Clinical utility of risk models to refer patients with adnexal masses to specialized oncology care: multicenter external validation using decision curve analysis
File(s)
Author(s)
Type
Journal Article
Abstract
Purpose: To evaluate the utility of preoperative diagnostic models for ovarian cancer based on ultrasound and/or biomarkers for referring patients to specialized oncology care. The investigated models were RMI, ROMA, and 3 models from the International Ovarian Tumor Analysis (IOTA) group [LR2, ADNEX, and the Simple Rules risk score (SRRisk)].Experimental Design: A secondary analysis of prospectively collected data from 2 cross-sectional cohort studies was performed to externally validate diagnostic models. A total of 2,763 patients (2,403 in dataset 1 and 360 in dataset 2) from 18 centers (11 oncology centers and 7 nononcology hospitals) in 6 countries participated. Excised tissue was histologically classified as benign or malignant. The clinical utility of the preoperative diagnostic models was assessed with net benefit (NB) at a range of risk thresholds (5%-50% risk of malignancy) to refer patients to specialized oncology care. We visualized results with decision curves and generated bootstrap confidence intervals.Results: The prevalence of malignancy was 41% in dataset 1 and 40% in dataset 2. For thresholds up to 10% to 15%, RMI and ROMA had a lower NB than referring all patients. SRRisks and ADNEX demonstrated the highest NB. At a threshold of 20%, the NBs of ADNEX, SRrisks, and RMI were 0.348, 0.350, and 0.270, respectively. Results by menopausal status and type of center (oncology vs. nononcology) were similar.Conclusions: All tested IOTA methods, especially ADNEX and SRRisks, are clinically more useful than RMI and ROMA to select patients with adnexal masses for specialized oncology care. Clin Cancer Res; 1-9. ©2017 AACR.
Date Issued
2017-05-16
Date Acceptance
2017-05-09
Citation
Clinical Cancer Research, 2017, 23 (17), pp.5082-5090
ISSN
1557-3265
Publisher
American Association for Cancer Research
Start Page
5082
End Page
5090
Journal / Book Title
Clinical Cancer Research
Volume
23
Issue
17
Copyright Statement
© 2017 American Association for Cancer Research.
Identifier
PII: 1078-0432.CCR-16-3248
Subjects
Science & Technology
Life Sciences & Biomedicine
Oncology
MULTIVARIATE INDEX ASSAY
OVARIAN MALIGNANCY ALGORITHM
TUMOR-ANALYSIS-GROUP
PREDICTION MODELS
SIMPLE-RULES
IOTA GROUP
CANCER
ULTRASOUND
DIAGNOSIS
SURGERY
1112 Oncology And Carcinogenesis
Oncology & Carcinogenesis
Publication Status
Published