A comparison between an ultrasound based prediction model (LR2) and the risk of ovarian malignancy algorithm (ROMA) to assess the risk of malignancy in women with an adnexal mass.
OBJECTIVE
The identification of novel biomarkers led to the development of the ROMA algorithm incorporating both HE4 and CA125 to predict malignancy in women with a pelvic mass. An ultrasound based prediction model (LR2) developed by the International Ovarian Tumor Analysis (IOTA) study offers better diagnostic performance than CA125 alone. In this study we compared the diagnostic accuracy between LR2 and ROMA.


METHODS
This study included women with a pelvic mass scheduled for surgery and enrolled in a previous prospective diagnostic accuracy study. Experienced ultrasound examiners, general gynecologists and trainees supervised by one of the experts performed the preoperative transvaginal ultrasound examinations. Serum biomarkers were taken prior to surgery. Accuracy of LR2 and ROMA was estimated at completion of this study and did not form part of the decision making process. Final outcome was histology of removed tissues and surgical stage if relevant.


RESULTS
In total 360 women were evaluated. 216 women had benign disease and 144 a malignancy. Overall test performance of LR2 (AUC 0.952) with 94% sensitivity and 82% specificity was significantly better than ROMA (AUC 0.893) with 84% sensitivity and 80% specificity. Difference in AUC was 0.059 (95% CI: 0.026-0.091; P-value 0.0004). Similar results were obtained when stratified for menopausal status.


CONCLUSIONS
LR2 shows a better diagnostic performance than ROMA for the characterization of a pelvic mass in both pre- and postmenopausal women. These findings suggest that HE4 and CA125 may not play an important role in the diagnosis of ovarian cancer if good quality ultrasonography is available.
Academic Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
Jeroen
J
Kaijser
Toon
T
Van Gorp
Kirsten
K
Van Hoorde
Caroline
C
Van Holsbeke
Ahmad
A
Sayasneh
Ignace
I
Vergote
Tom
T
Bourne
Dirk
D
Timmerman
Ben
B
Van Calster
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood; D018394:CA-125 Antigen / Q000097:blood*; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D005185:Fallopian Tube Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D016015:Logistic Models; D008565:Membrane Proteins / Q000097:blood*; D010051:Ovarian Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging; D010053:Ovary / Q000000981:diagnostic imaging*; D011506:Proteins / Q000378:metabolism*; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D014463:Ultrasonography; D000081102:WAP Four-Disulfide Core Domain Protein 2
D014408:Biomarkers, Tumor; D018394:CA-125 Antigen; C451759:MUC16 protein, human; D008565:Membrane Proteins; D011506:Proteins; D000081102:WAP Four-Disulfide Core Domain Protein 2; C569698:WFDC2 protein, human