Reducing false-positive screening MRI rate in women with extremely dense breasts using prediction models based on data from the DENSE trial
den Dekker, Bianca M.; Bakker, Marije F.; de Lange, Stéphanie V.; Veldhuis, Wouter B.; van Diest, Paul J.; Duvivier, Katya M.; Lobbes, Marc B.I.; Loo, Claudette E.; Mann, Ritse M.; Monninkhof, Evelyn M.; Veltman, Jeroen; Pijnappel, Ruud M.; van Gils, Carla H.
(2021) Radiology, volume 301, issue 2, pp. 283 - 292
(Article)
Abstract
Background High breast density increases breast cancer risk and lowers mammographic sensitivity. Supplemental MRI screening improves cancer detection but increases the number of false-positive screenings. Thus, methods to distinguish true-positive MRI screening results from false-positive ones are needed. Purpose To build prediction models based on clinical characteristics and MRI findings
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to reduce the rate of false-positive screening MRI findings in women with extremely dense breasts. Materials and Methods Clinical characteristics and MRI findings in Dutch breast cancer screening participants (age range, 50-75 years) with positive first-round MRI screening results (Breast Imaging Reporting and Data System 3, 4, or 5) after a normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the randomized controlled Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial from December 2011 through November 2015. In this secondary analysis, prediction models were built using multivariable logistic regression analysis to distinguish true-positive MRI screening findings from false-positive ones. Results Among 454 women (median age, 52 years; interquartile range, 50-57 years) with a positive MRI result in a first supplemental MRI screening round, 79 were diagnosed with breast cancer (true-positive findings), and 375 had false-positive MRI results. The full prediction model (area under the receiver operating characteristics curve [AUC], 0.88; 95% CI: 0.84, 0.92), based on all collected clinical characteristics and MRI findings, could have prevented 45.5% (95% CI: 39.6, 51.5) of false-positive recalls and 21.3% (95% CI: 15.7, 28.3) of benign biopsies without missing any cancers. The model solely based on readily available MRI findings and age had a comparable performance (AUC, 0.84; 95% CI: 0.79, 0.88; P = .15) and could have prevented 35.5% (95% CI: 30.4, 41.1) of false-positive MRI screening results and 13.0% (95% CI: 8.8, 18.6) of benign biopsies. Conclusion Prediction models based on clinical characteristics and MRI findings may be useful to reduce the false-positive first-round screening MRI rate and benign biopsy rate in women with extremely dense breasts. Clinical trial registration no. NCT01315015 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco in this issue.
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Keywords: Aged, Breast/diagnostic imaging, Breast Density, Breast Neoplasms/diagnostic imaging, False Positive Reactions, Female, Humans, Magnetic Resonance Imaging/methods, Middle Aged, Netherlands, Prospective Studies, Reproducibility of Results, Sensitivity and Specificity, Radiology Nuclear Medicine and imaging, Research Support, Non-U.S. Gov't, Randomized Controlled Trial, Multicenter Study, Journal Article
ISSN: 0033-8419
Publisher: Radiological Society of North America Inc.
Note: Funding Information: Supported by the University Medical Center Utrecht (project number: UMCU DENSE), the Netherlands Organization for Health Research and Development (project numbers: ZonMW-200320002-UMCU and ZonMW Preventie 50-53125-98-014), the Dutch Cancer Society (project numbers: DCS-UU-2009-4348, UU-2014-6859, and UU-2014-7151), the Dutch Pink Ribbon—A Sister’s Hope (project number: Pink Ribbon-10074), Bayer Pharmaceuticals (project number: BSP-DENSE), and Stichting Kankerpreventie Midden-West. For research purposes, Volpara Health Technologies provided Volpara Imaging Software, version 1.5, for installation on servers in the screening units. Funding Information: Author contributions: Guarantor of integrity of entire study, B.M.d.D., C.E.L., C.H.v.G.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, B.M.d.D., W.B.V., C.E.L., R.M.P.; clinical studies, B.M.d.D., M.F.B., S.V.d.L., W.B.V., K.M.D., M.B.I.L., C.E.L., R.M.M., E.M.M., J.V., R.M.P., C.H.v.G.; statistical analysis, B.M.d.D., M.F.B., C.H.v.G.; and manuscript editing, all authors Disclosures of Conflicts of Interest: B.M.d.D. disclosed no relevant relationships. M.F.B. disclosed no relevant relationships. S.V.d.L. Activities related to the present article: institution received a research grant from Bayer. Activities not related to the present article: disclosed no relevant relationships. Other relation- ships: disclosed no relevant relationships. W.B.V. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is the cofounder and minority shareholder of Quantib-U BV. Other relationships: disclosed no relevant relationships. P.J.v.D. disclosed no relevant relationships. K.M.D. disclosed no relevant relationships. M.B.I.L. disclosed no relevant relationships. C.E.L. disclosed no relevant relationships. R.M.M. Activities related to the present article: institution received a grant from Bayer Healthcare. Activities not related to the present article: is a consultant for Transonic Imaging; institution received grants from Bayer Healthcare, Siemens Healthineers, Medtronic, BD, Koning, and Seno Medical; gave lectures for Bayer Healthcare, Siemens Healthineers, Screenpoint Medical, BD, Seno Medical, and Transonic Imaging. Other relationships: disclosed no relevant relationships. E.M.M. disclosed no relevant relationships. J.V. Activities related to the present article: institution received a grant from Bayer Healthcare. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. R.M.P. Activities related to the present article: institution received a grant from Bayer Healthcare. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. C.H.v.G. Activities related to the present article: institution received grants from the Netherlands Organization for Health Research and Development (project no. ZonMW-200320002-UMCU, ZonMW Preventie 50-53125-98-014), the Dutch Cancer Society (project no. DCS-UU-2009-4348, UU-2014-6859, and UU-2014-7151), the Dutch Pink Ribbon–A Sister’s Hope (project no. Pink Ribbon-10074), Bayer Pharmaceuticals (project o. BSP-DENSE), and Stichting Kankerpreventie Midden-West; received a consulting fee from Bayer Pharmaceuticals; received travel support from Bayer Pharmaceuticals. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. Publisher Copyright: © 2021 Radiological Society of North America Inc.. All rights reserved.
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