Advances in Breast Imaging with Current Screening Recommendations and Controversies

Shahan, Cimmie; Layne, Ginger P.

doi:10.1016/j.ogc.2021.11.001

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…Breast ultrasound has progressed enormously over the last decade with markedly improved resolution and rapid image processing. 1 It is classified as a physiological function test in which a patient's body is directly examined by an operator. Thus, its accuracy depends on the quality of the equipment and environment, as well as on the observer's technique, experience, knowledge of disease, and how the findings are obtained.…”

Section: Introductionmentioning

confidence: 99%

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Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

et al. 2022

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Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI‐RADS) has become widespread worldwide, the problem of inter‐observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a system in which artificial intelligence (AI) can distinguish whether a static image obtained using a breast ultrasound represents BI‐RADS3 or lower or BI‐RADS4a or higher to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4028 images containing 5014 lesions and a test dataset consisting of 3166 images containing 3656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 91.2%, and 90.7%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Although deep‐learning methods to categorize benign and malignant tumors using breast ultrasound have been extensively reported, our work represents the first attempt to establish an AI system to classify BI‐RADS3 or lower and BI‐RADS4a or higher successfully, providing important implications for clinical actions. These results suggest that the AI diagnostic system is sufficient to proceed to the next stage of clinical application.

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Section: Introductionmentioning

confidence: 99%

“…Breast ultrasound has progressed enormously over the last decade with markedly improved resolution and rapid image processing 1 . It is classified as a physiological function test in which a patient's body is directly examined by an operator.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

et al. 2022

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Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis

et al. 2023

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Imaging Preferences in Women With a History of Breast Cancer Receiving Contrast-Enhanced Mammography

Moravia,

Mosaddhegi,

Mehta

et al. 2023

Journal of Breast Imaging

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Objective There is interest in contrast-enhanced mammography (CEM) to screen breast cancer survivors, yet it is unclear whether they would accept CEM as their annual exam. The purpose of this study was to understand patient preferences to guide CEM implementation for screening. Methods Consecutive women with breast cancer history who had CEM as their annual mammogram from July 2020 to August 2021 at a single academic institution completed an 18-question survey regarding prior contrast imaging, CEM experience, and comparison to other breast imaging exams. Response proportions were calculated, and chi-square or Fisher’s exact test were used to evaluate associations of demographics with responses. Results A total of 78% (104/133) of women undergoing CEM provided results. Most were satisfied with CEM (99%, 103/104), had nothing to complain about (87%, 90/104), did not find CEM anxiety provoking (69%, 72/104), felt comfortable having contrast for annual imaging (94%, 98/104), were willing to accept the small risk of a contrast reaction if CEM would find their cancer (93%, 97/104), and would like to have CEM for their exam next year (95%, 99/104). Compared with mammography, 23% (24/104) reported CEM was a better experience, and 63% (66/104) reported CEM was about the same. Of those who had prior MRI, the majority reported CEM was better (53%, 29/55) and would prefer CEM if both MRI and CEM had an equal chance of detecting cancer (73%, 41/56). Most preferences did not differ significantly according to demographics. Conclusion Most women surveyed considered CEM to be satisfactory and preferred compared to other breast screening modalities.

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Advances in Breast Imaging with Current Screening Recommendations and Controversies

Cited by 5 publications

References 46 publications

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

Establishment of a deep‐learning system to diagnose BI‐RADS4a or higher using breast ultrasound for clinical application

Breast MRI: Clinical Indications, Recommendations, and Future Applications in Breast Cancer Diagnosis

Imaging Preferences in Women With a History of Breast Cancer Receiving Contrast-Enhanced Mammography

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