Application of stepwise discriminant analysis for grading of astrocytomas / 中国癌症杂志

ABSTRACT

Background and purpose: Astrocytoma is the most common neuroepithelial neoplasm, and its grading has profound effect on its treatment and prognosis. To investigate the application of stepwise discriminant analysis in grading astrocytomas, this study developed two models of stepwise discriminant analysis according to relevant factors of astrocytoma. Methods: From January 2008 to April 2009, 111 primary astrocytoma patients were enrolled. Each patient was scored based on location, signal intensity on T1WI, signal intensity on T2WI, enhancement, edema, border, cyst or solidness, and mass effect of their magnetic resonance images. With their age score of grading, Fisher stepwise discriminant analysis and the Logistic discdminant were used. The results from the two models were then evaluated and compared. Results: According to Fisher stepwise diseriminant analysis, the predictive accuracy was 87.7% with 80.0% sensitivity, 91.5% specificity and 0.942 area of ROC curve. However, the predictive accuracy of Logistic discriminant analysis was 84.9% with 80.0% sensitivity, 86.8% specificity and 0.940 area of ROC curve. There were no statistically significant differences in terms of accuracy (P=0.250) and areas under ROC curve (Z=0.433, P=0.665) between the two models. Conclusion: Two stepwise discriminant analysis models are meaningful to predict the grading of astrocytoms, and the application of Fisher stepwise discriminant analysis is simpler than the Logistic discriminant analysis.