CSBJSep 2, 2025

🩻 AI isn’t here to replace radiologists — it’s here to write their first draft.

🔗 Hybrid framework for automated generation of mammography radiology reports. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.07.018

📚 CSBJ: https://www.csbj.org/

#Radiology #Mammography #BreastCancerScreening #MedicalImaging #DigitalHealth #RadiologyAI #CancerDetection #SpanishAI #ClinicalNLP #HealthcareInnovation #GlobalHealth #HumanCenteredAI #AIinHealthcare #MedicalAI #HealthTech

Izen Imaging and InterventionsMay 17, 2025

Digital vs Traditional Mammography: Which One Should You Choose?

Confused about mammography options? Learn how digital and traditional mammograms differ, and which one is better suited for your needs. Explore the benefits, differences, and considerations to make an informed decision. Read more here: https://creative9blogs.wordpress.com/2025/05/15/digital-vs-traditional-mammography-whats-the-difference-and-which-is-better/

#Mammography #BreastHealth #DigitalMammography #TraditionalMammography #BreastCancerAwareness #HealthCare #MedicalImaging #WomenHealth #EarlyDetection #HealthTips

Digital vs. Traditional Mammography: What’s the Difference and Which Is Better?

When it comes to early breast cancer detection, mammography is still one of the most useful tests. But now, there are two main types — traditional (film) and digital. If you’re going for&nbsp…

Creative9 Blogs
CSBJMar 27, 2025

🔬 This study explores how Federated Learning (FL) can revolutionize medical AI by enhancing generalizability while preserving patient privacy.

🔗 Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.03.031

📚 CSBJ Smart Hospital: https://www.csbj.org/smarthospital

#FederatedLearning #MedicalAI #Mammography #PrivacyPreservingAI #HealthcareInnovation #MedicalInformatics

European RadiologyDec 4, 2024

This study compared three contrast-enhanced #mammography (CEM) systems, finding that one system outperformed the others, providing higher contrast-to-noise ratios (CNR) at lower radiation doses, while highlighting the variability in performance among dual-energy subtraction (DES) algorithms in CEM. (Gisella Gennaro et al.)

#EuropeanRadiologyExperimental

🔗 https://buff.ly/3AiTYWu

Performance of dual-energy subtraction in contrast-enhanced mammography for three different manufacturers: a phantom study - European Radiology Experimental

Background Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. Methods A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. Results On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. Conclusion One system outperformed others in DES algorithms, providing higher CNR at lower doses. Relevance statement This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. Key Points DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose. Graphical Abstract

SpringerOpen
European RadiologyNov 28, 2024

A study on inter-reader agreement of the breast imaging reporting and data system (BI-RADS) contrast-enhanced #mammography (CEM) lexicon found moderate to substantial agreement for most features, with lower agreement for non-mass enhancement and enhancing asymmetry. (Calogero Zarcaro et al.)

#EuropeanRadiology #BIRADS

🔗 https://buff.ly/3OqsjpP

Inter-reader agreement of the BI-RADS CEM lexicon - European Radiology

Purpose The purpose of this study was to assess the inter-reader agreement of the breast imaging reporting and data system (BI-RADS) contrast-enhanced mammography (CEM) lexicon. Materials and methods In this IRB-approved, single-center, retrospective study, three breast radiologists, each with different levels of experience, reviewed 462 lesions in 421 routine clinical CEM according to the fifth edition of the BI-RADS lexicon for mammography and to the first version of the BI-RADS lexicon for CEM. Readers were blinded to patient outcomes and evaluated breast and lesion features on low-energy (LE) images (breast density, type of lesion, associated architectural distortion), lesion features on recombined (RC) images (type of enhancement, characteristic of mass enhancement, non-mass enhancement or enhancing asymmetry), and provided a final BI-RADS assessment. The inter-reader agreement was calculated for each evaluated feature using Fleiss’ kappa coefficient. Sensitivity and specificity were calculated. Results The inter-reader agreement was moderate to substantial for breast density (ĸ = 0.569), type of lesion on LE images (ĸ = 0.654), and type of enhancement (ĸ = 0.664). There was a moderate to substantial agreement on CEM mass enhancement descriptors. The agreement was fair to moderate for non-mass enhancement and enhancing asymmetry descriptors. Inter-reader agreement for LE and LE with RC BI-RADS assessment was moderate (ĸ = 0.421) and fair (ĸ = 0.364). Diagnostic performance was good and comparable for all readers. Conclusion Inter-reader agreement of the CEM lexicon was moderate to substantial for most features. There was a low agreement for some RC descriptors, such as non-mass enhancement and enhancing asymmetry, and BI-RADS assessment, but this did not impact the diagnostic performance. Key Points Question Data on the reproducibility and inter-reader agreement for the first version of the BI-RADS lexicon dedicated to CEM are missing. Finding The inter-reader agreement for the lexicon was overall substantial to moderate, but it was lower for the descriptors for non-mass enhancement and enhancing asymmetry. Clinical relevance A common lexicon simplifies communication between specialists in clinical practice. The good inter-reader agreement confirms the effectiveness of the CEM-BIRADS in ensuring consistent communication. Detailed definitions of some descriptors (non-mass, enhancing asymmetry) are needed to ensure higher agreements.

