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Introduction: Decision making in surgical oncology of the breast has increased its complexity over the last twenty years. This Delphi survey investigates the opinion of an expert panel about the decision making process in surgical procedures on the breast for oncological purposes. Methods: Twenty-seven experts were invited to partake into a Delphi Survey. At the first round they have been asked to provide a list of features involved in the decision making process (patient's characteristics; disease characteristics; surgical techniques, outcomes) and comment on it. Using text-mining techniques we extracted a list of mono-bi-trigrams potentially representative of decision drivers. A technique of “natural language processing” called Word2vec was used to validate changes to texts using synonyms and plesionyms. Word2Vec was also used to test the semantic relevance of n-grams within a corpus of knowledge made up of books edited by panel members. The final list of variables extracted was submitted to the judgement of the panel for final validation at the second round of the Delphi using closed ended questions. Results: 52 features out of 59 have been approved by the panel. The overall consensus was 87.1% Conclusions: Text mining and natural language processing allowed the extraction of a number of decision drivers and outcomes as part of the decision making process in surgical oncology on the breast. This result was obtained transforming narrative texts into structured data. The high level of consensus among experts provided validation to this process.

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.

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Background: The single-nucleotide polymorphism (SNP) 5p12-rs10941679 has been found to be associated with risk of breast cancer, particularly estrogen receptor (ER)-positive disease. We aimed to further explore this association overall, and by tumor histopathology, in the Breast Cancer Association Consortium.Methods: Data were combined from 37 studies, including 40,972 invasive cases, 1,398 cases of ductal carcinoma in situ (DCIS), and 46,334 controls, all of white European ancestry, as well as 3,007 invasive cases and 2,337 controls of Asian ancestry. Associations overall and by tumor invasiveness and histopathology were assessed using logistic regression.Results: For white Europeans, the per-allele OR associated with 5p12-rs10941679 was 1.11 (95% CI = 1.08–1.14, P = 7 × 10−18) for invasive breast cancer and 1.10 (95% CI = 1.01–1.21, P = 0.03) for DCIS. For Asian women, the estimated OR for invasive disease was similar (OR = 1.07, 95%CI = 0.99–1.15, P = 0.09). Further analyses suggested that the association in white Europeans was largely limited to progesterone receptor (PR)-positive disease (per-allele OR = 1.16, 95% CI = 1.12–1.20, P = 1 × 10−18 vs. OR = 1.03, 95% CI = 0.99–1.07, P = 0.2 for PR-negative disease; Pheterogeneity = 2 × 10−7); heterogeneity by ER status was not observed (P = 0.2) once PR status was accounted for. The association was also stronger for lower grade tumors [per-allele OR (95% CI) = 1.20 (1.14–1.25), 1.13 (1.09–1.16), and 1.04 (0.99–1.08) for grade 1, 2, and 3/4, respectively; Ptrend = 5 × 10−7].Conclusion: 5p12 is a breast cancer susceptibility locus for PR-positive, lower grade breast cancer.Impact: Multicenter fine-mapping studies of this region are needed as a first step to identifying the causal variant or variants. Cancer Epidemiol Biomarkers Prev; 20(10); 2222–31. ©2011 AACR.

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De-escalation of surgery has been central in the evolution of multidisciplinary management of breast cancer. Advances in oncology and increasing use of neoadjuvant chemotherapy (NACT) have opened opportunities for further surgical de-escalation especially for HER2 + and triple negative (TN) disease. The aim of this review is to discuss the recent data on de-escalation of surgery as well as the future directions.Patients with TN and HER2 + breast cancer with excellent response to NACT would be the ideal candidates for surgical de-escalation. Post-NACT image-guided biopsy, potentially combined with machine learning algorithms, may accurately identify patients achieving pathologic complete response that would be eligible for clinical trials assessing safety of omission of breast and axillary surgery.Multidisciplinary research is required to further support results of preliminary studies. Current data point towards a future when even less or no surgery may be required for exceptional responders.

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.

