Fuzzy rules to predict degree of malignancy in brain glioma.
The current pre-operative assessment of the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. 280 cases were studied, of which 111 were high-grade malignancies and 169 were low-grade, so that regular and interpretable patterns of the relationships between glioma MRI features and the degree of malignancy could be acquired. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used. The performance of a multi-layer perceptron network (MLP) trained with the error back-propagation algorithm (BP), the decision tree algorithm ID3, nearest neighbour and the original fuzzy min-max neural network were also evaluated. The results showed that two fuzzy decision rules on only six features achieved an accuracy of 84.6% (89.9% for low-grade and 76.6% for high-grade cases). Investigations with the proposed algorithm revealed that age, mass effect, oedema, postcontrast enhancement, blood supply, calcification, haemorrhage and the signal intensity of the T1-weighted image were important diagnostic factors.
Ye
C Z
CZ
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, China.
Yang
J
J
Geng
D Y
DY
Zhou
Y
Y
Chen
N Y
NY
D001932:Brain Neoplasms / Q000473:pathology*; D017143:Fuzzy Logic; D005910:Glioma / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging