Contrast-enhanced mammography in breast cancer screening

Coffey, Kristen; Jochelson, Maxine S.

doi:10.1016/j.ejrad.2022.110513

Cited by 41 publications

(21 citation statements)

References 41 publications

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“…The diagnostic performance of CEM vs. DM and/or US, and/or MRI has been investigated for various clinical indications, such as assessing equivocal mammographic abnormalities, preoperative staging of BC, and monitoring response to treatment. Results demonstrated that CEM outperforms DM/and/or US and comes close to the performance of breast MRI, being slightly less sensitive and possibly somewhat more specific 83–85 …”

Section: Part 2: Novel Imaging Approaches Toward Risk‐adapted Breast ...mentioning

confidence: 84%

“…Results demonstrated that CEM outperforms DM/and/or US and comes close to the performance of breast MRI, being slightly less sensitive and possibly somewhat more specific. [83][84][85] Acknowledging the performance of CEM in BC diagnosis, studies have been investigating its potential in the screening setting, and particularly, for women at higher-thanaverage risk. 86,87 According to the currently applied breast screening recommendations, 15,18,[22][23][24][25][26][27][28][29][30][31][32][33][34] women of higher risk, as well as women with extremely dense breasts, may benefit from additional screening with MRI, while US remains a viable option.…”

Section: X-ray-based Techniquesmentioning

confidence: 99%

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New Approaches and Recommendations for Risk‐Adapted Breast Cancer Screening

Tsarouchi

Hoxhaj

Mann

2023

Magnetic Resonance Imaging

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Population‐based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false‐positive rates, particularly in high‐risk women, challenge the indiscriminate nature of population‐based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk‐adapted approach. Recent advancements in breast imaging technology, including contrast material‐enhanced mammography (CEM), ultrasound (US) (automated‐breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast‐agent free), may provide new opportunities for risk‐adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk‐adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk‐adapted breast cancer screening approach.Evidence Level1.Technical EfficacyStage 5.

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Section: Part 2: Novel Imaging Approaches Toward Risk‐adapted Breast ...mentioning

confidence: 84%

Section: X-ray-based Techniquesmentioning

confidence: 99%

New Approaches and Recommendations for Risk‐Adapted Breast Cancer Screening

Tsarouchi

Hoxhaj

Mann

2023

Magnetic Resonance Imaging

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“…Mammography depends on the density difference between the lesion and the normal breast tissue to observe the overall morphology and characteristics of lesion, but it cannot expose the fine structure and blood supply of the lesion. [21,22] While functional magnetic resonance imaging can reveal the characteristics of tissues from the molecular level, MK represented the average diffusion kurtosis of each ladder direction in space. It was an index to measure the complexity of organizational structure.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

Wang

Jin

et al. 2022

Medicine

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Background: This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to monoexponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer.Methods: A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer.Results: Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed.Conclusions: Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.Abbreviations: 95% CI = 95% confidence interval, ADC = apparent diffusion coefficient, AUC = areas under the receiver operating characteristic curve, DKI = diffusion kurtosis imaging, DWI = diffusion weighted imaging, MD = mean diffusivity, MK = mean kurtosis.

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“…Brustkrebs ist die häufigste Krebsart der Frau und war 2020 für 684 996 Todesfälle global verantwortlich [16]. Die bildgebende Mammadiagnostik ist in der Früherkennung und Behandlung der Erkrankung essenziell [17,18]. Die digitale Mammographie (einschließlich weiterentwickelter Verfahren wie digitale Tomosynthese und kontrastmittelverstärkte Mammographie), Ultraschall und MRT sind die wichtigsten Methoden der bildgebenden Mammadiagnostik [17,18].…”

Section: Mammakarzinomunclassified

“…Dies erklärt, warum die nicht-invasive, bildgestützte Quantifizierung der Tumorgröße von entscheidender Bedeutung für das Patientinnen-Management ist. Grundsätzlich sind alle Methoden der bildgebenden Mammadiagnostik zur Beurteilung der Tumorgröße geeignet [17,18]. Allerdings ermöglicht die MRT eine besonders exakte, dreidimensionale und überlagerungsfreie Quantifizierung der Tumorgröße [17,30].…”

Section: Mammakarzinomunclassified

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

Bäuerle,

Dietzel,

Pinker

et al. 2023

Rofo

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Background Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). Method This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. Results and Conclusion Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. Key Points: Citation Format

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Contrast-enhanced mammography in breast cancer screening

Cited by 41 publications

References 41 publications

New Approaches and Recommendations for Risk‐Adapted Breast Cancer Screening

New Approaches and Recommendations for Risk‐Adapted Breast Cancer Screening

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

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