Automated grading of glioma based on density and atypia analysis in whole slide images.
10.7507/1001-5515.202103050
- Author:
Jineng HAN
1
;
Jiawei XIE
1
;
Song GU
1
;
Chaoyang YAN
1
;
Jianrui LI
2
;
Zhiqiang ZHANG
2
;
Jun XU
1
Author Information
1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, P.R.China.
2. Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, P.R.China.
- Publication Type:Journal Article
- Keywords:
cell density;
glioma grading;
histopathological image;
nuclear atypia
- MeSH:
Brain Neoplasms/diagnostic imaging*;
Glioma/diagnostic imaging*;
Humans;
Magnetic Resonance Imaging;
Neoplasm Grading;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2021;38(6):1062-1071
- CountryChina
- Language:Chinese
-
Abstract:
Glioma is the most common malignant brain tumor and classification of low grade glioma (LGG) and high grade glioma (HGG) is an important reference of making decisions on patient treatment options and prognosis. This work is largely done manually by pathologist based on an examination of whole slide image (WSI), which is arduous and heavily dependent on doctors' experience. In the World Health Organization (WHO) criteria, grade of glioma is closely related to hypercellularity, nuclear atypia and necrosis. Inspired by this, this paper designed and extracted cell density and atypia features to classify LGG and HGG. First, regions of interest (ROI) were located by analyzing cell density and global density features were extracted as well. Second, local density and atypia features were extracted in ROI. Third, balanced support vector machine (SVM) classifier was trained and tested using 10 selected features. The area under the curve (AUC) and accuracy (ACC) of 5-fold cross validation were 0.92 ± 0.01 and 0.82 ± 0.01 respectively. The results demonstrate that the proposed method of locating ROI is effective and the designed features of density and atypia can be used to predict glioma grade accurately, which can provide reliable basis for clinical diagnosis.