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 In the ACOSOG Z0011 trial, completing axillary lymph node dissection (cALND) did not benefit patients with T1–T2 cN0 early breast cancer and 1–2 positive sentinel lymph nodes (SLN) undergoing breast-conserving surgery (BCT). This paper reports cALND rates in the clinical routine for patients who had higher (T3–T4) tumor stages and/or underwent mastectomy but otherwise met the ACOSOG Z0011 eligibility criteria. Aim of this study is to determine cALND time trends and non-sentinel axillary metastases (NSAM) rates to estimate occult axillary tumor burden. Methods Data were included from patients treated in 179 German breast cancer centers between 2008 and 2015. Time-trend rates were analyzed for cALND of patients with T3–T4 tumors separated for BCT and mastectomy and regarding presence of axillary macrometastases or micrometastases. Results Data were available for 188,909 patients, of whom 19,009 were identified with 1–2 positive SLN. Those 19,009 patients were separated into 4 cohorts: (1) Patients with T1–T2 tumors receiving BCT (ACOSOG Z0011 eligible; n = 13,741), (2) T1–T2 with mastectomy (n = 4093), (3) T3–T4 with BCT (n = 269), (4) T3–T4 with mastectomy (n = 906). Among patients with T3–T4 tumors, cALND rates declined from 2008 to 2015: from 88.2 to 62.6% for patients receiving mastectomy and from 96.6 to 58.1% in patients receiving BCT. Overall rates for any NSAM after cALND for cohorts 1–4 were 33.4%, 42.3%, 46.9%, 58.8%, respectively. Conclusions The cALND rates have decreased substantially in routine care in patients with ‘extended’ ACOSOG Z0011 eligibility criteria. Axillary tumor burden is higher in these patients than in the ACOSOG Z0011 trial.

Purpose Little is known about the reason of high short-term complication rates after the subcutaneous placement of breast implants or expanders after mastectomy without biological matrices or synthetic meshes. This study aims to evaluate complications and their risk factors to develop guidelines for decreasing complication rates. Methods We included all cases of mastectomy followed by subcutaneous implant or expander placement between 06/2017 and 05/2018 (n = 92). Mean follow-up time was 12 months. Results Explantation occurred in 15 cases (16.3%). The surgeon’s preference for moderate vs. radical subcutaneous tissue resection had a significant influence on explantation rates (p = 0.026), impaired wound healing or infection (requiring surgery) (p = 0.029, p = 0.003 respectively) and major complications (p = 0.018). Multivariate analysis revealed significant influence on complication rates for radical subcutaneous tissue resection (p up to 0.003), higher implant volume (p up to 0.023), higher drain volume during the last 24 h (p = 0.049), higher resection weight (p = 0.035) and incision type (p = 0.011). Conclusion Based on the significant risk factors we suggest the following guidelines to decrease complication rates: favoring thicker skin envelopes after surgical preparation, using smaller implants, removing drains based on a low output volume during the last 24 h and no use of periareolar incision with extension medial or lateral. We should consider ADMs for subcutaneous one-stage reconstructions. The individual surgeon’s preference of subcutaneous tissue resection is of highest relevance for short-term complications—this has to be part of internal team discussions and should be considered in future trials for comparable results.

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.

Item does not contain fulltext 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.

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.

Purpose This is the first study to systematically evaluate the diagnostic accuracy of intraoperative specimen radiography on margin level and its potential to reduce second surgeries in patients treated with neoadjuvant chemotherapy. Methods This retrospective study included 174 cases receiving breast conserving surgery (BCS) after neoadjuvant chemotherapy (NACT) of primary breast cancer. Conventional specimen radiography (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 1044 margins were assessed. Of 47 (4.5%) histopathological positive margins, CSR identified 9 correctly (true positive). 38 infiltrated margins were missed (false negative). This resulted in a sensitivity of 19.2%, a specificity of 89.2%, a positive predictive value (PPV) of 7.7%, and a negative predictive value (NPV) of 95.9%. The rate of secondary procedures was reduced from 23 to 16 with a number needed to treat (NNT) of CSR-guided intraoperative re-excisions of 25. In the subgroup of patients with cCR, the prevalence of positive margins was 10/510 (2.0%), PPV was 1.9%, and the NNT was 85. Conclusion Positive margins after NACT are rare and CSR has only a low sensitivity to detect them. Thus, the rate of secondary surgeries cannot be significantly reduced by recommending targeted re-excisions, especially in cases with cCR. In summary, CSR after NACT is inadequate for intraoperative margin assessment but remains useful to document removal of the biopsy site clip.

