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Objectives: To evaluate the detectability of non-contrast-enhanced and contrast-enhanced spiral breast computed tomography ([non]-CE-SBCT) compared with mammography. Secondary objectives are to determine detectability depending on breast density and to evaluate appearance of breast malignancies according to BI-RADS descriptors., Methods: This retrospective institutional review board-approved study included 90 women with 105 biopsy-proven malignant breast lesions. Breast density, BI-RADS descriptors, and detectability were evaluated by 2 independent readers. Diagnostic confidence was rated on a 4-point Likert scale., Results: For readers 1 and 2, detectability was 83.8% and 80.0% for mammography, 99.1% and 99.1% for CE-SBCT ( P < 0.05), and 66.7% and 61.9% for non-CE-SBCT ( P < 0.05). With both readers, detectability in CE-SBCT was high for density A/B/C/D (both 100%/100%/100%/87.5%). Detectability of readers declined with increasing density for mammography (density A = 100%, B = 89.1% and 95.1%, C = 73.1%, D = 50.0% and 71.4%; P < 0.05) and for non-CE-SBCT (density A = 87.5% and 90.7%, B = 65.5% and 69.1%, C = 54.8% and 60.0%, D = 37.5%; P < 0.05). Mass lesions were detected with CT as often as with mammography, whereas architectural distortions and microcalcifications were detected less often with SBCT. Diagnostic confidence was very high or high in 97.2% for CE-SBCT, in 74.1% for non-CE-SBCT, and in 81.4% for mammography., Conclusions: Detectability and diagnostic confidence were very high in CE-SBCT, regardless of breast density. The detectability of non-CE-SBCT was lower than that of mammography and declined with increasing breast density., Competing Interests: Conflicts of interest and sources of funding: M.W. is part of the speaker bureau of Siemens Healthineers AG. E.W. is part of the speaker bureau of Siemens Healthineers AG and is a member of the advisory board of AB-CT and of Becton Dickinson GmbH. For the remaining authors, none were declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: Clinical experience regarding the use of dedicated photon-counting breast CT (PC-BCT) for diagnosis of breast microcalcifications is scarce. This study systematically compares the detection and classification of breast microcalcifications using a dedicated breast photon-counting CT, especially designed for examining the breast, in comparison with digital breast tomosynthesis (DBT)., Materials and Methods: This is a prospective intraindividual study on women with DBT screening-detected BI-RADS-4/-5 microcalcifications who underwent PC-BCT before biopsy. PC-BCT images were reconstructed with a noninterpolated spatial resolution of 0.15 × 0.15 × 0.15 mm (reconstruction mode 1 [RM-1]) and with 0.3 × 0.3 × 0.3 mm (reconstruction mode 2 [RM-2]), plus thin-slab maximum intensity projection (MIP) reconstructions. Two radiologists independently rated the detection of microcalcifications in direct comparison with DBT on a 5-point scale. The distribution and morphology of microcalcifications were then rated according to BI-RADS. The size of the smallest discernible microcalcification particle was measured. For PC-BCT, the average glandular dose was determined by Monte Carlo simulations; for DBT, the information provided by the DBT system was used., Results: Between September 2022 and July 2023, 22 participants (mean age, 61; range, 42-85 years) with microcalcifications (16 malignant; 6 benign) were included. In 2/22 with microcalcifications in the posterior region, microcalcifications were not detectable on PC-BCT, likely because they were not included in the PC-BCT volume. In the remaining 20 participants, microcalcifications were detectable. With high between-reader agreement (κ > 0.8), conspicuity of microcalcifications was rated similar for DBT and MIPs of RM-1 (mean, 4.83 ± 0.38 vs 4.86 ± 0.35) ( P = 0.66), but was significantly lower ( P < 0.05) for the remaining PC-BCT reconstructions: 2.11 ± 0.92 (RM-2), 2.64 ± 0.80 (MIPs of RM-2), and 3.50 ± 1.23 (RM-1). Identical distribution qualifiers were assigned for PC-BCT and DBT in 18/20 participants, with excellent agreement (κ = 0.91), whereas identical morphologic qualifiers were assigned in only 5/20, with poor agreement (κ = 0.44). The median size of smallest discernible microcalcification particle was 0.2 versus 0.6 versus 1.1 mm in DBT versus RM-1 versus RM-2 ( P < 0.001), likely due to blooming effects. Average glandular dose was 7.04 mGy (PC-BCT) versus 6.88 mGy (DBT) ( P = 0.67)., Conclusions: PC-BCT allows reliable detection of in-breast microcalcifications as long as they are not located in the posterior part of the breast and allows assessment of their distribution, but not of their individual morphology., Competing Interests: Conflicts of interest: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.

Objective: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI., Materials and Methods: The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value = 50-1500 s/mm 2 ). Three readers evaluated the vCE images with regard to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings., Results: The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p < 0.05). Non-significant effects (p > 0.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance., Conclusion: vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training., Key Points: Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research., (© 2024. The Author(s).)

· Breast MRI is an essential part of breast imaging. · The recommendations for performing breast MRI have been updated. · A table provides a compact and quick overview. More detailed comments supplement the table.. · The "classic" breast MRI can be performed based on the recommendations. Tips for special clinical questions, such as implant rupture, mammary duct pathology or local lymph node status, are included.. CITATION FORMAT: · Wenkel E, Wunderlich P, Fallenberg E et al. Aktualisierung der Empfehlungen der AG Mammadiagnostik der Deutschen Röntgengesellschaft zur Durchführung der Mamma-MRT. Fortschr Röntgenstr 2024; 196: 939 - 944., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Diffusion-weighted imaging (DWI) combined with radiomics can aid in the differentiation of breast lesions. Segmentation characteristics, however, might influence radiomic features. To evaluate feature stability, we implemented a standardized pipeline featuring shifts and shape variations of the underlying segmentations. A total of 103 patients were retrospectively included in this IRB-approved study after multiparametric diagnostic breast 3T MRI with a spin-echo diffusion-weighted sequence with echoplanar readout (b-values: 50, 750 and 1500 s/mm 2 ). Lesion segmentations underwent shifts and shape variations, with >100 radiomic features extracted from apparent diffusion coefficient (ADC) maps for each variation. These features were then compared and ranked based on their stability, measured by the Overall Concordance Correlation Coefficient (OCCC) and Dynamic Range (DR). Results showed variation in feature robustness to segmentation changes. The most stable features, excluding shape-related features, were FO (Mean, Median, RootMeanSquared), GLDM (DependenceNonUniformity), GLRLM (RunLengthNonUniformity), and GLSZM (SizeZoneNonUniformity), which all had OCCC and DR > 0.95 for both shifting and resizing the segmentation. Perimeter, MajorAxisLength, MaximumDiameter, PixelSurface, MeshSurface, and MinorAxisLength were the most stable features in the Shape category with OCCC and DR > 0.95 for resizing. Considering the variability in radiomic feature stability against segmentation variations is relevant when interpreting radiomic analysis of breast DWI data.

Objectives: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection., Materials and Methods: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285)., Results: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66., Conclusion: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms., Clinical Relevance Statement: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network., Key Points: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches., (© 2023. The Author(s).)

Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of morphological imaging criteria.Narrative review for decision-making regarding control and follow-up intervals for axillary LA according to currently published research. This article provides a practical overview of the management of vaccine-associated LA using image examples and a flowchart and provides recommendations for follow-up intervals. A particular focus is on patients presenting for diagnostic breast imaging. The diagnostic criteria for pathological lymph nodes (LN) are explained.Axillary LA is a common adverse effect after COVID-19 vaccination (0.3-53%). The average duration of LA is more than 100 days. LA is also known to occur after other vaccinations, such as the seasonal influenza vaccine. Systematic studies on this topic are missing. Other causes of LA after vaccination (infections, autoimmune diseases, malignancies) should be considered for the differential diagnosis. If the LA persists for more than 3 months after COVID-19 vaccination, a primarily sonographic follow-up examination is recommended after another 3 months. A minimally invasive biopsy of the LA is recommended if a clinically suspicious LN persists or progresses. In the case of histologically confirmed breast cancer, a core biopsy without a follow-up interval is recommended regardless of the vaccination, as treatment appropriate to the stage should not be influenced by follow-up intervals. For follow-up after breast cancer, the procedure depends on the duration of the LA and the woman's individual risk of recurrence.Vaccination history should be well documented and taken into account when evaluating suspicious LN. Biopsy of abnormal, persistent, or progressive LNs is recommended. Preoperative staging of breast cancer should not be delayed by follow-up. The risk of false-positive findings is accepted, and the suspicious LNs are histologically examined in a minimally invasive procedure. · The vaccination history must be documented (vaccine, date, place of application).. · If axillary LA persists for more than 3 months after vaccination, a sonographic follow-up examination is recommended after 3 months.. · Enlarged LNs that are persistent, progressive in size, or are suspicious on control sonography should be biopsied.. · Suspicious LNs should be clarified before starting oncological therapy, irrespective of the vaccination status, according to the guidelines and without delaying therapy.. · Wilpert C, Wenkel E, Baltzer PA et al. Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines. Fortschr Röntgenstr 2024; DOI 10.1055/a-2328-7536., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Objectives: To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS)., Methods: The MIPA observational study database (7245 patients) was searched for patients aged 18-80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs)., Results: Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111)., Conclusions: Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation., Clinical Relevance Statement: When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies., Key Points: • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies., (© 2023. The Author(s).)

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

Purpose: Broader clinical adoption of breast magnetic resonance imaging (MRI) faces challenges such as limited availability and high procedural costs. Low-field technology has shown promise in addressing these challenges. We report our initial experience using a next-generation scanner for low-field breast MRI at 0.55T., Methods: This initial cases series was part of an institutional review board-approved prospective study using a 0.55T scanner (MAGNETOM Free.Max, Siemens Healthcare, Erlangen/Germany: height < 2 m, weight < 3.2 tons, no quench pipe) equipped with a seven-channel breast coil (Noras, Höchberg/Germany). A multiparametric breast MRI protocol consisting of dynamic T1-weighted, T2-weighted, and diffusion-weighted sequences was optimized for 0.55T. Two radiologists with 12 and 20 years of experience in breast MRI evaluated the examinations., Results: Twelve participants (mean age: 55.3 years, range: 36-78 years) were examined. The image quality was diagnostic in all examinations and not impaired by relevant artifacts. Typical imaging phenotypes were visualized. The scan time for a complete, non-abbreviated breast MRI protocol ranged from 10:30 to 18:40 min., Conclusion: This initial case series suggests that low-field breast MRI is feasible at diagnostic image quality within an acceptable examination time., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M. Dietzel: no competing interest, S. Ohlmeyer: no competing interest, F. Laun: no competing interest, S. Bickelhaupt: no competing interest, R. Heiss and M. Uder: Member of speakers bureau of Siemens Healthineers., (Copyright © 2024. Published by Elsevier B.V.)

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions., (© 2024. The Author(s).)

Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht.