Use of a neural network and a multiple regression model to predict histologic grade of astrocytoma from MRI appearances.
Several MRI features of supratentorial astrocytomas are associated with high histologic grade by statistically significant p values. We sought to apply this information prospectively to a group of astrocytomas in the prediction of tumor grade. We used 10 MRI features of fibrillary astrocytomas from 52 patient studies to develop neural network and multiple linear regression models for practical use in predicting tumor grade. The models were tested prospectively on MR images from 29 patients studies. The performance of the models was compared against that of a radiologist. Neural network accuracy was 61% in distinguishing between low and high grade tumors. Multiple linear regression achieved an accuracy of 59%. Assessment of the images by a radiologist yielded 57% accuracy. We conclude that while certain MRI parameters may be statistically related to astrocytoma histologic grade, neural network and linear regression models cannot reliably use them to predict tumor grade.
Christy
P S
PS
Mayo Clinic, Rochester, MN 55905, USA.
Tervonen
O
O
Scheithauer
B W
BW
Forbes
G S
GS
D001254:Astrocytoma / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012680:Sensitivity and Specificity