Preoperative sensitivity and specificity for early-stage ovarian cancer when combining cancer antigen CA-125II, CA 15-3, CA 72-4, and macrophage colony-stimulating factor using mixtures of multivariate normal distributions.

PURPOSE

In CA-125-based ovarian cancer screening trials, overall specificity and screening sensitivity of ultrasound after an elevated CA-125 exceeded 99.6% and 70%, respectively, thereby yielding a positive predictive value (PPV) exceeding 10%. However, sensitivity for early-stage disease was only 40%. This study aims to increase preoperative sensitivity for early-stage ovarian cancer while maintaining the annual referral rate to ultrasound at 2% by combining information across CA-125II, CA 15-3, CA 72-4, and macrophage colony-stimulating factor (M-CSF). For direct comparisons between marker panels, all sensitivity results correspond to a 98% fixed first-line specificity (referral rate 2%).

PATIENTS AND METHODS

Logistic regression, classification tree, and mixture discriminant analysis (MDA) models were fit to a training data set of preoperative serum measurements (63 patients, 126 healthy controls) from one center. Estimates from the training set applied to an independent validation set (60 stage I to II patients, 98 healthy controls) from two other centers provided unbiased estimates of sensitivity.

RESULTS

Preoperative sensitivities for early-stage disease of the optimal panels were 45% for CA-125II; 67% for CA-125II and CA 72-4; 70% for CA-125II, CA 72-4, and M-CSF; and 68% for all four markers (latter two results using MDA).

CONCLUSION

Efficiently combining information on CA-125II, CA 72-4, and M-CSF significantly increased preoperative early-stage sensitivity from 45% with CA-125II alone to 70%, while maintaining 98% first-line specificity. Screening trials with these markers using MDA followed by referral to ultrasound may maintain previously high levels of specificity and PPV, while significantly increasing early-stage screening sensitivity. MDA is a useful, biologically justified method for combining biomarkers.