Calvarial eosinophilic granuloma: diagnostic models and image feature selection with a neural network.

Abstract

The authors analyzed the accuracy of diagnostic features used by an artificial neural network compared with logistic-regression analysis in the diagnosis with computed tomography (CT) of calvarial eosinophilic granuloma. Thirty-one of 167 patients with calvarial lesions were found to have eosinophilic granuloma. Clinical and CT data were used for logistic-regression and neural network models. Both models were tested by using the leave-one-out method. The final results of each model were compared by means of the area under the receiver operating characteristic curve (Az). Identification of eosinophilic granuloma was significantly more accurate with the neural network than with logistic regression (Az = 0.9846 +/- 0.0157 [standard deviation] vs 0.9117 +/- 0.0373) (P = .001). The most important diagnostic features identified with the neural network were patient age and marginal sclerosis. For logistic regression, the most important features were age, shape, and lobularity. The neural network is a useful tool for analyzing the features of calvarial eosinophilic granuloma. Age and marginal sclerosis are important diagnostic features.