SpringerLink
European RadiologySep 6, 2024

Training in #mammography has traditionally been done through information from institutional libraries, books, and experience over time. This study explores whether #AI-generated images can help in simulation education to improve the performance of residents in training. (Krithika Rangarajan et al.)

#EuropeanRadiology

🔗 https://buff.ly/3ZbOZke

Simulation training in mammography with AI-generated images: a multireader study - European Radiology

Objectives The interpretation of mammograms requires many years of training and experience. Currently, training in mammography, like the rest of diagnostic radiology, is through institutional libraries, books, and experience accumulated over time. We explore whether artificial Intelligence (AI)-generated images can help in simulation education and result in measurable improvement in performance of residents in training. Methods We developed a generative adversarial network (GAN) that was capable of generating mammography images with varying characteristics, such as size and density, and created a tool with which a user could control these characteristics. The tool allowed the user (a radiology resident) to realistically insert cancers within different regions of the mammogram. We then provided this tool to residents in training. Residents were randomized into a practice group and a non-practice group, and the difference in performance before and after practice with such a tool (in comparison to no intervention in the non-practice group) was assessed. Results Fifty residents participated in the study, 27 underwent simulation training, and 23 did not. There was a significant improvement in the sensitivity (7.43 percent, significant at p-value = 0.03), negative predictive value (5.05 percent, significant at p-value = 0.008) and accuracy (6.49 percent, significant at p-value = 0.01) among residents in the detection of cancer on mammograms after simulation training. Conclusion Our study shows the value of simulation training in diagnostic radiology and explores the potential of generative AI to enable such simulation training. Clinical relevance statement Using generative artificial intelligence, simulation training modules can be developed that can help residents in training by providing them with a visual impression of a variety of different cases. Key Points Generative networks can produce diagnostic imaging with specific characteristics, potentially useful for training residents. Training with generating images improved residents’ mammographic diagnostic abilities. Development of a game-like interface that exploits these networks can result in improvement in performance over a short training period.

SpringerLink
European RadiologyAug 7, 2024

Breast density, which is considered an independent risk factor for the development of #BreastCancer, decreases the sensitivity of #mammography for screening; thus, women with dense breasts have an increased risk of late breast cancer diagnosis.

This study by Ritse M. Mann et al. dives into the recommendations by EUSOBI regarding screening in women with extremely dense breasts.

#EuropeanRadiology #RadiologyAndBeyond

Read it here 👉 https://buff.ly/3M8BJ7v

Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI) - European Radiology

Abstract Breast density is an independent risk factor for the development of breast cancer and also decreases the sensitivity of mammography for screening. Consequently, women with extremely dense breasts face an increased risk of late diagnosis of breast cancer. These women are, therefore, underserved with current mammographic screening programs. The results of recent studies reporting on contrast-enhanced breast MRI as a screening method in women with extremely dense breasts provide compelling evidence that this approach can enable an important reduction in breast cancer mortality for these women and is cost-effective. Because there is now a valid option to improve breast cancer screening, the European Society of Breast Imaging (EUSOBI) recommends that women should be informed about their breast density. EUSOBI thus calls on all providers of mammography screening to share density information with the women being screened. In light of the available evidence, in women aged 50 to 70 years with extremely dense breasts, the EUSOBI now recommends offering screening breast MRI every 2 to 4 years. The EUSOBI acknowledges that it may currently not be possible to offer breast MRI immediately and everywhere and underscores that quality assurance procedures need to be established, but urges radiological societies and policymakers to act on this now. Since the wishes and values of individual women differ, in screening the principles of shared decision-making should be embraced. In particular, women should be counselled on the benefits and risks of mammography and MRI-based screening, so that they are capable of making an informed choice about their preferred screening method. Key Points • The recommendations in Figure 1 summarize the key points of the manuscript

SpringerLink
Crittenden Medical LiabilityJul 2, 2024

Reflecting on AI's Impact in Radiology!

