Reducing False-Positive Screening MRI Rates in Women with Extremely Dense Breasts

HomeRadiologyVol. 301, No. 2 PreviousNext Reviews and CommentaryFree AccessEditorialReducing False-Positive Screening MRI Rates in Women with Extremely Dense BreastsMassimo Imbriaco Massimo Imbriaco Author AffiliationsFrom the Department of Advanced Biomedical Sciences, University of Naples Federico II, Via Pansini 5, 80131 Naples, Italy.Address correspondence to the author (e-mail: [email protected]).Massimo Imbriaco Published Online:Aug 17 2021https://doi.org/10.1148/radiol.2021211547MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In See also the article by den Dekker et al in this issue.Massimo Imbriaco, MD, was board certified in nuclear medicine in 1994 and in radiology in 1999 at the University of Naples Federico II. He then completed two research fellowships in the Nuclear Medicine Department at Memorial Sloan-Kettering Cancer Center and the Radiology Department at New York University. In 2015, he joined the Department of Advanced Biomedical Sciences of the University of Naples Federico II as associate professor of radiology. Dr Imbriaco has long-standing experience in body imaging with an emphasis on cardiovascular, breast, and prostatic imaging and has lectured extensively both within Europe and internationally, authoring over 220 peer-reviewed articles.Download as PowerPointOpen in Image Viewer Breast cancer mammographic screening has proven effective in the identification of women with asymptomatic cancer. The goal of screening is to catch cancer at an earlier stage before it progresses, enabling women to undergo less invasive treatments with better outcomes (1). However, the presence of dense breast tissue reduces the sensitivity of screening mammography in the detection of breast cancer. Women with dense breasts have three to five times greater lifetime risk of developing breast cancer than women with mostly fatty breasts (2).Among all breast imaging modalities, breast MRI offers the highest cancer detection rate. There is increasing evidence for use of MRI to screen not only the small proportion of women with very high risk of breast cancer but also women with average risk (3). The effectiveness of breast MRI in screening women with extremely dense breasts has been investigated in the American College of Radiology Imaging Network (ACRIN) 6666 trial. The ACRIN trial included women with heterogeneously or extremely dense breasts who had at least one risk factor (4). The study reported a sensitivity of 31.3% for mammography alone, which improved to 100% with the addition of MRI. However, the improvement in sensitivity was accompanied by a reduction in specificity for mammography combined with MRI (70.6%). The Dense Tissue and Early Breast Neoplasm Screening (DENSE) trial is a parallel-group randomized trial designed to investigate the cost-effectiveness of biennial screening with mammography and MRI compared with that of mammography alone in Dutch women aged 50–75 years with extremely dense breasts (5).The results of the DENSE trial have shown that supplemental MRI screening in women with extremely dense breasts and normal mammography results improves detection of clinically relevant cancers, resulting in the diagnosis of fewer interval cancers than mammography alone during a 2-year screening period (6).The moderate specificity of breast MRI can result in false-positive findings that necessitate further imaging, tissue sampling, or additional follow-up examinations. Several screening MRI studies have reported a false-positive rate (FPR) ranging from 52 per 1000 cases to 97 per 1000 cases; thus, FPR reduction is an important issue when considering use of breast MRI as a screening tool (3).In this issue of Radiology, den Dekker et al (7) propose prediction models based on patient and MRI characteristics to reduce the false-positive screening MRI rate in women with extremely dense breasts. Patient and MRI characteristics of 454 women (age range, 50–75 years) with a positive first-round MRI screening result (Breast Imaging Reporting and Data System [BI-RADS] 3, 4, or 5) after normal screening mammography with extremely dense breasts (Volpara density category 4) were prospectively collected within the DENSE trial. Of the 454 women with a positive MRI result in the first supplemental MRI screening round, 79 were diagnosed with breast