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Introduction: International guidelines recommend genetic testing for women with familial breast cancer at an expected prevalence of pathogenic germline variants (PVs) of at least 10%. In a study sample of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), we have previously shown that women with TNBC diagnosed before the age of 50 years but without a family history of breast or ovarian cancer (sTNBC) meet this criterion. The present study investigates the PV prevalence in BRCA1, BRCA2, and nine additional cancer predisposition genes in an extended sTNBC study sample including a cohort of women with a later age at sTNBC diagnosis. Patients and Methods: In 1,600 women with sTNBC (median age at diagnosis: 41 years, range 19–78 years), we investigated the association between age at diagnosis and PV occurrence in cancer predisposition genes using logistic regression. Results: 260 sTNBC patients (16.2%) were found to have a PV in cancer predisposition genes (BRCA1: n = 170 [10.6%]; BRCA2: n = 46 [2.9%], other: n = 44 [2.8%]). The PV prevalence in women diagnosed between 50 and 59 years (n = 194) was 11.3% (22/194). Logistic regression showed a significant increase in PV prevalence with decreasing age at diagnosis (OR 1.41 per 10 years younger age at diagnosis; 95% confidence interval: 1.21–1.65; p < 0.001). The PV prevalence predicted by the model was above 10% for diagnoses before the age of 56.8 years. Conclusion: Based on the data presented, we recommend genetic testing by gene panel analysis for sTNBC patients diagnosed before the age of 60 years. Due to the still uncertain estimate for women with sTNBC diagnosed above the age of 60 years, further studies are needed.

ZusammenfassungDie Mammasonografie hat sich seit vielen Jahren neben der Mammografie als wichtige Methode zur Abklärung von Brustbefunden etabliert. Problematisch bleiben jedoch Unterschiede in der Interpretation von Befunden 1 2. Dies vermindert die diagnostische Treffsicherheit der Sonografie nach Detektion eines Befundes, erschwert die interdisziplinäre Kommunikation und den Vergleich von wissenschaftlichen Arbeiten 3. Das American College of Radiology (ACR) hatte 1999 eine Arbeitsgruppe gebildet (International Expert Working Group), die auf der Basis der langjährig etablierten BI-RADS-Klassifizierung von mammografischen Befunden und unter Berücksichtigung von Literaturdaten eine ähnliche Einteilung für die Ultraschalluntersuchung erarbeitet hatte 4. Aufgrund inhaltlicher Unterschiede hatte die DEGUM bereits 2006 einen eigenen BI-RADS-analogen Kriterienkatalog publiziert 3. Die aktuelle 5. Edition des ACR-BI-RADS-Katalogs offenbart neben diesen weiterhin bestehenden inhaltlichen Unterschieden zudem das Problem der formalen Lizenzierung, wird aber inhaltlich von der DEGUM als weiteres Befundbeschreibungs- und Dokumentationssystem anerkannt.Der Arbeitskreis Mammasonografie der DEGUM beabsichtigt mit der „Best Practice Guideline“, den senologisch tätigen Kolleginnen und Kollegen einen aktuellen Dignitätskriterien- und Befundungskatalog sowie „Best Practice“-Empfehlungen zu den verschiedenen Modalitäten an die Hand zu geben.

ZusammenfassungDie Mammasonografie hat sich seit vielen Jahren neben der Mammografie als wichtige Methode zur Abklärung von Brustbefunden etabliert.Der Arbeitskreis Mammasonografie der DEGUM beabsichtigt mit der „Best Practice Guideline“ den senologisch tätigen Kolleginnen und Kollegen neben dem in Teil I publizierten aktuellen Dignitätskriterien- und Befundungskatalog in dem vorliegenden Teil II die additiven und fakultativen Anwendungsmodalitäten zur Abklärung von Brustbefunden zu beschreiben und dazu DEGUM-Empfehlungen zu äußern, um die Differenzialdiagnose von unklaren Läsionen zu erleichtern.Die vorliegende „Best Practice Guideline“ hat sich zum Ziel gesetzt, den Anforderungen zur Qualitätssicherung und der Gewährleistung einer qualitätskontrollierten Durchführung der Mammasonografie nachzukommen. Die wichtigsten Aspekte der Qualitätssicherung werden in diesem Teil II der Best Practice Guideline erläutert.

PDF file, 70K, Availability of information on tumor morphology and receptor status for Europeans, by each included BCAC study.

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PDF file, 57K, Genotyping characteristics of each BCAC participating study.

Background: Our recent genome-wide association study identified a novel breast cancer susceptibility locus at 9q31.2 (rs865686).Methods: To further investigate the rs865686–breast cancer association, we conducted a replication study within the Breast Cancer Association Consortium, which comprises 37 case–control studies (48,394 cases, 50,836 controls).Results: This replication study provides additional strong evidence of an inverse association between rs865686 and breast cancer risk [study-adjusted per G-allele OR, 0.90; 95% confidence interval (CI), 0.88; 0.91, P = 2.01 × 10−29] among women of European ancestry. There were ethnic differences in the estimated minor (G)-allele frequency among controls [0.09, 0.30, and 0.38 among, respectively, Asians, Eastern Europeans, and other Europeans; P for heterogeneity (Phet) = 1.3 × 10−143], but no evidence of ethnic differences in per allele OR (Phet = 0.43). rs865686 was associated with estrogen receptor–positive (ER+) disease (per G-allele OR, 0.89; 95% CI, 0.86–0.91; P = 3.13 × 10−22) but less strongly, if at all, with ER-negative (ER−) disease (OR, 0.98; 95% CI, 0.94–1.02; P = 0.26; Phet = 1.16 × 10−6), with no evidence of independent heterogeneity by progesterone receptor or HER2 status. The strength of the breast cancer association decreased with increasing age at diagnosis, with case-only analysis showing a trend in the number of copies of the G allele with increasing age at diagnosis (P for linear trend = 0.0095), but only among women with ER+ tumors.Conclusions: This study is the first to show that rs865686 is a susceptibility marker for ER+ breast cancer.Impact: The findings further support the view that genetic susceptibility varies according to tumor subtype. Cancer Epidemiol Biomarkers Prev; 21(10); 1783–. ©2012 AACR.

