Approximately 15% of breast cancers are diagnosed after the patient undergoes screening mammography with negative results and before the next recommended screening examination.1 These interval cancers (cases of cancer diagnosed during the interval between examinations) include both cancers that were present during screening mammography but were missed on examination and rapidly growing cancers that present symptomatically and tend to have a poorer prognosis than cancers detected during screening.1-3 Identifying women who are at high risk of breast cancer with a poor prognosis despite regular screening mammography could enable targeted supplemental screening for women for whom screening mammography may not be sufficient. This study describes the incidence of and risk factors associated with breast cancer with a poor prognosis after screening mammography with negative findings.
Mammography data were obtained from the Population-Based Research Optimizing Screening Through Personalized Regimens (PROSPR) consortium,4 which includes Dartmouth Hitchcock Medical Center (Lebanon, New Hampshire), Brigham and Women’s Hospital (Boston, Massachusetts), University of Pennsylvania Health System (Philadelphia), and Vermont Breast Cancer Surveillance System (Burlington). The institutional review boards of Dartmouth Hitchcock Medical Center, Brigham and Women’s Hospital, the University of Pennsylvania Health System, and the University of Vermont approved the study and waived the need for informed consent. The study population included women 40 years or older with no earlier diagnosis of breast cancer who received screening mammography between 2011 and 2014. Mammograms with negative results were those with an initial Breast Imaging Reporting and Data System category 1 or 2; mammograms in all other categories were classified as having positive results. Cancer diagnoses within 1 year after screening mammography were obtained from state cancer registries. Breast cancer cases with a poor prognosis were defined as cases of cancer meeting any of the following criteria: distant metastases; cancer-positive regional lymph nodes; estrogen receptor–positive and/or progesterone receptor–positive and HER2-negative invasive cancer 2 cm or more in diameter; estrogen receptor–negative, progesterone receptor–negative, HER2-negative (triple-negative) invasive cancer 1 cm or more in diameter; or HER2-positive invasive cancer 1 cm or more in diameter. This definition is the primary outcome for the ongoing Tomosynthesis Mammographic Imaging Screening Trial.5 Multivariable logistic regression was used to assess the association of age, breast density, and family history with cancer diagnosis. SAS software, version 9.4 (SAS Institute Inc) was used for all analyses. P ≤ .05 was considered statistically significant, and all P values were 2-sided.
Table 1 provides the incidence of breast cancer among 306 028 women 40 years or older who had no earlier diagnosis of breast cancer and underwent mammography screening between 2011 and 2014. Cases of cancer diagnosed after screening mammography with negative results were more likely to be associated with a poor prognosis (43.8%) than those diagnosed after mammography with positive results (26.9%). Among all women with negative mammography results (Table 2, Model 1), women with dense breasts had twice the odds of receiving a cancer diagnosis (irrespective of prognosis) compared with women with nondense breasts (odds ratio [OR], 2.07; 95% CI, 1.48-2.89; P = .02). Age and family history were not significantly associated with breast cancer diagnosis after negative screening mammography results. However, among women who received a diagnosis of cancer after negative mammography results (Table 2, Model 2), younger age was associated with having cancer with a poor prognosis (patients aged 40-49 years vs those 70-89 years: OR, 3.52; 95% CI, 1.15-10.72; P = .048; P for trend = .005). Breast density and family history were not significantly associated with poor prognosis. In contrast, for women with positive screening mammogram results, breast density was not associated with any cancer diagnosis, but age (eg, patients aged 40-49 years vs those 70-89 years: OR, 0.22; 95% CI, 0.18-0.26; P < .001) and family history (positive vs negative family history: OR, 1.29; 95% CI, 1.12-1.48; P < .001) were (Table 2, Model 3), and none of these factors were associated with breast cancer with a poor prognosis (Table 2, Model 4).
Our results indicate that the positive predictive value of mammography results depends on patient age and family history but not on breast density. In contrast, the negative predictive value depends on breast density but not on patient age or family history. Although the rate of breast cancer after negative mammography results is small, the likelihood that such cases will be associated with a poor prognosis highlights the need to improve early detection for these women. Although breast density is predictive of interval cancer overall, it is less predictive of whether that interval cancer will have a poor prognosis. Younger age is predictive of interval breast cancer with a poor prognosis. This may reflect the dual origins of interval cancers. Cancers that are present but not detected by screening mammography may be more likely to be associated with a good prognosis and occur among older women, whereas cancers that develop between screening examinations may be more likely to be rapidly growing cancers associated with a poor prognosis and occur among younger women. Only a few patient characteristics were available, limiting the number of risk factors that were assessed. Breast density has received much attention as the primary factor identifying a need for supplemental screening, but considering both breast density and age may be more effective in identifying women who are at risk for breast cancer with a poor prognosis.
Accepted for Publication: January 26, 2018.
Correction: This article was corrected on July 12, 2018, to correct the corresponding author’s email address.
Corresponding Author: Anne Marie McCarthy, PhD, Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St, Boston, MA 02114 (firstname.lastname@example.org).
Published Online: May 3, 2018. doi:10.1001/jamaoncol.2018.0352
Author Contributions: Dr Barlow had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: All authors.
Acquisition, analysis, or interpretation of data: McCarthy, Barlow, Conant, Haas, Sprague, Armstrong.
Drafting of the manuscript: McCarthy, Barlow.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Barlow.
Obtained funding: Conant, Haas, Sprague, Armstrong.
Administrative, technical, or material support: Haas.
Study supervision: Conant, Haas, Li, Armstrong.
Conflict of Interest Disclosures: None reported.
Funding/Support: This work was supported by National Institutes of Health/National Cancer Institute Population-Based Research Optimizing Screening through Personalized Regimens (PROSPR) program grants U54CA163313, U54CA163303 (Dr Sprague), U54CA163307, and U01CA163304 (Dr Barlow).
Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Group Information: The members of the PROSPR consortium are Roberta M. diFlorio-Alexander, MD, MS, Departments of Radiology and Obstetrics and Gynecology, Geisel School of Medicine at Dartmouth; Despina Kontos, PhD, MSc, Department of Radiology, University of Pennsylvania Perelman School of Medicine; Tracy Onega, PhD, Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Departments of Biomedical Data Science and Epidemiology, Geisel School of Medicine at Dartmouth; Pamela Vacek, PhD, Departments of Medical Biostatistics and Pathology, University of Vermont; Donald L. Weaver, MD, Department of Pathology and the University of Vermont Cancer Center, University of Vermont; and Mitchell D. Schnall, MD, PhD, Department of Radiology, University of Pennsylvania Perelman School of Medicine.
CD. Breast cancer characteristics associated with digital versus film-screen mammography for screen-detected and interval cancers. AJR Am J Roentgenol
. 2015;205(3):676-684.PubMedGoogle ScholarCrossref
et al. Tumor characteristics associated with mammographic detection of breast cancer in the Ontario Breast Screening Program. J Natl Cancer Inst
. 2011;103(12):942-950.PubMedGoogle ScholarCrossref
et al. Risk factors and tumor characteristics of interval cancers by mammographic density. J Clin Oncol
. 2015;33(9):1030-1037.PubMedGoogle ScholarCrossref
et al; Population-Based Research Optimizing Screening Through Personalized Regimens consortium. Unifying screening processes within the PROSPR consortium: a conceptual model for breast, cervical, and colorectal cancer screening. J Natl Cancer Inst
. 2015;107(6):djv120.PubMedGoogle ScholarCrossref