Document Detail


A new method to classify pathologic grades of astrocytomas based on magnetic resonance imaging appearances.
MedLine Citation:
PMID:  21045488     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Background: Astrocytoma is the most common neuroepithelial neoplasm, and its grading greatly affects treatment and prognosis. Objective: According to relevant factors of astrocytoma, this study developed a support vector machine (SVM) model to predict the astrocytoma grades and compared the SVM prediction with the clinician's diagnostic performance. Patients and Methods: Patients were recruited from a cohort of astrocytoma patients in our hospital between January 2008 and April 2009. Among all astrocytoma patients, nine had grade I, 25 had grade II, 12 had grade III, and 60 had grade IV astrocytoma. An SVM model was constructed using radial basis kernel. The SVM model was trained with nine magnetic resonance (MR) features and one clinical parameter by fivefold cross-validation and differentiated astrocytomas of grades I-IV at two levels, respectively. The clinician also predicted the grade of astrocytoma. According to the two prediction methods above, the areas under receiving operating characteristics (ROC) curves to discriminate low- and high-grade groups, accuracies of high-grade grouping, overall accuracy, and overall kappa values were compared. Results: For SVM, the overall accuracy was 0.821 and the overall kappa value was 0.679; for clinicians, the overall accuracy was 0.651 and the overall kappa value was 0.466. The diagnostic performance of SVM is significantly better than clinician performance, with the exception of the low-grade group. Conclusions: The SVM model can provide useful information to help clinicians improve diagnostic performance when predicting astrocytoma grade based on MR images.
Authors:
Zhong-Xin Zhao; Kai Lan; Jia-He Xiao; Yu Zhang; Peng Xu; Lu Jia; Min He
Related Documents :
12403388 - Omphalocele: prenatal mr findings.
8816528 - Refractory epilepsy: comparison of mr imaging, ct, and histopathologic findings in 117 ...
15935318 - Female pelvis.
11972468 - Mr and ultrasound in screening of patients with suspected biliary tract disease.
21879908 - Blast-related traumatic brain injury in u.s. military personnel.
19041038 - Pharyngeal dysphagia: what the radiologist needs to know.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Neurology India     Volume:  58     ISSN:  0028-3886     ISO Abbreviation:  Neurol India     Publication Date:    2010 Sep-Oct
Date Detail:
Created Date:  2010-11-03     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0042005     Medline TA:  Neurol India     Country:  India    
Other Details:
Languages:  eng     Pagination:  685-90     Citation Subset:  IM    
Affiliation:
Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu Sichuan - 610 041, P.R., China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Malignant peripheral nerve sheath tumor of the tongue.
Next Document:  Sarcoglycanopathy: Clinical and histochemical characteristics in 66 patients.