RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSION: The neural network is a useful tool for analyzing the features of calvarial eosinophilic granuloma. Age and marginal sclerosis are important diagnostic features.
RATIONALE AND OBJECTIVES: 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. MATERIALS AND METHODS: 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). RESULTS: 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. CONCLUSION: The neural network is a useful tool for analyzing the features of calvarial eosinophilic granuloma. Age and marginal sclerosis are important diagnostic features.