PDF file, 107K, Summary characteristics of the participating BCAC case-control studies.

Purpose A previous study in our breast unit showed that the diagnostic accuracy of intraoperative specimen radiography and its potential to reduce second surgeries in a cohort of patients treated with neoadjuvant chemotherapy were low, which questions the routine use of Conventional specimen radiography (CSR) in this patient group. This is a follow up study in a larger cohort to further evaluate these findings. Methods This retrospective study included 376 cases receiving breast conserving surgery (BCS) after neoadjuvant chemotherapy (NACT) of primary breast cancer. CSR was performed to assess potential margin infiltration and recommend an intraoperative re-excision of any radiologically positive margin. The histological workup of the specimen served as gold standard for the evaluation of the accuracy of CSR and the potential reduction of second surgeries by CSR-guided re-excisions. Results 362 patients with 2172 margins were assessed. The prevalence of positive margins was 102/2172 (4.7%). CSR had a sensitivity of 37.3%, a specificity of 85.6%, a positive predictive value (PPV) of 11,3% and a negative predictive value (NPV) of 96.5%. The rate of secondary procedures was reduced from 75 to 37 with a number needed to treat (NNT) of CSR guided intraoperative re-excisions of 10. In the subgroup of patients with cCR, the prevalence of positive margins was 38/1002 (3.8%), PPV was 6.5% and the NNT was 34. Conclusion This study confirms our previous finding that the rate of secondary surgeries cannot be significantly reduced by CSR-guided intraoperative re-excisions in cases with cCR after NACT. The routine use CSR after NACT is questionable and alternative tools of intraoperative margin assessment should be evaluated.

The aim of this preclinical study was to test if the organic coating of two breast tissue clip markers (HydroMARK and UltraClip) can be sampled selectively by an ultrasound-guided core cut biopsy (CC). Ten clip markers of each type were inserted in four turkey breasts. Sonographic visibility was graded by the performing physicians. CC was performed, aiming to sample only the organic coating while leaving the clip marker in situ. Mammography, specimen radiography and gross inspection of the biopsy samples were conducted to evaluate dislocation or removal of the clips. The specimens were examined histopathologically to detect fragments of the coating material. HydroMARK was superior regarding CC feasibility and detectability of the coating. The organic coating of HydroMARK could be sampled selectively without dislocating the clip marker. Fragments of the coating material were visible macroscopically in seven out of ten biopsy specimens and could be detected in the histopathologic workup in five out of ten specimens. The coating material of the UltraClip was not visible in any sample. This study showed that the organic coating could be identified in CC biopsy samples, both on a macroscopic and microscopic level. This could potentially be used to verify representativity of minimal invasive biopsies.

Background: Pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is associated with beneficial long-term outcome in early breast cancer (EBC). pCR is defined as ypT0/is, ypN0. It is well known that pCR rates depend on tumor subtype. However, the impact of different therapy regimens, dose delays and dose reductions on pCR rates is still unclear. This retrospective study analyzed the therapy dose of patients with pCR and non-pCR after NACT for EBC using referenced and delivered summation dose intensity product (SDIP) and relative dose intensity (RDI) calculations. Methods: SDIP of different therapy regimens were calculated by defining a unit dose intensity (UDI) for each therapy (Hryniuk et al. JCO 1998). The UDI is defined as the dose in mg/m2/week that produces a 30% complete or partial remission rate as a single agent in first-line therapy for metastatic breast cancer. For each regimen, the planned dose intensities (PDI) were divided by the UDI for every single drug. The summation dose intensity (SDI) is the addition of the resulting decimal fractions. Multiplying the SDI by the treatment intervals and number of cycles gives the SDIP. SDIP can be divided into referenced SDIP (rSDIP) and delivered SDIP (dSDIP). RDI is the ratio of dSDIP in comparison to rSDIP. Therapy dose calculations were performed for patients who received NACT for EBC at the National Center for Tumor Diseases (NCT) Heidelberg, Germany, between 01/2015 and 08/2019. Results: 590 patients (median age 51 years) were included, median follow up was 38 months, 225 patients (38.1%) achieved pCR. 65 patients (11.0%) were hormone receptor positive HER2 negative (HR+HER2-), 164 (27.8%) were HR-HER2-, 133 (22.5%) were HR+HER2+, 97 (16.4%) were HR-HER2+. Significant difference between the pCR and non-pCR group was observed for HR-status (p Citation Format: Thomas M Deutsch, Michelle Kobel, Manuel Feisst, Fabian Riedel, Katharina Smetanay, Carlo Fremd, Laura Michel, Michael Golatta, Joerg Heil, Markus Wallwiener, Andreas Schneeweiss. Impact of summation dose intensity product on pathologic response in patients receiving neoadjuvant chemotherapy for early breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-12-08.

