Strategies to Diagnose Ovarian Cancer: New Evidence from Phase 3 of the Multicentre International IOTA Study

Overview

Journal Br J Cancer

Specialty Oncology

Date 2014 Jun 18

PMID 24937676

Citations 45

Authors

A Testa

J Kaijser

L Wynants

D Fischerova

C Van Holsbeke

D Franchi

L Savelli

E Epstein

A Czekierdowski

S Guerriero

R Fruscio

F P G Leone

I Vergote

T Bourne

L Valentin

B Van Calster

D Timmerman

Affiliations

Soon will be listed here.

Abstract

Background: To compare different ultrasound-based international ovarian tumour analysis (IOTA) strategies and risk of malignancy index (RMI) for ovarian cancer diagnosis using a meta-analysis approach of centre-specific data from IOTA3.

Methods: This prospective multicentre diagnostic accuracy study included 2403 patients with 1423 benign and 980 malignant adnexal masses from 2009 until 2012. All patients underwent standardised transvaginal ultrasonography. Test performance of RMI, subjective assessment (SA) of ultrasound findings, two IOTA risk models (LR1 and LR2), and strategies involving combinations of IOTA simple rules (SRs), simple descriptors (SDs) and LR2 with and without SA was estimated using a meta-analysis approach. Reference standard was histology after surgery.

Results: The areas under the receiver operator characteristic curves of LR1, LR2, SA and RMI were 0.930 (0.917-0.942), 0.918 (0.905-0.930), 0.914 (0.886-0.936) and 0.875 (0.853-0.894). Diagnostic one-step and two-step strategies using LR1, LR2, SR and SD achieved summary estimates for sensitivity 90-96%, specificity 74-79% and diagnostic odds ratio (DOR) 32.8-50.5. Adding SA when IOTA methods yielded equivocal results improved performance (DOR 57.6-75.7). Risk of Malignancy Index had sensitivity 67%, specificity 91% and DOR 17.5.

Conclusions: This study shows all IOTA strategies had excellent diagnostic performance in comparison with RMI. The IOTA strategy chosen may be determined by clinical preference.

Citing Articles

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Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm.

Ledger A, Ceusters J, Valentin L, Testa A, Van Holsbeke C, Franchi D BMC Med Res Methodol. 2023; 23(1):276.

PMID: 38001421 PMC: 10668424. DOI: 10.1186/s12874-023-02103-3.

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Zhou S, Guo Y, Wen L, Liu J, Fu Y, Xu F BMC Med Imaging. 2023; 23(1):190.

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Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics.

Brincat M, Mira A, Lawrence A Diagnostics (Basel). 2023; 13(21).

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