Artificial neural network computer prediction of ovarian malignancy in women with adnexal masses.
OBJECTIVE
Assessment of the usefulness of a neural model to predict which ovarian tumors are malignant.


METHODS
Age, menopausal status, body mass index, grayscale and Doppler ultrasonographic features, as well as levels of specific markers (CA 125, tissue polypeptide specific antigen) were examined in 686 women with adnexal masses. The probability of malignancy was calculated using an artificial neural network software and the diagnostic efficiency of the received model was estimated using a receiver-operating characteristics (ROC) curve.


RESULTS
Of the 686 women, 431 (62.8%) had a benign and 255 (37.2%) had a malignant ovarian tumor. The significant malignancy predictors are age, menopausal status, maximum tumor diameter, internal wall structure of tumor, presence of septa and/or solid elements, tumor location, location of vessels, and blood flow indexes. The best network provided 96.0% sensitivity and 97.7% specificity. The area under the curve for the received model was 0.9716.


CONCLUSIONS
An artificial neural network model based on clinical and ultrasonographic data allows to calculate the probability of tumor malignancy.
Szpurek
D
D
Division of Gynecological Surgery, Karol Marcinkowski University of Medical Sciences, Poznan, Poland. kgo@gpsk.am.poznan.pl
Moszynski
R
R
Smolen
A
A
Sajdak
S
S
D000291:Adnexal Diseases / Q000000981:diagnostic imaging*; D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D002648:Child; D005260:Female; D006801:Humans; D008593:Menopause; D008875:Middle Aged; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000098:blood supply / Q000175:diagnosis*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler