Focal Calvarial Bone Lesions: Comparison of Logistic Regression and Neural Network Models

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Abstract

RATIONALE AND OBJECTIVES.

To assess the accuracy of logistic regression (LR) and artificial neural networks (NN) in the diagnosis of calvarial lesions using computed tomography (CT) and to establish the importance of the different features needed for the diagnosis.

METHODS.

One hundred sixty-seven patients with calvarial lesions as the only known disease were enrolled. The clinical and CT data were used for LR and NN models. Both models were tested with the leave-one-out method. The final results of each model were compared using the area under receiver operating characteristic curves (Az).

RESULTS.

Of the lesions, 73.1% were benign and 26.9% were malignant. There was no statistically significant difference between LR and NN in differentiating malignancy. In characterizing the histologic diagnoses, NN was statistically superior to LR. Important NN features needed for malignancy classification were age and edge definition, and for the histologic diagnoses matrix, marginal sclerosis, and age.

CONCLUSION.

NNs offer wide possibilities over statistics for the study of calvarial lesions other than their superior diagnostic performance.

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