BackgroundAbout 40 % of women with breast cancer achieve a pathologic complete response in the breast after neoadjuvant systemic treatment (NST). To identify these women, vacuum-assisted biopsy (VAB) was evaluated to facilitate risk-adaptive surgery. In confirmatory trials, the rates of missed residual cancer [false-negative rates (FNRs)] were unacceptably high (> 10%). This analysis aimed to improve the ability of VAB to exclude residual cancer in the breast reliably by identifying key characteristics of false-negative cases.MethodsUni- and multivariable logistic regressions were performed using data of a prospective multicenter trial (n= 398) to identify patient and VAB characteristics associated with false-negative cases (no residual cancer in the VAB but in the surgical specimen). Based on these findings FNR was exploratively re-calculated.ResultsIn the multivariable analysis, a false-negative VAB result was significantly associated with accompanying ductal carcinoma in situ (DCIS) in the initial diagnostic biopsy [odds ratio (OR), 3.94;p p = 0.066), and age (OR, 1.03;p = 0.034). Exclusion of women with DCIS or multicentric disease (n = 114) and classication of VABs that did not remove the clip marker as uncertain representative VABs decreased the FNR to 2.9% (3/104).ConclusionFor patients without accompanying DCIS or multicentric disease, performing a distinct representative VAB (i.e., removing a well-placed clip marker) after NST suggests that VAB might reliably exclude residual cancer in the breast without surgery. This evidence will inform the design of future trials evaluating risk-adaptive surgery for exceptional responders to NST.

PURPOSE Despite their promises, digital innovations have scarcely translated to technologies used in routine clinical practice, making the identification of barriers to successful implementation a research priority. Low levels of transdisciplinary skills represent such a barrier but so far, this has not been evaluated and compared between information technology (IT) and health care specialists. In this study, we evaluated the level of digital health literacy among IT and health care specialists. MATERIALS AND METHODS An anonymous questionnaire was distributed to staff at a breast cancer unit and an IT department of two German universities in December 2020. The survey questionnaire consisted of the previously validated eHealth Literacy Assessment Toolkit and additional questions with respect to age, profession, and career stage. Mann-Whitney or Wilcoxon rank-sum tests and two-sample chi-square tests were used for the analysis. RESULTS The survey was completed by 113 individuals: 70 (61.9%) IT specialists and 43 (38.1%) health care specialists. Health care specialists scored significantly higher on the health-related scales and IT specialists scored significantly higher on the digitally related scales. No single participant identified themselves to have the highest level of literacy on all survey questions (n = 0 of 113; 0%). Only one person (n = 1 of 113; 0.9%) consistently reported a high or the highest level of literacy. CONCLUSION Although IT and health care specialists showed great literacy in their respective disciplines, only few individuals combined both digital and health care literacy. Multidisciplinary teams and transdisciplinary curricula are crucial to bridge skill gaps between disciplines and to drive the implementation of digital health initiatives.

Tailoring of breast cancer treatment to the individual has especially occurred in breast cancer surgery: paradigms have changed from Halsted's radical mastectomy in 1882, to simple mastectomy, to lumpectomy. Within the next decade, we might face another paradigm change of omitting breast cancer surgery at all in case of a complete response after neoadjuvant systemic treatment. In this article, we provide an overview of the reasoning for this new paradigm change, the criticism it has evoked, and under which conditions it might be incorporated into clinical practice. We also take a look at previous paradigm changes in breast cancer surgery and the insights they provide us in the current situation on a statistical but also on a psychological level.

