Clinical utility of risk modelsto refer patients with adnexal masses to specialized oncology care : Multicenter external validation using decision curve analysis
(2017) In Clinical Cancer Research 23(17). p.5082-5090- 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.... (More)
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.
(Less)
- author
- Wynants, Laure
; Timmerman, Dirk
; Verbakel, Jan Y.
; Testa, Antonia
; Savelli, Luca
; Fischerova, Daniela
; Franchi, Dorella
; Van Holsbeke, Caroline
; Epstein, Elisabeth
; Froyman, Wouter
; Guerriero, Stefano
; Rossi, Alberto
; Fruscio, Robert
; Leone, Francesco P.G.
; Bourne, Tom
; Valentin, Lil
LU
and Van Calster, Ben
(Less) - organization
- publishing date
- 2017-09-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- risk models, Adnexal Masses, oncology care
- in
- Clinical Cancer Research
- volume
- 23
- issue
- 17
- pages
- 9 pages
- publisher
- American Association for Cancer Research
- external identifiers
-
- scopus:85029542806
- pmid:28512173
- pmid:28512173
- wos:000409037300015
- ISSN
- 1078-0432
- DOI
- 10.1158/1078-0432.CCR-16-3248
- language
- English
- LU publication?
- yes
- id
- 975f6c5a-6152-4dae-8ae5-8bca12340a02
- date added to LUP
- 2017-10-10 11:14:16
- date last changed
- 2024-02-13 08:35:51
@article{975f6c5a-6152-4dae-8ae5-8bca12340a02,
abstract = {{<p>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.</p>}},
author = {{Wynants, Laure and Timmerman, Dirk and Verbakel, Jan Y. and Testa, Antonia and Savelli, Luca and Fischerova, Daniela and Franchi, Dorella and Van Holsbeke, Caroline and Epstein, Elisabeth and Froyman, Wouter and Guerriero, Stefano and Rossi, Alberto and Fruscio, Robert and Leone, Francesco P.G. and Bourne, Tom and Valentin, Lil and Van Calster, Ben}},
issn = {{1078-0432}},
keywords = {{risk models; Adnexal Masses; oncology care}},
language = {{eng}},
month = {{09}},
number = {{17}},
pages = {{5082--5090}},
publisher = {{American Association for Cancer Research}},
series = {{Clinical Cancer Research}},
title = {{Clinical utility of risk modelsto refer patients with adnexal masses to specialized oncology care : Multicenter external validation using decision curve analysis}},
url = {{http://dx.doi.org/10.1158/1078-0432.CCR-16-3248}},
doi = {{10.1158/1078-0432.CCR-16-3248}},
volume = {{23}},
year = {{2017}},
}