Background: Breast ultrasound identifies additional carcinomas not detected in mammography, but has a higher rate of false-positive findings which result in more unnecessary breast biopsies. Shear-Wave Elastography (SWE), an ultrasound technique used to quantify the stiffness of a lesion, showed promising results to improve the diagnostic performance of B-mode breast ultrasound but also to miss some cancers. As the stiffness of a lesion is found to be influenced by individual patient characteristics, incorporation of lesion stiffness in more individualized assessments may be key to the problem of reducing unnecessary breast biopsies without impairing the breast cancer detection rate. Thus, in this study, we evaluated whether an intelligent algorithm incorporating traditional SWE values as well as other patient and clinical variables (hereafter “intelligent SWE”) could reduce the number of unnecessary breast biopsies without impairing the breast cancer detection rate compared to traditional SWE and B-mode breast ultrasound for patients with suspicious breast lesions. Methods: We trained, tested, and validated machine learning algorithms using patient, clinical, ultrasound, and SWE information to classify breast masses. We used international, multicenter data from 857 women with BI-RADS 4 breast masses at 12 study sites in 7 countries. Patients underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation. 10-fold cross-validation was used to train and test the algorithms on data from 11 of the 12 sites which were further validated using the additional site’s data. The results of B-mode breast ultrasound, traditional SWE, and intelligent SWE were compared to the gold standard of histopathologic evaluation. We calculated sensitivity, specificity, and AUROC and used McNemar tests to test for significant differences in diagnostic performance. Results: The mean age was 49.5 years (SD 16.3) and 42.2% breast masses (n=362 of 857) were found to be malignant as confirmed by histopathology. In the external validation set (n=285), traditional SWE showed a significantly higher diagnostic performance compared to B-mode breast ultrasound (P < 0.001), whereas intelligent SWE outperformed both B-mode breast ultrasound and traditional SWE (P < 0.001). The neural network algorithm showed a significantly higher diagnostic performance compared to the Logistic Regression with Elastic Net Penalty (P = 0.004). The neural network algorithm achieved a sensitivity of 100% (95% CI 97.1 to 100%, 126 of 126) and a specificity of 50.3% (95% CI 42.3 to 58.3%, 80 of 159); the number of unnecessary biopsies were reduced by 50.3% (79 vs. 159) without missing any cancer compared to B-mode breast ultrasound. Model-agnostic variable importance plots to provide insights into the model predictions showed that the three most important variables for intelligent SWE were patient age followed by Shear-Wave velocity and orientation of the lesion (parallel vs. not parallel) in B-mode ultrasound. Conclusion: This is the first evidence which suggests that the majority of false-positive breast biopsies could be safely avoided by using intelligent SWE without impairing breast cancer detection rates. These results may be helpful in their ability to reduce treatment burden for patients, providers, and healthcare systems. Trial registration: NCT02638935. Funding: Siemens Medical Solutions USA, Inc Diagnostic Performance ComparisonB-mode Breast UltrasoundTraditional Shear-Wave ElastographyIntelligent Shear-Wave Elastography – Logistic Regression with Elastic Net PenaltyIntelligent Shear-Wave Elastography – neural networkAUROC – value (95% CI)–0.84 (0.79-0.89)0.93 (0.90-0.95)0.93 (0.90-0.96)Sensitivity – % (95% CI); no.100% (97.1-100%); 126 of 12697.6% (93.2-99.5%); 123 of 126100% (97.1-100%); 126 of 126100% (97.1-100%); 126 of 126Specificity – % (95% CI); no.0% (0.0-2.3%); 0 of 15923.9% (17.5-31.3%); 38 of 15936.5% (29.0-44.5%); 58 of 15950.3% (42.3-58.3%); 80 of 159Negative predictive value – % (95% CI); no.–92.7% (80.1-98.5%); 38 of 41100% (93.8-100%); 58 of 58100% (95.5-100%); 80 of 80Positive predictive value – % (95% CI); no.44.2% (38.4-50.2); 126 of 28550.4% (44.0-56.8%); 123 of 24455.5% (48.8-62.1%); 126 of 22761.5% (54.4-68.2%); 126 of 205 Citation Format: André Pfob, Chris Sidey-Gibbons, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Chi Ho, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Joerg Heil, Michael Golatta. Intelligent shear-wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): An international, multicenter analysis [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-05.

Aim This update of the interdisciplinary S3 guideline on the Diagnosis, Therapy and Follow-up of Cervical Cancer (AWMF Registry No. 032/033OL) was published in March 2021. This updated guideline was funded by German Cancer Aid (Deutsche Krebshilfe) as part of the German Guideline Program in Oncology. The guideline was coordinated by the German Society of Gynecology and Obstetrics (Deutsche Gesellschaft für Gynäkologie und Geburtshilfe, DGGG) and the Working Group on Gynecological Oncology (Arbeitsgemeinschaft Gynäkologische Onkologie, AGO) of the German Cancer Society (Deutsche Krebsgesellschaft, DKG). Method The process of updating the S3 guideline dating from 2014 was based on an appraisal of the available evidence using the criteria of evidence-based medicine, adaptations of existing evidence-based national and international guidelines or – if evidence was lacking – on a consensus of the specialists involved in compiling the update. After an initial review of the current literature was carried out according to a prescribed algorithm, several areas were identified which, in contrast to the predecessor version from September 2014, required new recommendations or statements which took account of more recently published literature and the appraisal of the new evidence. Recommendations The short version of this guideline consists of recommendations and statements on the epidemiology, screening, diagnostic workup and therapy of patients with cervical cancer. The most important new aspects included in this updated guideline include the newly published FIGO classification of 2018, the radical open surgery approach for cervical cancers up to FIGO stage IB1, and use of the sentinel lymph node technique for tumors ≤ 2 cm. Other changes include the use of PET-CT, new options in radiotherapy (e.g., intensity-modulated radiotherapy, image-guided adaptive brachytherapy), and drug therapies to treat recurrence or metastasis.