PURPOSE The aim of the current work was to clarify whether a preoperative lymphoscintigraphy (LSG) enhances staging accuracy of sentinel lymph node biopsy (SLNB). PATIENTS AND METHODS In a prospective, multicenter, randomized phase III trial, patients with cN0 early breast cancer or extensive/high-grade ductal carcinoma in situ planned for standard radioactive-labeled colloid LSG with subsequent SLNB were randomly assigned 1:1 to receive SLNB either with knowledge of the LSG findings or without. As the false-negative rate of SLNB correlates with the number of resected sentinel lymph nodes (SLNs), our primary end point was the mean number of histologically detected SLNs per patient. One thousand one hundred two evaluable patients were necessary to demonstrate noninferiority of SLNB without LSG. Stratified one-sided 95% CI for the difference (without LSG − with LSG) in the mean number of histologically detected SLNs had to be greater than −0.27 (10% noninferiority margin). Stratification was performed according to tumor focality and trial site. Additional predefined secondary end points (rates of node-positive patients and of completion axillary lymph node dissection) were analyzed to rule out differences in the reliable detection of nodal metastases. RESULTS Between May 2014 and October 2015, 1,198 patients were randomly assigned in 23 German and Swiss breast centers. Modified intention-to-treat analysis (n = 1,163) showed a mean number of histologically detected SLNs of 2.21 with LSG and 2.26 without LSG (difference 0.05; stratified 95% CI, −0.18 to infinity), thus establishing noninferiority of omitting preoperative LSG. Secondary end points displayed no statistically significant differences. CONCLUSION We show that SLNB is equally effective irrespective of the surgeon’s knowledge of preoperative LSG results. SLNB without LSG will speed up the preoperative workflow and reduce cost.

OBJECTIVES The BI-RADS© classification provides a standardized way to describe ultrasound findings in breast cancer diagnostics. However, there is little information regarding which BI-RADS© descriptors are most strongly associated with malignancy, to better distinguish BI-RADS© 3 (follow-up imaging) and 4 (diagnostic biopsy) breast masses. METHODS Patients were recruited as part of an international, multicenter trial (NCT02638935). The trial enrolled 1294 women (6 excluded) categorized as BI-RADS© 3 or 4 upon routine B-mode ultrasound examination. Ultrasound images were evaluated by three expert physicians according to BI-RADS©. All patients underwent histopathological confirmation (reference standard). We performed univariate and multivariate analyses (chi-square test, logistic regression, and Krippendorff's alpha). RESULTS Histopathologic evaluation showed malignancy in 368 of 1288 masses (28.6%). Upon performing multivariate analysis, the following descriptors were significantly associated with malignancy (P

International audience; Primary systemic therapy is increasingly used in the treatment of patients with early-stage breast cancer, but few guidelines specifically address optimal locoregional therapies. Therefore, we established an international consortium to discuss clinical evidence and to provide expert advice on technical management of patients with early-stage breast cancer. The steering committee prepared six working packages to address all major clinical questions from diagnosis to surgery. During a consensus meeting that included members from European scientific oncology societies, clinical trial groups, and patient advocates, statements were discussed and voted on. A consensus was reached in 42% of statements, a majority in 38%, and no decision in 21%. Based on these findings, the panel developed clinical guidance recommendations and a toolbox to overcome many clinical and technical requirements associated with the diagnosis, response assessment, surgical planning, and surgery of patients with early-stage breast cancer. This guidance could convince clinicians and patients of the major clinical advancements purported by primary systemic therapy, the use of less extensive and more targeted surgery to improve the lives of patients with breast cancer.

Objective This study aimed to investigate the feasibility and accuracy of non-radioactive target lymph node (TLN) biopsy and targeted axillary dissection (TAD) in routine clinical practice. Background data TAD involves TLN biopsy (TLNB) and sentinel lymph node biopsy (SLNB) and was recently introduced as a new standard for less invasive axillary staging in breast cancer (BC) patients undergoing neoadjuvant systemic therapy (NST); however, clinical evidence is limited. Methods The SenTa study is a prospective registry study conducted at 50 centers. Patients with invasive BC who underwent clip insertion into the most suspicious axillary lymph node (LN) were eligible. Axillary surgery was performed with or without SLNB, TLNB, and/or axillary lymph node dissection (ALND). Main endpoints were the detection rate (DR) and false-negative rate (FNR) of TLNB and TAD after NST. Results Between 2017 and 2018, 548 consecutive BC patients underwent clip placement into biopsy-confirmed positive LNs. After NST (n = 473), the clipped TLN was intraoperatively resected in 329 of 423 patients (77.8%, 95% confidence interval [CI]: 74.0 to 82.0). TAD was successful in 199 of 229 patients (DR: 86.9%, 95% CI: 81.8 to 91.0), the SLN and TLN were identical in 129 patient (64.8%). FNRs were 7.2% (8 of 111, 95% CI: 3.1 to 13.6) for TLNB followed by ALND (n = 203) and 4.3% (2 of 46, 95% CI: 0.5 to 14.8) for TAD followed by ALND (n = 77). Conclusions The SenTa study demonstrates the feasibility of TAD in a real-world cohort of BC patients. Our findings are of great importance for de-escalation of surgical strategies.

