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161 Prediction of ovarian cancer using a multivariate assay: a randomized controlled trial to improve diagnostic strategies in filipino women (preliminary results of the overa study)
  1. C Velayo1,
  2. K Reforma2,
  3. R Sicam2,
  4. M Hernandez-Diwa1 and
  5. A Sy1
  1. 1University of the Philippines, Philippines
  2. 2Philippine General Hospital, Philippines


Introduction In our setting, conventional utilization of clinico-diagnostic, sonographic and single biochemical marker characteristics predominates the pre-operative evaluation of ovarian masses, while the value of multivariate assays has yet to be elucidated. In this study, a multivariate assay (OVERA®) was compared to singular and combined models for malignancy risk calculation.

Methods This is an ongoing randomized controlled trial using OVERA among Filipino women with ovarian masses in the University of the Philippines - Philippine General Hospital. A preliminary analysis comparing OVERA and other strategies for malignancy risk prediction was performed.

Results As of this report, 347 women have been enrolled in the study. Based on sonologic classifiers, high risk patients had higher predictive scores using LR1 (χ2[2]: 260.81, p<0.01), LR2 (χ2[2]: 271.57, p<0.01), Sassone (χ2[2]: 127.26, p<0.01), Lerner (χ2[2]: 153.98, p<0.01), IOTA-ADNEX (χ2[2]: 215.22, p<0.01), and ROMA (χ2[2]: 144.48, p<0.01). Based on categorical classifiers, CA-125 (χ2: 34.59, p<0.01), OVERA (χ2: 54.25, p<0.01), and ROMA (χ2: 85.29, p<0.01) discerned well in the high-risk group, while HE4 (χ2: 105.10, p<0.01) discerned lower risk better. OVERA as a numerical variable appears to fare well in detecting the likelihood of malignant tumors (χ2[4]: 3.38, p: 0.50) as compared to sonologic models but was not as discerning as other models using the conventional cut-off value (5.0). A higher cut-off (6.9) would confer more optimal values of specificity across age (pre-menopausal or menopausal) and histopathologic types.

Conclusion The preliminary results stress the importance of population based studies in evaluating biochemical assays for ovarian cancer risk prediction.

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