Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing...

Full description

Saved in:
Bibliographic Details
Published in:International journal of biomedical imaging 2018-01, Vol.2018 (2018), p.1-11
Main Authors: Sun, Jiawei, Peng, Suting, Liu, Boqiang, Chen, Wei, Qiao, Xu
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Automation
Brain
Brain cancer
Brain research
Brain tumors
Classification
Diagnostic systems
Evaluation
Feature extraction
Genotype & phenotype
Glioma
Image processing
Magnetic resonance imaging
Medical imaging
Medical prognosis
Medical research
Multiscale analysis
Neural networks
NMR
Nuclear magnetic resonance
Principal components analysis
Radiomics
Segmentation
Support vector machines
Therapeutic applications
Tumors
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.
ISSN:1687-4188
1687-4196
DOI:10.1155/2018/2512037