Automated grading of glioma based on density and atypia analysis in whole slide images / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 1062-1071, 2021.
Article
em Zh
| WPRIM
| ID: wpr-921846
Biblioteca responsável:
WPRO
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.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Neoplasias Encefálicas
/
Imageamento por Ressonância Magnética
/
Gradação de Tumores
/
Máquina de Vetores de Suporte
/
Glioma
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2021
Tipo de documento:
Article