Bayesian Classifier for Predicting Malignant Renal Cysts on MDCT: Early Clinical Experience
July 23rd, 2009 Posted by: admin
OBJECTIVE. The objective of our study was to evaluate the
feasibility and usefulness of the Bayesian classifier for predicting malignant
renal cysts on MDCT.
MATERIALS AND METHODS. Ninety-three complicated cysts with
pathologic confirmation were enrolled. Patient age and sex and seven
morphologic features of the cysts including the maximum diameter, wall
features, wall thickness, septa features, measurable enhancement of the wall
and septa, presence of calcification, and presence of an enhancing soft-tissue
component were used to train the Bayesian classifier. Four radiologists
independently reviewed the MDCT images, and the probability of malignancy in
each cyst was rated by the radiologists and the Bayesian classifier. The
diagnostic performances of the radiologists’ visual decisions and the Bayesian
classifier were then compared using receiver operating characteristic (ROC)
curve analysis. The sensitivity and specificity were also compared between the
visual decisions and the Bayesian classifier.
RESULTS. The area under the ROC curve for predicting malignant renal
cysts by the Bayesian classifier was greater than the visual decisions of
three readers (reader 1, p = 0.02; reader 2, p < 0.01;
reader 4, p = 0.02) and was similar to the visual decision of one
reader (reader 3, p = 0.51). The specificity for predicting malignant
renal cysts was greater by the Bayesian classifier than by the visual
decisions in readers 2 (p = 0.04) and 4 (p = 0.02) and was
similar in readers 1 (p = 0.68) and 3 (p = 1.00). In terms
of sensitivity, there was no significant difference between the Bayesian
classifier and the visual decisions in all four readers (p >
0.05).
CONCLUSION. For predicting malignant renal cysts on MDCT, the
Bayesian classifier is feasible and may improve diagnostic performance.
