Qualitative diagnosis of calvarial metastasis by neural network and logistic regression.
OBJECTIVE
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.


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.


CONCLUSIONS
Neural networks offer wide possibilities over statistics for the study of calvarial metastases other than their minimum clinical and radiologic features for diagnosis.
Arana
Estanislao
E
Department of Radiology, Hospital Universitario Dr Peset, Valencia, Spain.
Martí-Bonmatí
Luis
L
Bautista
Daniel
D
Paredes
Roberto
R
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D004803:Eosinophilic Granuloma / Q000175:diagnosis / Q000453:epidemiology; D004814:Epidermal Cyst / Q000175:diagnosis / Q000453:epidemiology; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010016:Osteoma / Q000175:diagnosis / Q000453:epidemiology; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012886:Skull / Q000473:pathology*; D012888:Skull Neoplasms / Q000175:diagnosis* / Q000453:epidemiology / Q000556:secondary*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data