The aims of the Oncoplastic Breast Consortium initiative were to identify important knowledge gaps in the field of oncoplastic breast-conserving surgery and nipple-sparing or skin-sparing mastectomy with immediate breast reconstruction, and to recommend appropriate research strategies to address these gaps. A total of 212 surgeons and 26 patient advocates from 55 countries prioritised the 15 most important knowledge gaps from a list of 38 in two electronic Delphi rounds. An interdisciplinary panel of the Oncoplastic Breast Consortium consisting of 63 stakeholders from 20 countries obtained consensus during an in-person meeting to select seven of these 15 knowledge gaps as research priorities. Three key recommendations emerged from the meeting. First, the effect of oncoplastic breast-conserving surgery on quality of life and the optimal type and timing of reconstruction after nipple-sparing or skin-sparing mastectomy with planned radiotherapy should be addressed by prospective cohort studies at an international level. Second, the role of adjunctive mesh and the positioning of implants during implant-based breast reconstruction should ideally be investigated by randomised controlled trials of pragmatic design. Finally, the BREAST-Q questionnaire is a suitable tool to assess primary outcomes in these studies, but other metrics to measure patient-reported outcomes should be systematically evaluated and quality indicators of surgical morbidity should be further assessed.

OBJECTIVE We evaluated the ability of minimally invasive, image-guided vacuum-assisted biopsy (VAB) to reliably diagnose a pathologic complete response in the breast (pCR-B). SUMMARY BACKGROUND DATA Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in up to 80% of women with breast cancer. In such cases, breast surgery, the gold standard for confirming pCR-B, may be considered overtreatment. METHODS This multicenter, prospective trial enrolled 452 women presenting with initial stage 1-3 breast cancer of all biological subtypes. Fifty-four women dropped out; 398 were included in the full analysis. All participants had an imaging-confirmed partial or complete response to NST and underwent study-specific image-guided VAB before guideline-adherent breast surgery. The primary endpoint was the false-negative rate (FNR) of VAB-confirmed pCR-B. RESULTS Image-guided VAB alone did not detect surgically confirmed residual tumor in 37 of 208 women [FNR, 17.8%; 95% confidence interval (CI), 12.8-23.7%]. Of these 37 women, 12 (32.4%) had residual DCIS only, 20 (54.1%) had minimal residual tumor (

Objective The FUSION-X-US-II prototype was developed to combine 3D automated breast ultrasound (ABUS) and digital breast tomosynthesis in a single device. We evaluated the performance of ABUS and tomosynthesis in a single examination in a clinical setting. Methods In this prospective feasibility study, digital breast tomosynthesis and ABUS were performed using the FUSION-X-US-II prototype without any change of the breast position in patients referred for clarification of breast lesions with an indication for tomosynthesis. The tomosynthesis and ABUS images of the prototype were interpreted independently from the clinical standard by a breast diagnostics specialist. Any detected lesion was classified using BI-RADS® scores, and results of the standard clinical routine workup (gold standard) were compared to the result of the separate evaluation of the prototype images. Image quality was rated subjectively and coverage of the breast was measured. Results One hundred one patients received both ABUS and tomosynthesis using the prototype. The duration of the additional ABUS acquisition was 40 to 60 s. Breast coverage by ABUS was approximately 80.0%. ABUS image quality was rated as diagnostically useful in 86 of 101 cases (85.1%). Thirty-three of 34 malignant breast lesions (97.1%) were identified using the prototype. Conclusion The FUSION-X-US-II prototype allows a fast ABUS scan in combination with digital breast tomosynthesis in a single device integrated in the clinical workflow. Malignant breast lesions can be localized accurately with direct correlation of ABUS and tomosynthesis images. The FUSION system shows the potential to improve breast cancer screening in the future after further technical improvements. Key Points • The FUSION-X-US-II prototype allows the combination of automated breast ultrasound and digital breast tomosynthesis in a single device without decompression of the breast. • Image quality and coverage of ABUS are sufficient to accurately detect malignant breast lesions. • If tomosynthesis and ABUS should become part of breast cancer screening, the combination of both techniques in one device could offer practical and logistic advantages. To evaluate a potential benefit of a combination of ABUS and tomosynthesis in screening-like settings, further studies are needed.

