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Publications (2)1.04 Total impact

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    ABSTRACT: This retrospective investigation was directed to the applicability of Radial Basis Function Neural Network (RBF-NN) and Discriminant Analysis in the grading of gliomas. The data on 116 patients with primary glioma in our hospital from February 2008 to April 2009 were collected. Kruskal-Wallis H test was used to draw in the variable age ranks and then to take them out from the range of different grades of gliomas. The results of RBF-NN model, discriminant analysis, and the combined model of RBF-NN and discriminant analysis were evaluated and compared respectively with and without age. In this study, different classifications of gliomas showed statistically significant differences in age: and the accuracy of the models with age was better than the ones without age. The predictive accuracy and Kappa value of RBF-NN model and the combined model were also better than those exhibited by Bayes discriminant analysis. Consequently, as a prediction model, or to help other models, RBF-NN is of significance to predicting the grade of gliomas.
    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 12/2010; 27(6):1384-8.
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    ABSTRACT: 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 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. 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. The SVM model can provide useful information to help clinicians improve diagnostic performance when predicting astrocytoma grade based on MR images.
    Neurology India 01/2010; 58(5):685-90. · 1.04 Impact Factor