[Use of multilayer perception artificial neutral networks for the prediction of the probability of malignancy in adnexal tumors].
BACKGROUND
Advanced statistical methods are currently are more often used in the prediction of ovarian malignancy when adnexal tumor is detected. These methods include logistic regression analysis and artificial neural networks (ANN's), i.e. computer programs which are capable of learning from presented data and further predict various events, such as clinical diagnosis or outcome of a given treatment.


METHODS
We have analyzed data of 307 women with adnexal tumors who were operated in the Ist Dept. of Gynecology, Medical University in Lublin between 2000-2002. Following clinical and sonographic variables were included: age, menopausal status, serum CA-125, bilaterality, tumor size and volume, papillary projections, septa, solid parts presence, Doppler blood flow indices (PI, RI, Vmax), and subjective-color Doppler score. A multiple layer perceptron (MLP) neural network with 13 input variables, 11 hidden neurons and one output variable was constructed to assess probability of malignancy in each women (Statistica v. 6.0 for Windows, Statsoft, USA). Sensitivity, specificity and accuracy of the model were calculated. Receiver-Operating Characteristics curves were generated and corresponding Areas Under ROC Curves (AUROC's) for all diagnostic tests were compared.


RESULTS
Final histologic examination revealed 228 (74.3%) benign tumors and 79 (25.7%) malignant masses including 21 women with FIGO stage I ovarian cancer. With a 75% cut-off probability of malignancy level the sensitivity and specificity of the best network in the testing set was 96.7% and 100%, respectively. In the validation set the corresponding values of sensitivity and specificity were 82.3% and 97.5%. The highest of all used tests AUROC equal to 0.9749 was found for the ANN predictive model.


CONCLUSIONS
ANN may help in the extraction of the most useful predictive clinical and ultrasound data. The sensitivity and specificity of the ANN's generated model were higher than currently used single clinical and diagnostic tests. However, a prospective testing in a new, much larger group of women with adnexal tumors is essential for the clinical usefulness of the proposed statistical model.
Smoleń
Agata
A
Zakładu Matematyki i Biostatystyki Medycznej AM w Lublinie.
Czekierdowski
Artur
A
Stachowicz
Norbert
N
Kotarski
Jan
J
D000328:Adult; D000368:Aged; D003198:Computer Simulation / Q000592:standards; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D012680:Sensitivity and Specificity; D018615:Ultrasonography, Doppler, Color / Q000379:methods