Comparison of diagnostic usefulness of predictive models in preliminary differentiation of adnexal masses.
The purpose of this study was to compare prognostic models evaluating the probability of an ovarian cancer occurrence based on a number of clinical and ultrasonographic data in women with adnexal masses. A total of 686 women with adnexal masses underwent the examinations between 1994 and 2002. The recorded parameters included: age, menopausal status, body mass index, the grayscale and Doppler ultrasonographic examination, and selected markers concentration levels. In order to find the best combination of features, which significantly influences the probability of malignancy, stepwise logistic regression analysis, as well as artificial neural network, was used. The diagnostic efficiency of received models was estimated and compared using receiver-operating characteristics (ROC) curve. The results indicate that 431 and 255 patients had a benign and malignant ovarian tumor, respectively. Application of stepwise logistic regression analysis revealed statistically significant importance of eight features. The sensitivity and specificity for the received model were 65.71% and 77.59%, respectively. Three-layer perceptron network shows 13 features as significant predictors of malignancy. The network gave a sensitivity of 85.7% and specificity of 93.1%. Comparison of area under ROC curve for received models was 0.9679 vs 0.9716. Prognostic values of the analyzed neural model are not optimal but seem to surpass logistic regression model in terms of the predictive possibilities.
Moszynski
R
R
Division of Gynecological Surgery, Karol Marcinkowski University of Medical Sciences, 33 Polna Street, 60-535 Poznan, Poland.
Szpurek
D
D
Smolen
A
A
Sajdak
S
S
D000328:Adult; D000367:Age Factors; D000368:Aged; D002277:Carcinoma / Q000000981:diagnostic imaging* / Q000401:mortality / Q000473:pathology*; D003937:Diagnosis, Differential; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging* / Q000401:mortality / Q000473:pathology*; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012307:Risk Factors; D012680:Sensitivity and Specificity; D015996:Survival Rate; D014463:Ultrasonography