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.

BACKGROUND. Supplemental screening breast ultrasound (US) detects additional cancers in women with dense breasts but identifies many BI-RADS 3 lesions that result in short-term follow-up and biopsi...

Background We recently published 2-year results of the prospective, randomized IMRT-MC2 trial, showing non-inferior local control and cosmesis in breast cancer patients after conventionally fractionated intensity-modulated radiotherapy with simultaneously integrated boost (IMRT-SIB), compared to 3D-conformal radiotherapy with sequential boost (3D-CRT-seqB). Here, we report on 2-year quality of life results. Patients and Methods 502 patients were enrolled and randomized to IMRT-SIB (50.4 Gy in 1.8 Gy fractions with a 64.4 Gy SIB to the tumor bed) or to 3D-CRT-seqB (50.4 Gy in 1.8 Gy fractions, followed by a sequential boost of 16 Gy in 2 Gy fractions). For quality of life (QoL) assessment, patients completed the QLQ-C30 and QLQ-BR23 questionnaires at baseline, 6 weeks and 2 years after radiotherapy. Results Significant differences between treatment arms were seen 6 weeks after radiotherapy for pain (22.3 points for IMRT vs. 27.0 points for 3D-CRT-seqB; p = 0.033) and arm symptoms (18.1 points for IMRT vs. 23.6 points for 3D-CRT-seqB; p = 0.013), both favoring IMRT-SIB. Compared to baseline values, both arms showed significant improvement in global score (IMRT: p = 0.009; 3D-CRT: p = 0.001) after 2 years, with slight deterioration on the role (IMRT: p = 0.008; 3-D-CRT: p = 0.001) and social functioning (IMRT: p = 0.013, 3D-CRT: p = 0.001) as well as the future perspectives scale (IMRT: p = 0.003; 3D-CRT: p = 0.0034). Conclusion This is the first randomized phase III trial demonstrating that IMRT-SIB was associated with slightly superior QoL compared to 3-D-CRT-seqB. These findings further support the clinical implementation of SIB in adjuvant breast cancer treatment.

Purpose: Today, the decision to treat patients with chemotherapy for early breast cancer (EBC) is made based on the patient’s individual risk stratification and tumor biology. In cases with chemotherapy indication, the neoadjuvant application (NACT) is the preferred option in comparison with primary surgery and adjuvant chemotherapy (ACT). Age remains a relevant factor in the decision-making process. The aim of the present study was to illustrate the impact of age on the use of systemic therapy in clinical routine. Methods: The study separately analyzed chemotherapy use among six age cohorts of EBC patients who had been treated at 104 German breast units between January 2008 and December 2017. Results: In total, 124,084 patients were included, 46,279 (37.3%) of whom had received chemotherapy. For 44,765 of these cases, detailed information on treatment was available. Within this cohort, chemotherapy was administered as NACT to 14,783 patients (33.0%) and as ACT to 29,982 (67.0%) patients. Due to the higher prevalence of unfavorable tumor subtypes, younger patients had a higher rate of chemotherapy (≤29y: 74.2%; 30–39y: 71.3%) and a higher proportion of NACT administration (Conclusion: The data from the nationwide German cohort reveal relevant age-dependent discrepancies concerning the use of chemotherapy for EBC. n = 250 words

Today, the decision to treat patients with chemotherapy for early breast cancer (EBC) is made based on the patient's individual risk stratification and tumor biology. In cases with chemotherapy indication, the neoadjuvant application (NACT) is the preferred option in comparison with primary surgery and adjuvant chemotherapy (ACT). Age remains a relevant factor in the decision-making process. The aim of the present study was to illustrate the impact of age on the use of systemic therapy in clinical routine.The study separately analyzed chemotherapy use among six age cohorts of EBC patients who had been treated at 104 German breast units between January 2008 and December 2017.In total, 124,084 patients were included, 46,279 (37.3%) of whom had received chemotherapy. For 44,765 of these cases, detailed information on treatment was available. Within this cohort, chemotherapy was administered as NACT to 14,783 patients (33.0%) and as ACT to 29,982 (67.0%) patients. Due to the higher prevalence of unfavorable tumor subtypes, younger patients had a higher rate of chemotherapy (≤ 29y: 74.2%; 30-39y: 71.3%) and a higher proportion of NACT administration ( ≤ 29y: 66.9%; 30-39y: 56.0%) in comparison with elderly patients, who had lower rates for overall chemotherapy (60-69y: 37.5%; ≥ 70y: 17.6%) and NACT (60-69y: 25.5%; ≥ 70y: 22.8%). Pathologic complete response was higher in younger than in older patients (≤ 29y: 30.4% vs. ≥ 70y: 16.7%), especially for HER2- subtypes.The data from the nationwide German cohort reveal relevant age-dependent discrepancies concerning the use of chemotherapy for EBC.

