Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics

International Journal of Biomedical Imaging
Wei ChenXu Qiao

Abstract

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.

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Citations

May 23, 2020·Journal of Digital Imaging·Hiba MzoughiKharedine Ben Mahfoudhe
Jan 15, 2020·Journal of Healthcare Engineering·Xi ChenYusong Lin
Jun 12, 2020·Korean Journal of Radiology : Official Journal of the Korean Radiological Society·Seung Hak LeeEun Sook Ko
Jun 20, 2019·European Journal of Nuclear Medicine and Molecular Imaging·Martina SolliniMargarita Kirienko
Apr 13, 2021·Radiology. Artificial Intelligence·Katharina V HoebelJayashree Kalpathy-Cramer
May 27, 2021·Scientific Reports·Kazuma KobayashiRyuji Hamamoto

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Methods Mentioned

BETA
imaging techniques
features extraction

Software Mentioned

ANTS
XGBoost
SVM
DeepMedic
SimpleITK
net
Pyradiomics

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