High-throughput texture analysis in the distinction of single metastatic brain tumors from high-grade gliomas
10.3760/cma.j.issn.0253-3766.2018.11.009
- VernacularTitle: 高通量纹理分析鉴别脑内单发转移瘤和高级别胶质瘤
- Author:
Haolin YIN
1
;
Dongbao LI
2
;
Yu JIANG
3
;
Shihong LI
1
;
Yong CHEN
3
;
Guangwu LIN
1
Author Information
1. Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
2. School of Physics and Materials Science, East China Normal University, Shanghai 200062, China
3. Department of Radiology, Sichuan Key Laboratory of Medical Image, The Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
- Publication Type:Clinical Trail
- Keywords:
Gliomas;
Brain metastasis;
Texture analysis;
Radiomics;
Diagnosis, differential
- From:
Chinese Journal of Oncology
2018;40(11):841-846
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of high-throughput texture analysis in the distinction of single brain metastases (SBM) from high-grade gliomas (HGG) and validate the established model.
Methods:A total of 86 patients who were histologically diagnosed with SBM or HGG were retrospectively collected, including 43 patients with SBM and 43 with HGG. All of patients were performed preoperative conventional head magnetic resonance imaging (MRI) scans. A total of 236 fluid-attenuated inversion recovery (FLALR) images containing the information of tumors were selected from the MRI images and each image was considered as an object. The training set had 200 images, including 106 from SBM group and 94 from HGG group, whereas the validation set had 36 images, including 19 from SBM group and 17 from HGG. After images preprocessing, images segmentation, features extraction, and features selection, a radiomic diagnostic model was finally established using the training set. The diagnostic performance of the diagnostic model was evaluated using a receiver operating characteristic (ROC) curve. Hierarchical clustering analysis was used to evaluate the quality of the extracted feature data and the classification effect of the model. The model was further validated using the independent validation set.
Results:A total of 629 features were extracted and quantified from each sample, and 41 features were selected to establish feature subsets and the diagnostic model. The classification decision function of the model is f(x)=sign and the kernel function of the model is K(x, xi)=exp. In the training set, the diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 0.845, 0.849, 0.840, 0.857 and 0.832, respectively. The area under the ROC curve reached to 0.939. Similar results were obtained in the validation set.
Conclusion:The high-throughput texture analysis shows high accuracy in differentiating SBM from HGG.