Objectives: To define reference values for shear wave elastography (SWE) in unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination. Methods: In total, 177 clinically and sonographically unsuspicious axillary lymph nodes were prospectively evaluated with SWE using Virtual Touch Tissue Imaging Quantification (VTIQ) in 175 women. Mean values of tissue stiffness for axillary fatty tissue, lymph node cortex, and lymph node hilus were measured. Additionally, test-retest reliability of SWE in the assessment of axillary lymph node stiffness was evaluated by repeating each measurement three times. Results: In 177 axillary lymph nodes, the mean stiffness of lymph node cortex, hilus, and surrounding fatty tissue as quantified by SWE was 1.90 m/s (SD: 0.34 m/s), 2.02 m/s (SD: 0.37 m/s), and 1.75 m/s (SD: 0.38 m/s), respectively. The mean stiffness of cortex and hilus was significantly higher compared to fatty tissue (p < 0.0001). SWE demonstrated good test–retest reliability in the assessment of stiffness of the lymph node hilus, cortex, and the surrounding fatty tissue with an intraclass correlation of 0.79 (95% CI: 0.75; 0.83), 0.75 (95% CI: 0.70; 0.79), and 0.78 (95% CI: 0.74; 0.82), respectively, (p < 0.0001). Conclusions: Reference values for SWE in unsuspicious axillary lymph nodes are determined. These results may help to better identify axillary lymph node metastasis for breast cancer patients when combined with other lymph node features. SWE is a reliable method for the objective quantification of tissue stiffness of axillary lymph nodes. Advances in knowledge: This study presents physiological reference values for tissue stiffness by examining the axillary lymph nodes with SWE in 175 women with sonomorphologically unsuspicious lymph nodes.

Background: Neoadjuvant chemotherapy (NACT) is standard of care for patients with triple-negative breast cancer (TNBC). Treatment response, especially pathologic complete response (pCR), is a strong predictive factor for treatment outcome. In the setting of up-front surgery, retrospective data have suggested improved outcome in patients with early TNBC that received breast-conserving surgery with adjuvant radiotherapy (BCT) as compared to mastectomy. Methods: We identified 2632 patients with early TNBC from the German Breast Group meta-database. Patients with cT1-2 cN0 and ypN0, available surgery and follow-up data were eligible for this project. A total of 1074 patients from 8 prospective NACT trials were analyzed. Endpoints of interest were locoregional recurrence as first site of relapse (LRR, other sites of recurrence were considered competing events), disease-free survival (DFS) and overall survival (OS); analyses were performed using univariate and multivariate Fine-Gray (for LRR) and Cox models including study, age, cT, surgery type and pCR. For the analyses including pCR as covariable, only patients at risk at the landmark time were evaluated. Results: Median age was 48 years, 69.6% of patients had cT2 tumors and 85.3% underwent BCS. Of the 1074 analyzed patients, 48.8% achieved pCR. After a median follow-up of 64 months, there were 94 (8.8%) locoregional events as first site of relapse. Upon univariate analysis, absence of pCR (hazard ratio [HR]=2.28; 95%CI 1.44-3.61; p< 0.001) and ypT-stage (ypT0/is vs. ypT1-3, HR=0.61; 95%CI 0.40-0.95; p=0.028) were significantly associated with LRR, while type of surgery, age and cT-stage were not. Upon multivariate analysis, absence of pCR was the only factor associated with increased risk of LRR (HR=2.22; 95%CI 1.38-3.58; p=0.001). Patients that underwent mastectomy (N=158) were significantly younger (age ≤ 50 years 72.8% vs. 59.9% for BCT [N=916]; p=0.002) DFS and OS was significantly better in patients who underwent BCT compared to mastectomy (DFS: HR=0.47; 95%CI 0.34-0.66; p< 0.001 and OS: HR=0.40; 95%CI 0.26-0.63; p< 0.001). In multivariate analysis, BCT was associated with a significantly better DFS and OS as compared to mastectomy (DFS: HR=0.51; 95%CI 0.36-0.72; p< 0.001; and OS HR=0.43; 95%CI 0.27-0.68; p< 0.001), whereas absence of pCR was associated with significantly worse DFS and OS (DFS: HR=2.43; 95%CI 1.78-3.31; p< 0.001; and OS: HR=3.15; 95%CI 1.94-5.10; p< 0.001). Conclusions: In this retrospective analysis from the GBG meta-database, treatment response, e.g. pCR, was the main determinant of locoregional recurrence in patients with early stage TNBC treated with NACT. BCT was associated with improved DFS and OS compared to mastectomy, which may at least in part reflect favorable patient selection. Citation Format: David Krug, Valentina Vladimirova, Michael Untch, Thorsten Kühn, Andreas Schneeweiss, Carsten Denkert, Beyhan Ataseven, Christine Solbach, Bernd Gerber, Hans Tesch, Michael Golatta, Sabine Seiler, Jörg Heil, Valentina Nekljudova, Sibylle Loibl. PD15-06 Pathologic complete response and breast-conserving surgery are associated with improved prognosis in patients with early-stage triple-negative breast cancer treated with neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD15-06.

Background Over the past decade, chemotherapy has been used more selectively in early breast cancer (EBC) due to better risk stratification. Neoadjuvant chemotherapy (NACT) has evolved to the primary treatment option. The type and size of hospitals is known to have a substantial influence on the kinds of treatment they provide, and therefore on patient outcomes (e.g. rates for pathological complete response, pCR), but it is not yet known how this has affected delivery of chemotherapy for EBC in Germany. Methods This study analyzed chemotherapy use and pCR rates after NACT for EBC patients treated at 104 German institutions 2008–2017. Institutions were separated into associated hospital type (university hospital; teaching hospital; community hospital) and annual caseload (≤100; 101–250; >250 cases/year). Results Overall, 124,084 patients were included, of whom 11.6% were treated at university hospitals, 63.1% at teaching hospitals, and 25.3% at community hospitals. In total, 46,274 (37.3%) received chemotherapy, of whom 44,765 had information available about systemic treatment and surgery. From 2008 to 2017, chemotherapy use declined from 48.3% to 36.4% for university hospitals, from 40.7% to 30.3% for teaching hospitals, and from 42.4% to 33.7% for community hospitals. Furthermore, the proportion of NACT increased the most in university hospitals (from 32.0% to 68.1%); whereas, the rate of pCR (defined as ypT0 ypN0) increased irrespective of institutional type. Analyses regarding annual caseload did not show any differences. Conclusions The results from this large, nationwide cohort reflect a more selective use of chemotherapy in Germany, irrespective of institutional type or case load.

