Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses.
OBJECTIVE
The aim of this study was to generate and evaluate artificial neural network (ANN) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy.


METHODS
The data were collected prospectively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals, Leuven, between August 1994 and August 1996. The outcome measure was the histological classification of excised tissues as malignant (including borderline) or benign.


METHODS
Age, menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables. The ANNs were trained on a randomly selected set of 116 patient records and tested on the remainder (n = 57). The performance of each model was evaluated using receiver operating characteristic (ROC) curves and compared with corresponding data from an established risk of malignancy index (RMI) and a logistic regression model.


RESULTS
There were 124 benign masses, five of borderline malignancy and 44 invasive cancers (of which 29% were metastatic); 37% of patients with a malignant or borderline tumor had stage I disease. The best ANN gave an area under the ROC curve of 0.979 for the whole dataset, a sensitivity of 95.9% and specificity of 93.5%. The corresponding values for the RMI were 0.882, 67.3% and 91.1%, and for the logistic regression model 0.956, 95.9% and 85.5%, respectively.


CONCLUSIONS
An ANN can be trained to provide clinically accurate information, on whether or not an adnexal mass is malignant, from the patient's menopausal status, serum CA 125 levels, and some simple ultrasonographic criteria.
Timmerman
D
D
Department of Obstetrics and Gynecology, University Hospitals Leuven, Belgium.
Verrelst
H
H
Bourne
T H
TH
De Moor
B
B
Collins
W P
WP
Vergote
I
I
Vandewalle
J
J
D000291:Adnexal Diseases / Q000097:blood / Q000000981:diagnostic imaging* / Q000601:surgery; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018394:CA-125 Antigen / Q000097:blood; D003198:Computer Simulation; D016002:Discriminant Analysis; D019160:Endosonography; D005260:Female; D006801:Humans; D008875:Middle Aged; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000097:blood / Q000000981:diagnostic imaging / Q000601:surgery; D011237:Predictive Value of Tests; D011300:Preoperative Care; D011446:Prospective Studies; D012372:ROC Curve; D011897:Random Allocation; D015203:Reproducibility of Results; D018615:Ultrasonography, Doppler, Color; D014621:Vagina / Q000000981:diagnostic imaging
D018394:CA-125 Antigen