Adoption of Digital Breast Tomosynthesis in Clinical Practice

Richman, Ilana B.; Hoag, Jessica R.; Xu, Xiao; Forman, Howard P.; Hooley, Regina J.; Busch, Susan H.; Gross, Cary P.

doi:10.1001/jamainternmed.2019.1058

Cited by 50 publications

(32 citation statements)

References 6 publications

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“…With DBT, the percentage of symptomatic FN cancers was significantly lower in White women than in Black women, compared with those screened with DM. This could suggest differences in access to DBT between these populations, although without knowing the race or ethnicity of all study participants, this cannot be definitively stated (30).…”

Section: Discussionmentioning

confidence: 94%

False-Negative Rates of Breast Cancer Screening with and without Digital Breast Tomosynthesis

Durand¹,

Friedewald²,

Plecha³

et al. 2021

Radiology

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D igital breast tomosynthesis (DBT) has been shown to improve screening performance by increasing cancer detection and decreasing the number of false-positive findings compared with screening with digital mammography (DM) alone (1-9). However, questions remain regarding whether the improvement in cancer detection is due to greater detection of slower-growing well-differentiated cancers that tend to have better prognosis and little impact on long-term outcomes, such as breast cancer-specific mortality (6). Symptomatic false-negative (FN) cancers, also known as interval cancers, manifest after a negative screening and before the next routine screening, with symptoms such as a palpable lump, pain, or nipple discharge. Symptomatic FN cancers tend to have worse outcomes owing to rapid growth rates and aggressive biologic types compared with screendetected cancers (10). Thus, a reduction in symptomatic FN cancers may be considered a surrogate for longer-term screening outcomes, such as morbidity and mortality (11). FN rates can be confounded by an additional group of cancers called asymptomatic FN cancers, which are detected by means of other imaging modalities between mammographic screens. Owing to legislation in the United States related to notification regarding dense breasts, more patients are

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Section: Discussionmentioning

confidence: 94%

False-Negative Rates of Breast Cancer Screening with and without Digital Breast Tomosynthesis

Durand¹,

Friedewald²,

Plecha³

et al. 2021

Radiology

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“…Digital breast tomosynthesis (DBT) has rapidly disseminated for routine breast cancer screening, with evidence of improved overall screening performance with DBT when compared with digital mammography (DM). [1][2][3][4][5][6] Use of DBT has steadily increased since its approval by the US Food and Drug Administration in 2011, 7 with 63% of Mammography Quality Standards Act-certified facilities reporting DBT units in 2019. 8 Although this uptake has been associated with growing evidence that DBT improves screening recall or cancer detection, 1,3,6,9,10 the magnitude of these improvements varies across studies and by screening setting.…”

Section: Introductionmentioning

confidence: 99%

Screening Performance of Digital Breast Tomosynthesis vs Digital Mammography in Community Practice by Patient Age, Screening Round, and Breast Density

et al. 2020

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IMPORTANCE Digital mammography (DM) and digital breast tomosynthesis (DBT) are used for routine breast cancer screening. There is minimal evidence on performance outcomes by age, screening round, and breast density in community practice. OBJECTIVE To compare DM vs DBT performance by age, baseline vs subsequent screening round, and breast density category. DESIGN, SETTING, AND PARTICIPANTS This comparative effectiveness study assessed 1 584 079 screening examinations of women aged 40 to 79 years without prior history of breast cancer, mastectomy, or breast augmentation undergoing screening mammography at 46 participating Breast Cancer Surveillance Consortium facilities from January 2010 to April 2018. EXPOSURES Age, Breast Imaging Reporting and Data System breast density category, screening round, and modality. MAIN OUTCOMES AND MEASURES Absolute rates and relative risks (RRs) of screening recall and cancer detection. RESULTS Of 1 273 492 DM and 310 587 DBT examinations analyzed, 1 028 891 examinations (65.0%) were of white non-Hispanic women; 399 952 women (25.2%) were younger than 50 years; and 671 136 women (42.4%) had heterogeneously dense or extremely dense breasts. Adjusted differences in DM vs DBT performance were largest on baseline examinations: for example, per 1000 baseline examinations in women ages 50 to 59, recall rates decreased from 241 examinations for DM to 204 examinations for DBT (RR, 0.84; 95% CI, 0.73-0.98), and cancer detection rates increased from 5.9 with DM to 8.8 with DBT (RR, 1.50; 95% CI, 1.10-2.08). On subsequent examinations, women aged 40 to 79 years with heterogeneously dense breasts had improved recall rates and improved cancer detection with DBT. For example, per 1000 examinations in women aged 50 to 59 years, the number of recall examinations decreased from 102 with DM to 93 with DBT (RR, 0.91; 95% CI, 0.84-0.98), and cancer detection increased from 3.7 with DM to 5.3 with DBT (RR, 1.42; 95% CI, 1.23-1.64). Women aged 50 to 79 years with scattered fibroglandular density also had improved recall and cancer detection rates with DBT. Women aged 40 to 49 years with scattered fibroglandular density and women aged 50 to 79 years with almost entirely fatty breasts benefited from improved recall rates without change in cancer detection rates. No improvements in recall or cancer detection rates were observed in women with extremely dense breasts on subsequent examinations for any age group. CONCLUSIONS AND RELEVANCE This study found that improvements in recall and cancer detection rates with DBT were greatest on baseline mammograms. On subsequent screening (continued) Key Points Question Does screening performance Author affiliations and article information are listed at the end of this article.

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“…1 Although mammography-based screening has been shown to reduce breast cancer mortality, 2 it has also been associated with physical and psychological harms caused by false positives and unnecessary biopsies. [3][4][5] To address these concerns, many clinics have started to switch their screening programs from 2D full-field digital mammography (FFDM) to 3D digital breast tomosynthesis (DBT), 6 which has been shown to increase the sensitivity of breast cancer screening 7,8 and reduce false positives. 9 Deep learning (DL) using convolutional neural networks has previously been used to aid in the evaluation of screening mammography to enhance the specificity of malignancy prediction, particularly for FFDM exams.…”

Section: Purposementioning

confidence: 99%

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

Singh¹,

Matthews²,

Shah³

et al. 2020

Medical Imaging 2020: Computer-Aided Diagnosis

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Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becoming common in clinical settings. However, deep learning (DL) models have been developed mainly on conventional 2D full-field digital mammography (FFDM) or scanned film images. Due to a lack of large annotated DBT datasets, it is difficult to train a model on DBT from scratch. In this work, we present methods to generalize a model trained on FFDM images to DBT images. In particular, we use average histogram matching (HM) and DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity projection (MIP) of DBT images. In the proposed approach, the differences between the FFDM and DBT domains are reduced via HM and then the base model, which was trained on abundant FFDM images, is fine-tuned. When evaluating on image patches extracted around identified findings, we are able to achieve similar areas under the receiver operating characteristic curve (ROC AUC) of ∼ 0.9 for FFDM and ∼ 0.85 for MIP images, as compared to a ROC AUC of ∼ 0.75 when tested directly on MIP images.

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Adoption of Digital Breast Tomosynthesis in Clinical Practice

Abstract: had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Cited by 50 publications

References 6 publications

False-Negative Rates of Breast Cancer Screening with and without Digital Breast Tomosynthesis

False-Negative Rates of Breast Cancer Screening with and without Digital Breast Tomosynthesis

Screening Performance of Digital Breast Tomosynthesis vs Digital Mammography in Community Practice by Patient Age, Screening Round, and Breast Density

Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis

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