Purpose Breast cancer (BC) is the most common malignancy among women and prognosis is strongly influenced by tumor subtype. Neoadjuvant chemotherapy (NAC) is the standard treatment for both locally advanced- and early-stage triple-negative and Her2-positive BC. Pathologic complete response (pCR) to NAC is an important predictor of patient outcomes. Neutrophil-to-lymphocyte-ratio (NLR) in peripheral blood is associated with prognosis in various malignancies. Here, we investigated the value of the pretreatment NLR as a response predictor in neoadjuvant-treated patients with BC. Methods A retrospective chart analysis of 862 patients with invasive BC treated with NAC at the Heidelberg University Hospital during 2003–2015 was conducted. NLR was calculated as the ratio of the absolute neutrophil and lymphocyte counts in peripheral blood, and pCR was defined as absence of invasive or in situ carcinoma in breast and axillary lymph nodes. Results A total of 151 patients with invasive BC who underwent NAC were included in this study. NLR tended to be higher in the pCR group than the non-pCR group (p p = 0.048) in patients with luminal B/Her2-negative tumors. Further, we found a significant difference in NLR according to remission status in postmenopausal patients (2.861 vs. 2.313, respectively; p = 0.043). Conclusion NLR was significantly higher only for patients achieving pCR in the Luminal B/Her2-negative and postmenopausal subgroups. Hence, NLR is a candidate additional predictive factor in patients with Luminal B/Her2-negative BC.

Purpose: Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in an average of 35% of women with breast cancer. In such cases, breast surgery may be considered overtreatment. However, imaging and vacuum-assisted biopsy (VAB) alone showed high rates of missed cancer compared to standard breast surgery. We therefore evaluated multivariate algorithms using patient, tumor, and VAB variables to accurately identify patients with breast pCR. Methods: We developed and tested three multivariate approaches: elastic net regression, Support Vector Machines (SVM), and a deep neural network. We analyzed 452 patients, randomly partitioned into training and test samples (2:1 ratio), who participated in three prospective studies assessing the feasibility of VAB to accurately detect residual disease after neoadjuvant systemic treatment (NST). The studies were conducted at 23 sites in the United States, Germany, and South Korea. The trials enrolled women who presented with clinical stage I-III breast cancer of any biological subtype and a partial or complete response to NST confirmed by ultrasonography, mammography, or magnetic resonance imaging; all patients underwent guideline-adherent surgery. We compared the performance of the multivariate algorithms to the histopathologic evaluation of disease response in the surgical specimen (reference standard) - false-negative rate (FNR, missed residual cancer) and specificity (identification of breast pCR) were the main outcome measures. The best performing algorithm on the test set with respect to sensitivity and specificity was validated using data of an independent fourth trial. We compared the performance of the multivariate approaches to the performance of imaging and/or VAB. Results: In the test set (n=152), elastic net regression, SVM and the neural network revealed an FNR of 1.2% (1 of 85 patients with missed residual disease). Specificity of the elastic net regression was 46.3% (31 of 67 women with surgically confirmed breast pCR identified), of the SVM 62.7% (42 of 67) and of the neural network 67.2% (45 of 67). All multivariate algorithms performed better than imaging or VAB: FNR 25.9% (22 of 85) and 16.5% (14 of 85), respectively. Subsequent external validation (n=50) of the neural network algorithm showed a false-negative rate of 0% (0 of 27) and a specificity of 65.2% (15 of 23). The area under the ROC curve for the deep neural network was 0.97 (95% CI, 0.94 to 1.00). Analyzing the coefficients of the elastic net regression (regularized beta; ß) showed that the lesion diameter on imaging after NST (ß = 0.31) and VAB results (ß = 0.49) were the most important variables in the prediction of residual tumor. Other variables were also important: age (ß = 0.18), in-situ in the initial diagnostic (not VAB) biopsy (ß = 0.11), difficulties during the pathologic evaluation of the VAB specimen (ß = 0.11); needle size 7G (ß = -0.06, as opposed to 8G, 9G, 10G), multicentricity on imaging after NST (ß = 0.06), hormone-receptor positivity (ß = 0.01), and a clip marker positioned within the (former) lesion (ß = -0.01, as opposed to a clip marker positioned 5mm from the lesion Conclusion: A multivariate algorithm can accurately select breast cancer patients without residual disease after neoadjuvant treatment. This finding may pave the way to study omission of breast surgery in these patients in the future. Performance of multivariate algorithms compared to imaging and vacuum-assisted biopsyFalse-negative rate - value (95% CI)Specificity - value (95% CI)Negative predictive value - value (95% CI)Positive predictive value - value (95% CI)Test set (n=152)Imaging25.9% (17.0-36.5%)61.2% (48.5-72.9%)65.1% (52.0-76.7%)70.8% (60.2-79.9%)VAB16.5% (9.3-26.1%)89.6% (79.7-95.7%)81.1% (70.3-89.3%)91.0% (82.4-96.3%)Imaging + VAB5.9% (1.9-13.2%)52.2% (39.7-64.6)87.5% (73.2-95.8%)71.4% (62.1-79.6%)Elastic net regression1.2% (0.0-6.4%)46.3% (34.0-58.9%)96.9% (83.8-99.9%)70.0% (61.0%-78.0%)Support Vector Machine1.2% (0.0-6.4%)62.7% (50.0 - 74.2%)97.7% (87.7-99.9%)77.1% (68.0-84.6%)Deep Neural Network1.2% (0.0-6.4%)67.2% (54.6-78.2%)97.8% (88.5-99.9%)79.3% (70.3-86.5%)Validation set (n=50)Deep Neural Network0.0% (0.0-12.8%)65.2% (42.7-83.6%)100% (78.2-100%)77.1% (59.9-89.6%) Citation Format: André Pfob, Chris Sidey-Gibbons, Han-Byoel Lee, Marios Konstantinos Tasoulis, Vivian Koelbel, Michael Golatta, Gaiane M. Rauch, Benjamin D. Smith, Vicente Valero, Fiona MacNeill, Wonshik Han, Walter Paul Weber, Geraldine Rauch, Henry Kuerer, Joerg Heil. Identify breast cancer patients with pathologic complete response in the breast after neoadjuvant systemic treatment - an international, multicenter analysis [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS2-42.