In our last webinar, we explored how AI enhances mammography, spotting small lesions and improving accuracy. Now, don't miss our upcoming webinar to delve deeper into these advancements!

Join our upcoming webinar: Liability Coverage, Risk & Transparency: Solutioning for Advanced Practice Providers.

🗓️ July 25th, 2024
👉 Register Here: https://zurl.co/NPRT

#AI #Radiology #Mammography #Healthcare #Webinar

Virtual Event - Crittenden Medical

Supporting Healthcare Underwriters in Today’s World: Tips from the Risk & Patient Safety Field Liability Coverage, Risk & Transparency: Solutioning for Advanced Practice Providers Webinar Series 2024-2025 Upcoming — Nov. 5th, 2024 Start Time: 10:00 AM PST | 11:00 AM MST | 12:00 PM CST | 1:00 PM EST Register Conference + Webinar Register Webinar […] More

Crittenden Medical
European RadiologyMay 15, 2024

This study finds Gisella Gennaro et al. characterizing the behavior of automatic exposure control for digital #mammography, digital breast tomosynthesis, and low-energy and high-energy acquisitions used in CE mammography.

#EuropeanRadiologyExperimental

🔗 https://buff.ly/3yqPJ9R

Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM - European Radiology Experimental

Background Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the “optimal” exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. Graphical Abstract

SpringerOpen
Insights Into ImagingMar 4, 2024

Nataliia Moshina et al. find that screening with digital breast #tomosynthesis (DBT) did not result in a higher #BreastCancer detection rate compared to screening with digital #mammography (DM).

#InsightsIntoImaging

🔗 https://buff.ly/3SQDk5N

Digital breast tomosynthesis in mammographic screening: false negative cancer cases in the To-Be 1 trial - Insights into Imaging

Objectives The randomized controlled trial comparing digital breast tomosynthesis and synthetic 2D mammograms (DBT + SM) versus digital mammography (DM) (the To-Be 1 trial), 2016–2017, did not result in higher cancer detection for DBT + SM. We aimed to determine if negative cases prior to interval and consecutive screen-detected cancers from DBT + SM were due to interpretive error. Methods Five external breast radiologists performed the individual blinded review of 239 screening examinations (90 true negative, 39 false positive, 19 prior to interval cancer, and 91 prior to consecutive screen-detected cancer) and the informed consensus review of examinations prior to interval and screen-detected cancers (n = 110). The reviewers marked suspicious findings with a score of 1–5 (probability of malignancy). A case was false negative if ≥ 2 radiologists assigned the cancer site with a score of ≥ 2 in the blinded review and if the case was assigned as false negative by a consensus in the informed review. Results In the informed review, 5.3% of examinations prior to interval cancer and 18.7% prior to consecutive round screen-detected cancer were considered false negative. In the blinded review, 10.6% of examinations prior to interval cancer and 42.9% prior to consecutive round screen-detected cancer were scored ≥ 2. A score of ≥ 2 was assigned to 47.8% of negative and 89.7% of false positive examinations. Conclusions The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT + SM versus DM in the To-Be 1 trial is complex and not due to interpretive error alone. Critical relevance statement The randomized controlled trial on digital breast tomosynthesis and synthetic 2D mammograms (DBT) and digital mammography (DM), 2016–2017, showed no difference in cancer detection for the two techniques. The rates of false negative screening examinations prior to interval and consecutive screen-detected cancer for DBT were consistent with the rates in prior DM reviews, indicating that the non-superior DBT performance in the trial might not be due to interpretive error alone. Key points • Screening with digital breast tomosynthesis (DBT) did not result in a higher breast cancer detection rate compared to screening with digital mammography (DM) in the To-Be 1 trial. • The false negative rates for examinations prior to interval and consecutive screen-detected cancer for DBT were determined in the trial to test if the lack of differences was due to interpretive error. • The false negative rates were consistent with those of prior DM reviews, indicating that the lack of higher cancer detection for DBT versus DM was complex and not due to interpretive error alone. Graphical Abstract

SpringerOpen