Abstract
AIM: To develop and validate a biomarker-based index to optimize referral and diagnosis of patients with suspected ovarian cancer. Furthermore, to compare this new index with the Risk of Malignancy Index (RMI) and Risk of Ovarian Malignancy Algorithm (ROMA).
PATIENTS AND METHODS: A training study, consisting of patients with benign ovarian disease (n=809) and ovarian cancer (n=246), was used to develop the Copenhagen Index (CPH-I) utilizing the variables serum HE4, serum CA125 and patient age. Eight international studies provided the validation population; comprising 1060 patients with benign ovarian masses and 550 patients with ovarian cancer.
RESULTS: Overall, 2665 patients were included. CPH-I was highly significant in discriminating benign from malignant ovarian disease. At the defined cut-off of 0.070 for CPH-I the sensitivity and specificity were 95.0% and 78.4% respectively in the training cohort and 82.0% and 88.4% in the validation cohort. Comparison of CPH-I, ROMA and RMI demonstrated area-under-curve (AUC) at 0.960, 0.954 and 0.959 respectively in the training study and 0.951, 0.953 and 0.935 respectively in the validation study. Using a sensitivity of 95.0%, the specificities for CPH-I, ROMA and RMI in the training cohort were 78.4%, 71.7% and 81.5% respectively, and in the validation cohort 67.3%, 70.7% and 69.5% respectively.
CONCLUSION: All three indices perform well at the clinically relevant sensitivity of 95%, but CPH-I, unlike RMI and ROMA, is independent of ultrasound and menopausal status, and may provide a simple index to optimize referral of women with suspected ovarian cancer.
Original language | English |
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Journal | Gynecologic Oncology |
Volume | 138 |
Issue number | 3 |
Pages (from-to) | 640-6 |
Number of pages | 7 |
ISSN | 0090-8258 |
DOIs | |
Publication status | Published - 1 Sept 2015 |
Keywords
- Adolescent
- Adult
- Age Factors
- Aged
- Aged, 80 and over
- Algorithms
- Biomarkers, Tumor
- CA-125 Antigen
- Cohort Studies
- Diagnosis, Differential
- Female
- Humans
- Membrane Proteins
- Middle Aged
- Models, Statistical
- Multivariate Analysis
- Ovarian Diseases
- Ovarian Neoplasms
- Prospective Studies
- Proteins
- Severity of Illness Index
- Young Adult