Background and objectives: The Breast Imaging Reporting and Data System (BI-RADS) provides a standardized way to describe ultrasound images in breast cancer diagnostics. However, there is little information which descriptors are most strongly associated with malignancy and to which extend the single descriptors (tissue composition, shape, orientation, margin of lesion, echo pattern, posterior features, and calcifications) should be considered for the final evaluation of risk of malignancy. Thus, we aimed to identify which BI-RADS descriptors are most strongly associated with malignancy when evaluating ultrasound images in breast cancer diagnostics. Methods: This multicenter, prospective trial took place at 11 trial sites in Austria, France, Germany, Japan, Netherlands, Portugal, and the US from February 2016 to March 2019. The trial enrolled 1288 women presenting with a lesion ≥0.5 and ≤5 cm in 2D B-mode ultrasound. The examiner conducted a routine 2D B-mode ultrasound examination and had additional standard information about the patients’ disease history and family history. The examiner described the ultrasound images according to BI-RADS. All patients underwent histopathological confirmation which was the gold standard against which the clinical examiner was compared. We performed univariate and multivariate analyses using descriptive statistics, Chi-Square test, and logistic regression to identify which image descriptors are associated with malignancy. Results: Histopathologic evaluation showed malignancy in 368 of 1288 lesions (28.6%). The descriptors most strongly associated with malignancy were spiculated margins (rate of malignancy 84.9%; 79 of 93), calcification (69.9%; 51 of 73), un-parallel orientation (65.9%; 187 of 284), angular margins (64.6%; 64 of 99), posterior shadowing (62.4%; 88 of 142), irregular shape (55.2%; 208 of 377), and indistinct margins (52.0%; 185 of 356). Different tissue compositions and echo patterns were least useful to distinguish between malign and benign lesions. Upon multivariate analysis, calcifications (OR 5.52; 95% CI 1.94-15.87) and posterior shadowing (OR 16.13; 95% CI 2.75-90.91) remained significantly (p Conclusion: We identified which BI-RADS descriptors are most strongly associated with malignancy when evaluating ultrasound images in breast cancer diagnostics. Future research may look into providing not only a standardized image description but also a standardized final evaluation for the rate of malignancy with respect to the different predictive usefulness of the single descriptors. This may further standardize and objectify the risk evaluation in breast cancer diagnostics. Trial registration: NCT02638935 Table 1: Association of BI-RADS descriptors with final histopathologic resultsbenign pathologymalignant pathologyp-valuetissue compositionp Citation Format: André Pfob, Richard G. Barr, Volker Duda, Christopher Buesch, Joerg Heil, Michael Golatta. Identifying the most relevant descriptors when evaluating ultrasound images in breast cancer diagnostics: A secondary analysis of an international multicenter trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-16.

