Rationale and Objectives
The addition of digital breast tomosynthesis (DBT) to digital screening mammography (DM) has been shown to decrease recall rates and improve cancer detection rates, but there is a lack of data regarding the impact of DBT on rates of short-term follow-up. We assessed possible changes in performance measures with the introduction of DBT at our facility.
Materials and Methods
In our observational study, databases were used to compare rates of recall, short-term follow-up, biopsy, and cancer detection between women undergoing DM without ( n = 10,477) and women undergoing DM with ( n = 2304) the addition of DBT. Regression analysis was performed to determine associations with patient age, breast density, and availability of comparison examinations.
Results
The addition of DBT resulted in significantly lower recall rates (16%–14%, P = .017), higher rates of biopsy (12.7%–19.1%, P < .01), and increased detection of ductal carcinoma in situ, with a difference of 2.3 cases per 1000 screens ( P = .044). A 33% increase in cancer detection rates was observed with DBT, which did not reach statistical significance. Short-term follow-up of probably benign findings was 80% higher in the DBT group (odds ratio = 1.80, 95% confidence interval = 1.38–2.36, P < .001).
Conclusions
To our knowledge, we are the first to study the impact of DBT on rates of short-term follow-up, and observed an 80% increase over the DM group. Further research is needed to determine the malignancy rate of Breast Imaging Reporting and Data System 3 lesions detected with DBT, and establish appropriate follow-up to maximize cancer detection while minimizing expense and patient anxiety.
Introduction
Breast cancer screening with full-field digital mammography (DM) is currently the standard of care, and has been shown to reduce both morbidity and mortality . However, it is an imperfect technology and there are concerns regarding false-positive results and sensitivity, particularly in dense breasts. Estimated positive predictive values (PPVs) for screening mammography average 5% (range 4.4%–16.8%) , and false-negative rates for symptomatic women range from 8% to 66% depending on breast density and tumor type . It is thought that the large variability in interpretation performance between radiologists is partly owing to overlapping tissue , and superior sensitivity in fatty breasts is attributed to the ability to see lesion margins better, a key factor in distinguishing benign from malignant lesions . Digital breast tomosynthesis (DBT), a technology initially approved by the Food and Drug Administration in 2011, has been increasingly implemented as a screening tool to improve the specificity and possibly the sensitivity of routine screening mammography.
DBT creates cross-sectional images of the breast by imaging in a series of different projections as the x-ray tube moves in a limited arc over a compressed breast, and data are reconstructed through a mathematical algorithm into a series of thin-slice images that can be scrolled through. The result is reduction in superimposition of breast tissue, allowing better discrimination between normal tissue and pathology, as well as allowing better visualization of lesions . It has been shown that the addition of DBT can improve lesion detection, margin analysis, and localization . Several studies support the role of DBT in decreasing the recall rate and increasing cancer detection rate (CDR) , with reported 6%–67% reduction in recall rates . Moreover, there was increased detection of invasive cancers in some studies . Improvements in PPV for recalls and biopsies with the addition of DBT suggest that it may limit the harms of screening mammography, including unnecessary radiation, expense, biopsies, and patient anxiety . There is currently no set standard as to which patient populations should be managed with DBT ; however, some studies have demonstrated greater reduction in recall and increase in CDR in young patients and in patients with dense breasts . It is known that the sensitivity of mammography is reduced in dense breasts owing to obscuration of lesions, whereas at the same time women with dense breasts are prone to false-positive recalls attributed to overlapping normal tissue .
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Materials and Methods
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Results
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Table 1
Patient Characteristics
Characteristic Cohort 1: DM Cohort 2: DBT_P_ OR_n_ = 1694n = 319 Age categories, no. (%), y <51 694(41%) 137(43) .510 1.00(referent) ≥51 1000(59%) 182(57) 0.92(0.721.17) Dense breasts, no. (%) No 1101(65%) 151(47%) 1.00(referent) Yes 593(35%) 168(53%) <.001 2.06(1.622.63) Prior mammogram, no. (%) No 322(19%) 13(4%) <.001 1.00(referent) Yes 1372(81%) 306(96%) 5.52(3.139.75)
DBT, digital breast tomosynthesis; DM, digital screening mammography; OR, odds ratio.