Neoadjuvant chemotherapy (NACT) in early breast cancer (EBC) enables in vivo sensitivity testing and less radical surgery as compared to primary surgery and adjuvant chemotherapy (ACT). The aim of our study is to illustrate trends of systemic treatment of EBC. The study analyzed chemotherapy usage and time trends for patients with EBC treated at 104 German breast units between January 2008 and December 2017. The data were obtained through a quality-controlled benchmarking process. Altogether, 124 084 patients were included, of whom 46 279 (37.3%) received chemotherapy. For 44 765 of these cases, detailed information on systemic treatment and surgery were available. Overall use of chemotherapy declined from 42.0% in 2008 to 32.0% in 2017. During that same time, the proportion of NACT increased from 20.0% to 57.7%, irrespective of tumor subtype. The pathological complete response (pCR) rate (defined as ypT0 ypN0) at surgery after NACT increased from 15.0% to 34.2%. The results from this large cohort from the clinical routine reflect the refined indications for chemotherapy in EBC.

A favorable aesthetic outcome is meant to be the second most important aim after oncological safety in breast conservation surgery. In times of systematic breast cancer screening, advanced diagnostic techniques, and the widespread use of neoadjuvant systemic therapy with the result of reduced average tumor sizes, breast conservation is nowadays the standard of care in breast cancer surgery and fits to the preferences of most breast cancer patients. There are several described factors influencing aesthetic outcomes which may in part be addressed by the breast surgeon before planning a surgical procedure in order to maintain an optimal aesthetic result. Unfortunately, there is nearly no evidence of how to systematically improve aesthetic outcomes in these cases with assumed negatively influencing factors. This chapter will present and discuss the described factors influencing aesthetic outcomes on the one hand and will hypothesize and present existing evidence on how to optimize outcomes on the other hand.

IntroductionOutcome reporting in research studies of breast reconstruction is inconsistent and lacks standardisation. The results of individual studies therefore cannot be meaningfully compared or combined limiting their value. A core outcome set (COS) has been developed to address these issues and identified 11 key outcomes to be measured and reported in all future research and audit studies in reconstructive breast surgery (RBS). A COS represents what key outcomes should be measured. The next step is to determine how and when this should be done. The aim of this study is to develop a core measurement set (CMS) for use in research and audit studies in implant-based breast reconstruction.Methods and analysisThe CMS will be developed in accordance with the guidance developed by the Core Outcome Measures in Effectiveness Trials initiative (COMET) and COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) group for the selection of outcome measurement instruments (OMIs) for relevant outcome domains included in the RBS COS. This will involve three phases with strategies to promote implementation as a final additional phase. The phases are (1) conceptual considerations in which the target population, procedures and settings are defined; (2) systematic reviews to identify existing clinical, patient-reported and cosmetic OMIs and, if appropriate, assess their quality using COSMIN methodology; (3) a modified Delphi process including sequential Delphi surveys involving approximately 100 healthcare professionals and a face to face consensus meeting to agree and ratify which outcome definitions and OMIs should be used and standardised time points for assessment; (4) strategies to promote dissemination and adoption of the CMS.Ethics and disseminationEthical approval has been granted by University of Bristol Faculty Research Ethics Committee FREC ID 60221. Dissemination strategies will include scientific meeting presentations and peer-reviewed journal publications. Implementation activities will include engagement with journal editors and funders to promote uptake and use of the CMS.

Background: Neoadjuvant systemic treatment (NST) elicits a pathologic complete response (pCR, ypT0, ypN0) in 40-70% of women with HER2 positive, triple-negative, and high-proliferative Luminal B breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, their safe identification prior to surgery is a major challenge: imaging after NST, minimally-invasive biopsies, or combinations of both using narrow patient selection criteria are not accurate enough either because they showed high rates of missed cancer or high rates of missed pCR. Recently, the concept of an intelligent, minimally-invasive, vacuum-assisted biopsy (intelligent VAB) was introduced to identify exceptional responders to NST. The intelligent VAB is a multivariate risk algorithm that uses artificial intelligence techniques to analyze conventional VAB results alongside contextualizing patient, imaging, and tumor information. It showed great potential to reliably identify patients with a pCR in the breast (ypT0). However, the absent integration of the axillary status impairs clinical applicability. In this study, we evaluated the feasibility of an intelligent VAB to identify exceptional responders to NST in the breast and axilla. Methods: We trained, tested, and validated a machine learning algorithm (Extreme Gradient Boosting Tree) using patient, imaging, tumor, and conventional VAB variables to detect residual cancer after NST (ypT+/is or ypN+) prior to surgery. We used data from 318 women with cT1-3, cN0/+, HER2 positive, triple-negative breast or high-proliferative Luminal B breast cancer who underwent VAB before surgery (NCT02948764). We used 10-fold cross-validation to train and test the algorithm which was externally validated using data of an independent, similar trial (NCT02575612). Findings were compared to the histopathologic evaluation of the surgical specimen. False-negative rate (FNR), specificity, and area under the ROC curve (AUROC) were the main outcome measures. Results: In the development set (n=318), mean patient age was 52.5 years and 45.3% (144 of 318) achieved a pCR (ypT0 and ypN0). Using resampling methods, the intelligent VAB showed an FNR of 5.2% (9 of 174, 95% CI 2.4-9.5), a specificity of 37.5% (54 of 144, 95% CI 29.6-45.9), and an AUROC of 0.92 (95% CI 0.90-0.94) in the development set to detect residual cancer (ypT+/is or ypN+) after NST. In the external validation set (n=45), mean patient age was 48.1 years and 44.4% (20 of 45) achieved a pCR. The intelligent VAB showed an FNR of 0% (0 of 25, 95% CI 0.0-13.7), a specificity of 40.0% (8 of 20, 95% CI 19.1-63.9) and an AUROC of 0.91 (95% CI 0.82-0.97). Spiegelhalter’s Z confirmed a well-calibrated model (z score -0.746, P 0.228). FNR of the intelligent VAB was lower compared to imaging after NST, conventional VAB, or combinations of both using narrow patient selection criteria. Conclusion: An intelligent VAB can reliably exclude residual cancer after NST for women with cT1-3, cN0/+, HER2 positive, triple-negative breast or high-proliferative Luminal B breast cancer. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials. Trial registration: NCT02948764 and NCT02575612. Funding: German Research Foundation (DFG) Diagnostic Performance ComparisonFalse-negative rate - % (95% CI); no.Specificity - % (95% CI); no.Negative predictive value - % (95% CI); no.Positive predictive value - % (95% CI); no.AUROC - value (95% CI)Development set (n=318)Imaging after NST24.4% (18.0-13.7); 40 of 16452.2% (43.4-61.0); 69 of 13263.3% (53.5-72.3); 69 of 10966.3% (59.1-73.0); 124 of 187-Conventional VAB32.8% (25.8-40.3); 57 of 174100% (97.5-100); 144 of 14471.6% (64.9-77.8); 144 of 201100% (96.9-100); 117 of 117-Imaging after NST + VAB16.7% (11.4-23.2); 28 of 16832.1% (24.4-40.6); 44 of 13761.1% (48.9-72.4); 44 of 7260.1% (56.1-69.1); 140 of 223-VAB + patient selection9.1% (5.0-14.1) 15 of 17036.3% (28.2-45.0); 49 of 13576.6% (64.3-86.2); 49 of 6464.3% (57.9-70.4); 155 of 241-Intelligent VAB (Extreme Gradient Boosting tree)5.2% (2.4-9.6); 9 of 17437.5% (29.6-45.9); 54 of 14485.7% (74.6-93.3); 54 of 6364.7% (58.5-70.6); 165 of 2550.92 (0.90-0.94)External validation (n=45)Imaging after NST24.0% (9.4-45.1%);6 of 2565.0% (40.8-84.6%);13 of 2068.4% (43.4-87.4%);13 of 1973.1% (52.2-88.4%);19 of 26-Conventional VAB28.0% (12.1-49.4%);7 of 25100% (83.2-100%);20 of 2074.1% (53.7-88.9%);20 of 27100% (81.5-100%);18 of 18-Imaging after NST + VAB12.0% (2.5-31.2); 3 of 2565.0% (40.8-84.6%);13 of 2081.3% (54.4-96.0%); 13 of 1675.9% (56.5-89.7%); 22 of 29-VAB + patient selection4.0% (1.0-2.4); 1 of 2530.0% (9.4-45.1%); 6 of 2085.7% (69.8-99.8); 6 of 763.2% (46.0-78.2); 24 of 38-Intelligent VAB (Extreme Gradient Boosting tree)0.0% (0.0-13.7%);0 of 2540.0% (19.1-63.9%);8 of 20100% (63.1-100%);8 of 867.8% (50.2-82.0%);25 of 370.91 (0.82 - 0.97)AUROC = Area under the receiver operating characteristic curve; CI = confidence interval Citation Format: André Pfob, Chris Sidey-Gibbons, Geraldine Rauch, Bettina Thomas, Benedikt Schaefgen, Sherko Kuemmel, Toralf Reimer, Markus Hahn, Marc Thill, Jens-Uwe Blohmer, John Hackmann, Wolfram Malter, Inga Bekes, Kay Friedrichs, Sebastian Wojcinski, Sylvie Joos, Stefan Paepke, Tom Degenhardt, Joachim Rom, Achim Rody, Regina Große, Marion van Mackelenbergh, Mattea Reinisch, Maria Karsten, Michael Golatta, Joerg Heil. Intelligent vacuum-assisted breast biopsy to identify breast cancer patients with pathologic complete response after neoadjuvant systemic treatment for omission of breast and axillary surgery [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 PD7-02.