Background and objectives: The Breast Imaging Reporting and Data System (BI-RADS) has helped to standardize radiologic reports and assessment in breast cancer diagnostics. So far, BI-RADS consists of the sole, standardized description of images. Individual patient characteristics like disease and family history or age are no part of the current BI-RADS classification system but are often subjectively considered to evaluate the risk of breast cancer in the clinical setting. It is however unclear how and to which extent such additional patient information influence the evaluation of risk of malignancy. Thus, we compared the performance in the detection of breast cancer between the sole analysis of ultrasound images by physician experts and a physician actually examining and counseling a patient in the clinical setting. Methods: This multicenter, prospective trial took place at 11 trial sites in Austria, France, Germany, Japan, Netherlands, Portugal, and the US from February 2016 to March 2019. The trial enrolled 1288 women presenting with a lesion ≥0.5 and ≤5 cm in 2D B-mode ultrasound. In the clinical setting, the examiner conducted a routine 2D B-mode ultrasound examination and had additional standard information about the patients’ disease history and family history. The final ultrasound images made in the clinical routine (annotated with size measurements) but not any other information about the patient was given to three physician experts (>15 years of experience in breast cancer diagnostics). The examiner in the clinical setting and each of the three experts evaluated the ultrasound images according to BI-RADS and gave a likelihood score for malignancy according to ACR (American College of Radiology). Following the BI-RADS definition by ACR, malignancy was assumed for a likelihood of malignancy >2% (BI-RADS 4 or higher). All patients underwent histopathological confirmation which was the gold standard against which the clinical examiner and the three experts were compared. AUC, sensitivity, specificity, negative-predictive value (NPV), and positive-predictive value (PPV) were the performance measures. Results: Histopathologic evaluation showed malignancy in 368 of 1288 lesions (28.6%). AUC of the examiner in the clinical setting (AUC=0.94; 95% CI 0.92-0.95) was significantly better as for all three experts evaluating images only: expert one AUC=0.78 (95% CI 0.75-0.81); expert two AUC=0.81 (95% CI 0.78-0.84); expert three AUC=0.83 (95% CI 0.80-0.86). Sensitivity, specificity, NPV, and PPV of the examiner in the clinical setting were better as for all three experts evaluating images only. NPV of the examiner in the clinical setting was 98.6% (425 of 431), for expert one 87.8% (381 of 434), for expert two 91.2% (198 of 217), and for expert 3 84.1% (413 of 491). Conclusion: Our findings suggest that information about individual patient characteristics (e.g. age, disease and family history) has great influence to accurately evaluate the risk of breast cancer. Future research may look into incorporating not only a standardized description of images into the BI-RADS classification system but also a standardized description of these individual patient characteristics to further standardize and objectify the risk evaluation in breast cancer diagnostics. Trial registration: NCT02638935 Performance of the examiner in the clinical setting and the three experts evaluating images onlyExaminer clinical settingImages only – Expert 1Images only – Expert 2Images only – Expert 3AUC (95% CI)0.94 (0.92-0.95)0.78 (0.75-0.81)0.81 (0.78-0.84)0.83 (0.80-0.86)Sensitivity –% (no.)98.4% (362 of 368)85.6% (315 of 368)94.8% (349 of 368)78.8% (290 of 368)Specificity –% (no.)46.2% (425 of 920)41.4% (381 of 920)21.5% (198 of 920)44.9% (413 of 920)Negative Predictive Value –% (no.)98.6% (425 of 431)87.8% (381 of 434)91.2% (198 of 217)84.1% (413 of 491)Positive Predictive Value –% (no.)42.2% (362 of 857)36.9% (315 of 854)32.6% (349 of 1071)36.4% (290 of 797) Citation Format: André Pfob, Richard G. Barr, Volker Duda, Christopher Buesch, Joerg Heil, Michael Golatta. Differences in the diagnostic performance of breast ultrasound with or without additional patient information: A secondary analysis of an international multicenter trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-18.

Background Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in about 35% of women with breast cancer. In such cases, breast surgery may be considered overtreatment. We evaluated multivariate algorithms using patient, tumor, and vacuum-assisted biopsy (VAB) variables to identify patients with breast pCR. Methods We developed and tested four multivariate algorithms: a logistic regression with elastic net penalty, an Extreme Gradient Boosting (XGBoost) tree, Support Vector Machines (SVM), and neural network. We used data from 457 women, randomly partitioned into training and test set (2:1), enrolled in three trials with stage 1–3 breast cancer, undergoing VAB before surgery. False-negative rate (FNR) and specificity were the main outcome measures. The best performing algorithm was validated in an independent fourth trial. Results In the test set (n = 152), the logistic regression with elastic net penalty, XGboost tree, SVM, and neural network revealed an FNR of 1.2% (1 of 85 patients with missed residual cancer). Specificity of the logistic regression with elastic net penalty was 52.2% (35 of 67 women with surgically confirmed breast pCR identified), of the XGBoost tree 55.2% (37 of 67), of SVM 62.7% (42 of 67), and of the neural network 67.2% (45 of 67). External validation (n = 50) of the neural network showed an FNR of 0% (0 of 27) and a specificity of 65.2% (15 of 23). Area under the ROC curve for the neural network was 0.97 (95% CI, 0.94–1.00). Conclusion A multivariate algorithm can accurately select breast cancer patients without residual cancer after neoadjuvant treatment.

PURPOSENeoadjuvant systemic treatment (NST) elicits a pathologic complete response in 40%-70% of women with breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, nonsurgical approaches, including imaging or vacuum-assisted biopsy (VAB), were not able to accurately identify patients without residual cancer in the breast or axilla. We evaluated the feasibility of a machine learning algorithm (intelligent VAB) to identify exceptional responders to NST.METHODSWe trained, tested, and validated a machine learning algorithm using patient, imaging, tumor, and VAB variables to detect residual cancer after NST (ypT+ or in situ or ypN+) before surgery. We used data from 318 women with cT1-3, cN0 or +, human epidermal growth factor receptor 2–positive, triple-negative, or high-proliferative Luminal B–like breast cancer who underwent VAB before surgery (ClinicalTrials.gov identifier: NCT02948764 , RESPONDER trial). We used 10-fold cross-validation to train and test the algorithm, which was then externally validated using data of an independent trial (ClinicalTrials.gov identifier: NCT02575612 ). We compared findings with the histopathologic evaluation of the surgical specimen. We considered false-negative rate (FNR) and specificity to be the main outcomes.RESULTSIn the development set (n = 318) and external validation set (n = 45), the intelligent VAB showed an FNR of 0.0%-5.2%, a specificity of 37.5%-40.0%, and an area under the receiver operating characteristic curve of 0.91-0.92 to detect residual cancer (ypT+ or in situ or ypN+) after NST. Spiegelhalter's Z confirmed a well-calibrated model ( z score –0.746, P = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both.CONCLUSIONAn intelligent VAB algorithm can reliably exclude residual cancer after NST. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials.

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.

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.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.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.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.