventional magnetic resonance images to grade brain tumor.Materials and Methods: This paper uses the open dataset: BRATS2017. Kinds of features, including shape features, intensity features and texture features, were extracted from region of interest and a subset of features were selected by hybrid feature selection methods to distinguish the high grade glioma (HGG) from the low grade glioma (LGG). Three classification algorithms including support vector machine (SVM), decision tree, and K nearest neighborhood were compared. The wrapper method using genetic algorithm (GA) was compared with filter method.Results: SVM was found to exhibit the best accuracy: 91.33% and AUC: 0.90 when using the feature selection method of filter. The accuracy and AUC raise to 93.33% and 0.94 when further using the method of genetic algorithm.Conclusions: We could use radiomics methods and conventional magnetic resonance images to automatically grade brain tumor by selecting suitable features.
[Keywords] Magnetic resonance imaging；Radiomics；Glioma；Feature selection；Genetic algorithm；Neoplasm grading

YANG Zhi-wei Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

GUO Tian Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

XIE Hai-bin* Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

YANG Guang* Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, Shanghai 200062, China

*Correspondence to: Xie HB, E-mail: hbxie@phy.ecnu.edu.cn; Yang G, E-mail: gyang@phy.ecnu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Key Program of National Natural Science Foundation of China No. 61731009
Received  2018-04-09
Accepted  2018-04-28
DOI: 10.12015/issn.1674-8034.2018.06.008
DOI:10.12015/issn.1674-8034.2018.06.008.

[1]
Horn M. Magnetic resonance imaging methods and biologic applications. New Jersey: Humana Press, 2006.
[2]
Zacharaki EI, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med, 2009, 62(6): 1609-1618.
[3]
Mohan G, Subashini GM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomedical Signal Processing and Control, 2018, 39: 139-161.
[4]
El-Dahshan E, Mohsen HM, Revett K, et al. Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl, 2014, 41(11): 5526-5545.
[5]
Chaplot S, Patnaik LM, Jagannathan NR. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control, 2006, 1(1): 86-92.
[6]
Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging, 2012, 30(9): 1234-1248.
[7]
Louis DN, Perry A, Reifenberger G, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol, 2016, 131(6): 803-820.
[8]
Menze BH, Jakab A, Bauer S, et al. The multimodal brain tumor image segmentation benchmark(BRATS). IEEE Trans Med Imaging, 2015, 34(10): 1993-2024.
[9]
Bakas S, Akbari H, Sotiras A, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomics features. Sci Data, 2017, 4: 170117.
[10]
Emblem KE, Nedregaard B, Hald JK, et al. Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magn Reson Imaging, 2009, 30(1): 1-10.
[11]
Isin A, Direkoglu C, Sah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Computer Science, 2016, 102: 317-324.
[12]
Kumar PS, Dharun VS. A study of MRI segmentation methods in automatic brain tumor detection. Int J Eng Technol, 2016, 8(2): 609-614.
[13]
Choi SUS. Enhancing thermal conductivity of fluids with nano-particles. Asme Fed, 1995, 231(1): 99-105.
[14]
Gart JJ. Measures of multivariate skewness and kurtosis with applications. Biometrika, 1970, 57(3): 519-530.
[15]
Jain AK, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 1991, 24(12): 1167-1186.
[16]
Quinlan JR. Induction on decision tree. Machine Learning, 1986, 1(1): 81-106.
[17]
Guyon I, Elisseeff A. An introduction to variable and feature selection. J Machine Learn Res, 2003, 3(6): 1157-1182.
[18]
Goldberg DE. Genetic algorithm in search, optimization, and machine learning. New Jersey: Addison-Wesley, 1989.
[19]
Zhou M, Sun SD. Genetic algorithm theory and applications. Beijing: National Defense Industry Press, 1999.
[20]
Furey TS, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 2000, 16(10): 906.
[21]
Dasarathy BV. Nearest neighbor (NN) norms: NN pattern classification techniques. Los Alamitos: IEEE Computer Society, 1991, 13(100): 21-27.
[22]
Yang XL, Song Q, Wang Y. A weighted support vector machine for data classification. Inter J Pattern Recogn and Artificial Intel, 2007, 21(5): 961-976.
[23]
Breiman LI, Friedman JH, Olshen RA, et al. Classification and regression trees(CART). Encyclopedia of Ecology, 1984, 40(3): 582-588.
[24]
李航.统计学习方法.北京:清华大学出版社, 2012.
[25]
Chang CC, Lin CJ. LIBSVM: a library for support vector machine. ACM Transactions on Intel Systems and Technol, 2011, 2(3): 1-27.
[26]
Sachdeva J, Kumar V, Gupta I, et al. A package-SFERCB- "Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors". Applied Soft Computing, 2016, 47(C): 151-167.
[27]
邓慧媛，杨军，柯腾飞,等.颅内胶质瘤MRI影像组学评估颅内高级别与低级别胶质瘤.放射学实践, 2017, 32(12): 1229-1233.
[28]
林坤，次旦旺久，祁英,等.多模态磁共振成像技术在胶质瘤评价中的应用研究.磁共振成像, 2017, 9(1): 14-20.
[29]
张楠，杨本强.脑胶质瘤分级诊断的磁共振研究新进展.磁共振成像, 2017, 8(1): 67-71.

PREV Magnetic resonance imaging study on the structural changes of anterior and posterior hippocampal subfields in schizophrenia patients
NEXT The impact of others’ decision on individual risk-taking behavior: A rs-fMRI study
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn