Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis

Hadadi, Ibrahim; Rae, William; Clark, Julian; McEntee, Mark F.; Ekpo, Ernest U.

doi:10.1016/j.clbc.2021.03.006

Cited by 29 publications

(30 citation statements)

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“…Our findings that adding DBT and ultrasound to screening programs enhances the early detection of breast cancers in both dense and non-dense breasts are consistent with the literature [ 12 , 33 ]. Importantly, we found that cancers detected by DBT, and ultrasound are small and/or invasive DBT and ultrasound detected many breast cancers, including 79% of invasive cancers and 49% of cancers smaller than 1 cm.…”

Section: Discussionsupporting

confidence: 92%

“…Breast ultrasound is currently used primarily as an adjunct to assess suspicious findings identified during DM. It has been shown that ultrasound could detect small invasive cancers regardless of the density of a woman’s breast [ 12 ]. However, ultrasound has some limitations such as operator-dependence, low sensitivity in calcifications, a typical feature of ductal carcinoma in situ, and a high frequency of false positives [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that ultrasound could detect small invasive cancers regardless of the density of a woman’s breast [ 12 ]. However, ultrasound has some limitations such as operator-dependence, low sensitivity in calcifications, a typical feature of ductal carcinoma in situ, and a high frequency of false positives [ 12 , 13 ]. There is limited evidence that ultrasound is sufficient to be used as an adjunct to DM in screening programs; however, the literature suggests that using ultrasound in conjunction with DM may be beneficial, particularly in women at high risk of developing breast cancer [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

et al. 2022

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Background: To compare the diagnostic efficacy of digital breast tomosynthesis (DBT) and ultrasound across breast densities in women recalled for assessment. Methods: A total of 482 women recalled for assessment from January 2017 to December 2019 were selected for the study. Women met the inclusion criteria if they had undergone DBT, ultrasound and had confirmed biopsy results. We calculated sensitivity, specificity, PPV, and AUC for DBT and ultrasound. Results: In dense breasts, DBT showed significantly higher sensitivity than ultrasound (98.2% vs. 80%; p < 0.001), but lower specificity (15.4% vs. 55%; p < 0.001), PPV (61.3% vs. 71%; p = 0.04) and AUC (0.568 vs. 0.671; p = 0.001). In non-dense breasts, DBT showed significantly higher sensitivity than ultrasound (99.2% vs. 84%; p < 0.001), but no differences in specificity (22% vs. 33%; p = 0.14), PPV (69.2% vs. 68.8%; p = 0.93) or AUC (0.606 vs. 0.583; p = 0.57). Around 73% (74% dense and 71% non-dense) and 77% (81% dense and 72% non-dense) of lesions assigned a RANZCR 3 by DBT and ultrasound, respectively, were benign. Conclusion: DBT has higher sensitivity, but lower specificity and PPV than ultrasound in women with dense breasts recalled for assessment. Most lesions rated RANZCR 3 on DBT and ultrasound are benign and may benefit from short interval follow-up rather than biopsy.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

et al. 2022

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“…Nevertheless, it is very important to use the complete dataset for diagnosis and provide it for the subsequent treatment [16]. In a recent randomized controlled trial, magnetic resonance imaging (MRI) in the extremely dense breast screening group with negative MG showed a significantly reduced interval cancer rate at the cost of slightly increased false-positive cases (LoE1b/B/AGO+) [19, 20].…”

Section: Breast Cancer Diagnosticsmentioning

confidence: 99%

AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2022

et al. 2022

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Introduction: The AGO (Arbeitsgemeinschaft Gynäkologische Onkologie, German Gynecological Oncology Group) Task Force on Diagnosis and Treatment of Breast Cancer as an interdisciplinary team consists of specialists from gynecological oncology, pathology, diagnostic radiology, medical oncology, and radiation oncology with a special focus on breast cancer. Methods: The updated evidence-based treatment recommendation 2022 for early breast cancer (EBC) and metastatic breast cancer (MBC) of the AGO Task Force have been released. Results and Conclusion: This paper captures the update of EBC.

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“…In this study, we summarize well-known imaging modalities for breast cancer diagnosis and analysis. As many existing studies have shown, there are several imaging modalities, including mammography, histopathology, ultrasound, magnetic resonance imaging, positron emission tomography, digital breast tomosynthesis, and a combination of these modalities (multimodalities) [ 10 , 32 , 33 ]. There are various public or private datasets for these modalities.…”

Section: Imaging Modalities and Available Datasets For Breast Cancermentioning

confidence: 99%

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Madani

Behzadi

Nabavi

2022

Cancers

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Breast cancer is among the most common and fatal diseases for women, and no permanent treatment has been discovered. Thus, early detection is a crucial step to control and cure breast cancer that can save the lives of millions of women. For example, in 2020, more than 65% of breast cancer patients were diagnosed in an early stage of cancer, from which all survived. Although early detection is the most effective approach for cancer treatment, breast cancer screening conducted by radiologists is very expensive and time-consuming. More importantly, conventional methods of analyzing breast cancer images suffer from high false-detection rates. Different breast cancer imaging modalities are used to extract and analyze the key features affecting the diagnosis and treatment of breast cancer. These imaging modalities can be divided into subgroups such as mammograms, ultrasound, magnetic resonance imaging, histopathological images, or any combination of them. Radiologists or pathologists analyze images produced by these methods manually, which leads to an increase in the risk of wrong decisions for cancer detection. Thus, the utilization of new automatic methods to analyze all kinds of breast screening images to assist radiologists to interpret images is required. Recently, artificial intelligence (AI) has been widely utilized to automatically improve the early detection and treatment of different types of cancer, specifically breast cancer, thereby enhancing the survival chance of patients. Advances in AI algorithms, such as deep learning, and the availability of datasets obtained from various imaging modalities have opened an opportunity to surpass the limitations of current breast cancer analysis methods. In this article, we first review breast cancer imaging modalities, and their strengths and limitations. Then, we explore and summarize the most recent studies that employed AI in breast cancer detection using various breast imaging modalities. In addition, we report available datasets on the breast-cancer imaging modalities which are important in developing AI-based algorithms and training deep learning models. In conclusion, this review paper tries to provide a comprehensive resource to help researchers working in breast cancer imaging analysis.

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Diagnostic Performance of Adjunctive Imaging Modalities Compared to Mammography Alone in Women with Non-Dense and Dense Breasts: A Systematic Review and Meta-Analysis

Cited by 29 publications

References 75 publications

Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

Diagnostic Efficacy across Dense and Non-Dense Breasts during Digital Breast Tomosynthesis and Ultrasound Assessment for Recalled Women

AGO Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2022

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

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