Breast cancer screening in women with and without implants: retrospective study comparing digital mammography to digital mammography combined with digital breast tomosynthesis

Cohen, Ethan O.; Perry, Rachel; Tso, Hilda H.; Phalak, Kanchan; Lesslie, Michele D.; Gerlach, Karen E.; Sun, Jia; Srinivasan, Ashmitha; Leung, Jessica W.T.

doi:10.1007/s00330-021-08040-3

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…The first evaluated how the needle approach for prone DBT-guided biopsy affected hematoma formation and clip migration [21], and the second directly compared procedural details for prone and upright, stereotactic, and DBT-guided breast biopsies [19]. The study population from a third prior publication evaluating DBT screening in women with breast implants partially overlapped with this current study population [22].…”

Section: Methodsmentioning

confidence: 99%

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Suspicious Ultrasound-Occult Non-Calcified Mammographic Masses, Asymmetries, and Architectural Distortions Are Moderate Probability for Malignancy

Cohen,

Perry,

Legha

et al. 2024

Cancers

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Suspicious non-calcified mammographic findings have not been evaluated with modern mammographic technique, and the purpose of this work is to compare the likelihood of malignancy for those findings. To do this, 5018 consecutive mammographically guided biopsies performed during 2016–2019 at a large metropolitan, community-based hospital system were retrospectively reviewed. In total, 4396 were excluded for targeting calcifications, insufficient follow-up, or missing data. Thirty-seven of 126 masses (29.4%) were malignant, 44 of 194 asymmetries (22.7%) were malignant, and 77 of 302 architectural distortions (AD, 25.5%) were malignant. The combined likelihood of malignancy was 25.4%. Older age was associated with a higher likelihood of malignancy for each imaging finding type (all p ≤ 0.006), and a possible ultrasound correlation was associated with a higher likelihood of malignancy when all findings were considered together (p = 0.012). Two-view asymmetries were more frequently malignant than one-view asymmetries (p = 0.03). There were two false-negative biopsies (98.7% sensitivity and 100% specificity). In conclusion, the 25.4% likelihood of malignancy confirms the recommendation for biopsy of suspicious, ultrasound-occult, mammographic findings. Mammographically guided biopsies were highly sensitive and specific in this study. Older patient age and a possible ultrasound correlation should raise concern given the increased likelihood of malignancy in those scenarios.

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Section: Methodsmentioning

confidence: 99%

“…Mammographically guided biopsy was recommended for these findings when an ultrasound failed to reveal a definite correlation. Further imaging details have been previously described [22].…”

Section: Methodsmentioning

confidence: 99%

Suspicious Ultrasound-Occult Non-Calcified Mammographic Masses, Asymmetries, and Architectural Distortions Are Moderate Probability for Malignancy

Cohen,

Perry,

Legha

et al. 2024

Cancers

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“…Tomosynthesis provides a 3D image of the breast, facilitating the detection of small tumors and reducing the need for additional imaging tests. However, it exposes patients to slightly more radiation, takes longer to perform, and is more expensive ( 25 ).…”

Section: Medical Imaging Techniques For Breast Cancer Detectionmentioning

confidence: 99%

Mammography with deep learning for breast cancer detection

Wang

2024

Front. Oncol.

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X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

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Breast cancer screening in women with and without implants: retrospective study comparing digital mammography to digital mammography combined with digital breast tomosynthesis

Cited by 2 publications

References 37 publications

Suspicious Ultrasound-Occult Non-Calcified Mammographic Masses, Asymmetries, and Architectural Distortions Are Moderate Probability for Malignancy

Suspicious Ultrasound-Occult Non-Calcified Mammographic Masses, Asymmetries, and Architectural Distortions Are Moderate Probability for Malignancy

Mammography with deep learning for breast cancer detection

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