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Calvarial eosinophilic granuloma: diagnostic models and image feature selection with a neural network
E Arana1, L Martí-Bonmatí, D Bautista
1Department of Radiology, Hospital Casa Salud, Manuel Candela, Valencia, Spain.
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.