Academic radiology · 2004
Qualitative diagnosis of calvarial metastasis by neural network and logistic regression.
Сравнительное исследованиеОценочное исследованиеСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Arana Estanislao, Martí-Bonmatí Luis, Bautista Daniel, Paredes Roberto
Аннотация
RATIONALE AND OBJECTIVES: To simplify the diagnostic features used by an artificial neural network compared with logistic regression (LR) in the diagnosis of calvarial metastasis with computed tomography and analyze their accuracy.
MATERIALS AND METHODS: Twenty-one of 167 patients with calvarial lesions were found to have metastasis. Clinical and computed tomography data were used for LR and neural network 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 curve (Az).
RESULTS: The neural network identified metastasis significantly more successfully than LR with an Az of 0.9324 +/- 0.0386 versus 0.9192 +/- 0.0373, P = .01. The most important features selected by the LR and neural network were age and edge definition.
CONCLUSION: Neural networks offer wide possibilities over statistics for the study of calvarial metastases other than their minimum clinical and radiologic features for diagnosis.
MATERIALS AND METHODS: Twenty-one of 167 patients with calvarial lesions were found to have metastasis. Clinical and computed tomography data were used for LR and neural network 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 curve (Az).
RESULTS: The neural network identified metastasis significantly more successfully than LR with an Az of 0.9324 +/- 0.0386 versus 0.9192 +/- 0.0373, P = .01. The most important features selected by the LR and neural network were age and edge definition.
CONCLUSION: Neural networks offer wide possibilities over statistics for the study of calvarial metastases other than their minimum clinical and radiologic features for diagnosis.