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Table 2
Clinical Performance Measures
Metric Cohort 1: DM Cohort 2: DBT_P_ OR_n_ = 1,694n = 319 Recall 1,694/10,477 = 16% 319/2,304 = 14% .017 1.16(1.021.32) Biopsies performed No 1,481 (87%) 258 (81%) 1(referent) Yes 213 (13%) 61 (19%) .0018 1.64(1.202.25) Cancers detected 54/10,477 = 0.517% 18/2,304 = 0.781% .127 0.65(0.381.12) Cancers per 1000 screened 5.2 7.8 PPV1 (cancers/recall), % 54/1694 = 3% 18/319 = 5.6% .032 0.55(0.310.95) PPV2 (cancers/biopsy recommended), % 54/215 = 25.1% 18/61 = 29.5% .689 1.81(0.943.49) PPV3 (cancers/biopsy performed),% 54/213 = 25.3% 18/61 = 29.5% .516 0.81(0.431.52)
DBT, digital breast tomosynthesis; DM, digital screening mammography; OR, odds ratio; PPV, positive predictive value.
Table 3
Cancers Detected by Type and Screening Method
Outcome Cohort 1: DM Cohort 2: DBT Difference per 1000 Screens, no. (%)P__n = 10,477n = 2304 Total cancers 54 18 Cancers detected per 1000 screened 5.2(3.96.7) 7.8(4.612) 2.6 .127 Invasive cancers_N_ (% of total cancers) 33(61) 8(44) Rate per 1000 screened (95% CI) 3.1(2.24.4) 3.5(1.56.8) 0.4 .805 Ductal carcinoma in situ_N_ (% of total cancers) 21(39) 10(56) Rate per 1000 screened (95% CI) 2(1.23.1) 4.3(2.18) 2.3 .044
CI, confidence interval; DBT, digital breast tomosynthesis; DM, digital screening mammography.
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Table 4
Probably Benign (BIRADS 3) Lesions Detected by Type and Screening Method
Outcome Cohort 1: DM Cohort 2: DBT Difference per 1000 Screens, no. (%)P__n = 10,477n = 2304 Total probably benign lesions 382 103 Probably benign lesions detected per 1000 screened 36(3240) 44(3654) 8 .072 Probably benign calcifications_N_ (% of total probably benign lesions) 97(25) 18(17) Rate per 1000 screened (95% CI) 9.3(7.511) 7.8(4.612) −1.5 .509 Probably benign noncalcified lesions \* N (% of total probably benign lesions) 285(74) 85(82) Rate per 1000 screened (95% CI) 27(2430) 36(2945) 9 .015
BIRADS, Breast Imaging Reporting and Data System; CI, confidence interval; DBT, digital breast tomosynthesis; DM, digital screening mammography.
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Table 5
Association of Patient Characteristics with Odds of BIRADS 3 Recommendation at Recall
Characteristic Unadjusted, n = 2013 Multivariable, n = 2013 OR (95% CI)P OR (95% CI)P Screening modality DM 1.00(referent) <.001 1.00(referent) <.001 DBT 1.61(1.242.09) 1.80(1.382.36) Age categories, no.(%), y <51 1.00(referent) .287 1.00(referent) .314 ≥51 0.89(0.721.09) 1.12(0.891.39) Dense breasts, no. (%) No 1.00(referent) .010 1.00(referent) .001 Yes 0.75(0.600.93) 0.69(0.550.87) Prior mammogram, no. (%) No 1.00(referent) .039 1.00(referent) .023 Yes 0.76(0.580.98) 0.72(0.540.95)
BIRADS, Breast Imaging Reporting and Data System; CI, confidence interval; DBT, digital breast tomosynthesis; DM, digital screening mammography; OR, odds ratio.
Unadjusted: Univariate logistic regression analysis. Multivariable: Multivariable logistic regression model, adjusted for age, breast density, and prior mammogram.
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Discussion
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Conclusion
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