[Diagnosis of calvarial lesions. Feature selection by neural network and logistic regression].
OBJECTIVE
To establish the minimun set of features needed in the diagnosis of calvarial lesions using computed tomography (CT) and to assess the accuracy of logistic regression (LR) and artificial neural networks (NN) for their diagnosis.


METHODS
167 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 jacknife method. The final results of each model were compared using the area under ROC curves (A 2 ).


RESULTS
The lesions were 73.1 % benign and 26.9% 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.


CONCLUSIONS
A minimum four features is needed to diagnose these lesions, not being important patients' symptoms. NNs offer wide possibilities over statistics for the calvarial lesions study besides a superior diagnostic performance.
Arana
E
E
Servicios de Radiodiagnóstico de Clínica Quirón, Valencia, Spain.
Martí-Bonmatí
L
L
Bautista
D
D
Paredes
R
R
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012044:Regression Analysis; D015203:Reproducibility of Results; D012888:Skull Neoplasms / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed