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Purpose: The use of tissue adhesive instead of a drain following mastectomy was a point of interest for many breast surgeons. Postoperative formation of multiple unusual sonographic lesions was observed in patients that underwent mastectomy with TissuGlu. The aim of this study was to describe the sonographic features of these lesions and, when possible, to examine them histologically., Materials and Methods: This study includes 98 patients, 49 underwent mastectomy with the application of TissuGlu and 49 with drain insertion. Unusual postoperative sonographic findings were thoroughly described. A histological examination was carried out according to the guideline recommendations., Results: Unusual sonographic findings were detected in 87.8% of patients in the TissuGlu group and in only 4% of the patients in the drain group. These lesions were detectable between 6 and 59 months postoperatively. 47 breasts of the TissuGlu group were classified as category 3, while only 2 breasts as category 4. Lesions were on average 7.5 mm in diameter, echogenic or isoechoic with posterior shadowing, an irregular and ill circumscribed marginal contour, and a horizontal axis. All histologically examined lesions (n=29) were benign. Granulomatous tissue was histologically proven in 63% of those lesions (n=17), while residual adhesive material could be detected in 18.5% of lesions (n=5)., Conclusion: The use of TissuGlu adhesive after mastectomy may cause the formation of unusual palpable granulomas, with or without residual adhesive materials. Sonographic description of lesions will help physicians to differentiate between granulomas and local relapse., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Alongside mammography, breast ultrasound is an important and well-established method in assessment of breast lesions. With the "Best Practice Guideline", the DEGUM Breast Ultrasound (in German, "Mammasonografie") working group, intends to describe the additional and optional application modalities for the diagnostic confirmation of breast findings and to express DEGUM recommendations in this Part II, in addition to the current dignity criteria and assessment categories published in Part I, in order to facilitate the differential diagnosis of ambiguous lesions.The present "Best Practice Guideline" has set itself the goal of meeting the requirements for quality assurance and ensuring quality-controlled performance of breast ultrasound. The most important aspects of quality assurance are explained in this Part II of the Best Practice Guideline., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Objectives: We evaluated whether lesion-to-fat ratio measured by shear wave elastography in patients with Breast Imaging Reporting and Data System (BI-RADS) 3 or 4 lesions has the potential to further refine the assessment of B-mode ultrasound alone in breast cancer diagnostics., Methods: This was a secondary analysis of an international diagnostic multicenter trial (NCT02638935). Data from 1288 women with breast lesions categorized as BI-RADS 3 and 4a-c by conventional B-mode ultrasound were analyzed, whereby the focus was placed on differentiating lesions categorized as BI-RADS 3 and BI-RADS 4a. All women underwent shear wave elastography and histopathologic evaluation functioning as reference standard. Reduction of benign biopsies as well as the number of missed malignancies after reclassification using lesion-to-fat ratio measured by shear wave elastography were evaluated., Results: Breast cancer was diagnosed in 368 (28.6%) of 1288 lesions. The assessment with conventional B-mode ultrasound resulted in 53.8% (495 of 1288) pathologically benign lesions categorized as BI-RADS 4 and therefore false positives as well as in 1.39% (6 of 431) undetected malignancies categorized as BI-RADS 3. Additional lesion-to-fat ratio in BI-RADS 4a lesions with a cutoff value of 1.85 resulted in 30.11% biopsies of benign lesions which correspond to a reduction of 44.04% of false positives., Conclusions: Adding lesion-to-fat ratio measured by shear wave elastography to conventional B-mode ultrasound in BI-RADS 4a breast lesions could help reduce the number of benign biopsies by 44.04%. At the same time, however, 1.98% of malignancies were missed, which would still be in line with American College of Radiology BI-RADS 3 definition of <2% of undetected malignancies., (© 2023 The Authors. Journal of Ultrasound in Medicine published by Wiley Periodicals LLC on behalf of American Institute of Ultrasound in Medicine.)

Purpose:  In this prospective, multicenter trial we evaluated whether additional shear wave elastography (SWE) for patients with BI-RADS 3 or 4 lesions on breast ultrasound could further refine the assessment with B-mode breast ultrasound for breast cancer diagnosis., Materials and Methods:  We analyzed prospective, multicenter, international data from 1288 women with breast lesions rated by conventional 2 D B-mode ultrasound as BI-RADS 3 to 4c and undergoing 2D-SWE. After reclassification with SWE the proportion of undetected malignancies should be < 2 %. All patients underwent histopathologic evaluation (reference standard)., Results:  Histopathologic evaluation showed malignancy in 368 of 1288 lesions (28.6 %). The assessment with B-mode breast ultrasound resulted in 1.39 % (6 of 431) undetected malignancies (malignant lesions in BI-RADS 3) and 53.80 % (495 of 920) unnecessary biopsies (biopsies in benign lesions). Re-classifying BI-RADS 4a patients with a SWE cutoff of 2.55 m/s resulted in 1.98 % (11 of 556) undetected malignancies and a reduction of 24.24 % (375 vs. 495) of unnecessary biopsies., Conclusion:  A SWE value below 2.55 m/s for BI-RADS 4a lesions could be used to downstage these lesions to follow-up, and therefore reduce the number of unnecessary biopsies by 24.24 %. However, this would come at the expense of some additionally missed cancers compared to B-mode breast ultrasound (rate of undetected malignancies 1.98 %, 11 of 556, versus 1.39 %, 6 of 431) which would, however, still be in line with the ACR BI-RADS 3 definition (< 2 % of undetected malignancies)., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate., Methods: We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE., Results: In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound., Conclusion: The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

For many years, breast ultrasound has been used in addition to mammography as an important method for clarifying breast findings. However, differences in the interpretation of findings continue to be problematic 1 2. These differences decrease the diagnostic accuracy of ultrasound after detection of a finding and complicate interdisciplinary communication and the comparison of scientific studies 3. In 1999, the American College of Radiology (ACR) created a working group (International Expert Working Group) that developed a classification system for ultrasound examinations based on the established BI-RADS classification of mammographic findings under consideration of literature data 4. Due to differences in content, the German Society for Ultrasound in Medicine (DEGUM) published its own BI-RADS-analogue criteria catalog in 2006 3. In addition to the persistence of differences in content, there is also an issue with formal licensing with the current 5th edition of the ACR BI-RADS catalog, even though the content is recognized by the DEGUM as another system for describing and documenting findings. The goal of the Best Practice Guideline of the Breast Ultrasound Working Group of the DEGUM is to provide colleagues specialized in senology with a current catalog of ultrasound criteria and assessment categories as well as best practice recommendations for the various ultrasound modalities., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Objectives: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms., Methods: Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC)., Results: Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05)., Conclusions: The performance of humans and AI-based algorithms improves with multi-modal information., Key Points: • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses., (© 2022. The Author(s).)

Background: Shear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies., Methods: We report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures., Results: On performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off ≥3.70 m/s, SE cut-off ≥1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients)., Conclusion: Adding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)