cancer and 375 women had false-positive results.Multivariable logistic regression analysis based on participants with complete data was used to build prediction models to distinguish true-positive from false-positive findings. The full prediction model (model A, area under the receiver operating characteristic curve [AUC] = 0.88), which was based on all collected patient and MRI characteristics, could have prevented 45.5% of false-positive recalls and 21.3% of benign biopsies after first-round supplemental MRI screening in women with extremely dense breast tissue, without missing any cancers.Among the identified false-positive findings were 35 of 164 (21%) participants who underwent biopsy for a benign lesion. A second reduced prediction model (model B) included the variables age, menopausal status (perimenopausal vs postmenopausal), number of first-degree relatives with breast cancer, history of breast biopsy, MRI BIRADS classification (BI-RADS 4 vs BI-RADS 3, odds ratio = 33; BI-RADS 5 vs BI-RADS 3, odds ratio = 710), and initial phase kinetics (rapid versus slow). The AUC of the reduced prediction model was 0.85, with 85 of 264 (32.2%) false-positive MRI screening results that could have been identified, without missing any cancers. Among the identified participants with false-positive findings were 17 of 164 (10%) participants who underwent biopsy for a benign lesion. Finally, model C was solely based on readily available MRI characteristics and age, including 369 participants with complete data (AUC = 0.84). For this model, 107 of 301 (35.5%) false-positive MRI screening results and 13% of benign biopsies could have been prevented without missing any cancers. Among the identified participants with false-positive findings were 24 of 185 (13%) participants who underwent biopsy for a benign lesion.A wide range of breast cancer risk population models have been developed with varying degrees of clinical implementation and utility (8). However, these models generally show poor discriminatory accuracy and limited utility in predicting an individual’s breast cancer risk.Furthermore, few articles have correlated patient and MRI characteristics in relation to false-positive MRI screening results. Vreeman et al reported an increased FPR in first-round MRI screening in women with a high amount of fibroglandular tissue and high background parenchymal enhancement (BPE) (9). Baltzer et al reported that nonmass lesions were the major cause of false-positive breast MRI findings and that BI-RADS descriptors are not sufficient to differentiate benign from malignant nonmass lesions (10). In the study by den Dekker et al (7), which included women with extremely dense breasts, the amount of fibroglandular tissue and BPE was not associated with a higher FPR. As stated by den Dekker and colleagues (7), this difference may partly be explained by the different study samples and methods of BPE measurements (visual inspection as used in DENSE trial vs an automated tool). However, this could represent a possible limitation of the study, since an automated method for BPE analysis might be beneficial, allowing clinicians to obtain more accurate and reproducible measurements. Another important caveat of the study is that the proposed multivariable model was built using data from the first round of DENSE participants. The model needs further validation in large multicenter trials to be integrated into a clinical diagnostic workflow.Major strengths of the study by den Dekker et al include the study design, the large sample size, and the use of a multivariable statistical analysis. The authors use a novel and original approach that combines patient and MRI characteristics to lower FPR at MRI. As suggested by the authors, although the full model (model A), which was based on all patient and MRI characteristics, yielded the highest FPR reduction, such a model would require the investment of a substantial amount of time, money, and effort to collect patient data, thus raising the limited applicability of such a model in daily practice. However, model C, based solely on MRI characteristics and age, yielded a comparable reduction in FPR. As this model is based on readily available data, its implementation in clinical practice could be relatively easy.In conclusion, the applicability of breast MRI as a screening tool is a challenging and expanding research area. The study by den Dekker et al (7) nicely proposes innovative prediction models based on patient and MRI characteristics that allow reduction of the false-positive first-round screening MRI rate and therefore the number of benign biopsies. These results certainly require additional studies and validation in multicenter trials before being integrated into clinical practice. This is a first important step to the possible implementation of breast MRI as a screening tool in women with dense breasts.Disclosures of Conflicts of Interest: M.I. disclosed no relevant relationships.References1. Fuller MS, Lee CI, Elmore JG. Breast cancer screening: an evidence-based update. Med Clin North Am 2015;99(3):451–468. Crossref, Medline, Google Scholar2. Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med 2007;356(3):227–236. Crossref, Medline, Google Scholar3. Kuhl CK, Strobel K, Bieling H, Leutner C, Schild HH, Schrading S. Supplemental Breast MR Imaging Screening of Women with Average Risk of Breast Cancer. Radiology 2017;283(2):361–370. Link, Google Scholar4. Berg WA, Zhang Z, Lehrer D, et al. Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. JAMA 2012;307(13):1394–1404. Crossref, Medline, Google Scholar5. Emaus MJ, Bakker MF, Peeters PH, et al. MR Imaging as an Additional Screening Modality for the Detection of Breast Cancer in Women Aged 50-75 Years with Extremely Dense Breasts: The DENSE Trial Study Design. Radiology 2015;277(2):527–537. Link, Google Scholar6. Bakker MF, de Lange SV, Pijnappel RM, et al. Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N Engl J Med 2019;381(22):2091–2102. Crossref, Medline, Google Scholar7. den Dekker BM, Bakker MF, de Lange SV, et al. Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE Trial. Radiology 2021. https://doi.org/10.1148/radiol.2021210325. Published online August 17, 2021. Link, Google Scholar8. Cintolo-Gonzalez JA, Braun D, Blackford AL, et al. Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 2017;164(2):263–28.[Published correction appears in Breast Cancer Res Treat 2017;164(3):745.]. Crossref, Medline, Google Scholar9. Vreemann S, Dalmis MU, Bult P, et al. Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program : A retrospective cohort study. Eur Radiol 2019;29(9):4678–4690. Crossref, Medline, Google Scholar10. Baltzer PA, Benndorf M, Dietzel M, Gajda M, Runnebaum IB, Kaiser WA. 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Crossref, Medline, Google ScholarArticle HistoryReceived: June 18 2021Revision requested: June 29 2021Revision received: June 30 2021Accepted: July 2 2021Published online: Aug 17 2021Published in print: Nov 2021 FiguresReferencesRelatedDetailsCited ByManagement of Non-Mass Enhancement at Breast Magnetic Resonance in Screening Settings Referred for Magnetic Resonance-Guided BiopsyEduardode Faria Castro Fleury, CaioCastro, Mario Sergio Camposdo Amaral, DécioRoveda Junior2022 | Breast Cancer: Basic and Clinical Research, Vol. 16Accompanying This ArticleReducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE TrialAug 17 2021RadiologyRecommended Articles Reducing False-Positive Screening MRI Rate in Women with Extremely Dense Breasts Using Prediction Models Based on Data from the DENSE TrialRadiology2021Volume: 301Issue: 2pp. 283-292Mammographic Breast Density, Benign Breast Disease, and Subsequent Breast Cancer Risk in 3.9 Million Korean WomenRadiology2022Volume: 304Issue: 3pp. 534-541Closing the Chapter on Supplemental Breast Cancer Screening with USRadiology2021Volume: 298Issue: 3pp. 576-577BI-RADS Terminology for Mammography Reports: What Residents Need to KnowRadioGraphics2019Volume: 39Issue: 2pp. 319-320US and Digital Breast Tomosynthesis in Women with Focal Breast Complaints: Results of the Breast US Trial (BUST)Radiology2023Volume: 307Issue: 4See More RSNA Education Exhibits Certifications, Audits, And National Benchmarks: Breaking Down The Basics For The New Mammography AttendingDigital Posters20212022 New Trends in Breast Density - What Should We Know?Digital Posters2022Calling The Shots: What Radiologists Need To Know About Axillary Lymphadenopathy In The COVID EraDigital Posters2021 RSNA Case Collection BI-RADS 4C - High suspicion for malignancyRSNA Case Collection2022Invasive Lobular CarcinomaRSNA Case Collection2021Invasive ductal carcinoma as developing asymmetryRSNA Case Collection2021 Vol. 301, No. 2 Metrics Altmetric Score PDF download