Breast cancer is the leading cause of death from cancer in women and the most common cancer in the world [1]. To date, many patients with estrogen-receptor-positive (ER+) breast cancer are overtreated with chemotherapy when the rationale for adjuvant chemotherapy is based on clinicopathologic parameters. Different studies were able to demonstrate that a 21-gene expression assay (Oncotype DX® Genomic Health, Redwood City, CA) can predict the benefit from adjuvant chemotherapy in ER+ breast cancers [2, 3] and provide additional prognostic information independent of clinicopathological features [4]. Data from all patients with ER+ Her2neu− breast cancer undergoing Oncotype DX® testing between 2011 and 2014 at a tertiary referral center in Germany were analyzed. Oncotype DX® was performed in 69 cases, in 2 cases data were missing and in 3 cases Oncotype DX® could not be performed by the company. The results showed a low risk in 39 cases, an intermediate risk in 22 cases and a high risk in 3 cases. Based on Oncotype results, treatment recommendations were changed in 39 of 64 patients (61%). Before Oncotype DX® testing, chemotherapy was recommended in 67 patients, afterwards only in 25 patients. Data from 44 of 67 patients were matched to controls for stage, tumor grade, menopausal and hormone receptor status. Within a mean observation time of 19.7 months, cancer recurrence was observed in two patients. Oncotype DX® testing can be recommended for risk-tailored chemotherapy. Results should be validated in larger prospective studies.

// Anna-Lena Krause 1 , Florian Schuetz 2 , Marc Boudewijns 2 , Maria Pritsch 1 , Markus Wallwiener 2 , Michael Golatta 2 , Joachim Rom 2 , Joerg Heil 2 , Christof Sohn 2 , Andreas Schneeweiss 2 , Philipp Beckhove 1, 3, * and Christoph Domschke 2, * 1 Translational Immunology Division, German Cancer Research Center (DKFZ), Heidelberg, Germany 2 Department of Gynecology and Obstetrics, Heidelberg University Hospital, National Center for Tumor Diseases (NCT), Heidelberg, Germany 3 Regensburg Center for Interventional Immunology (RCI) and University Medical Center of Regensburg, Regensburg, Germany * Equal contributions Correspondence to: Philipp Beckhove, email: beckhove@rcii.de Christoph Domschke, email: christoph.domschke@med.uni-heidelberg.de Keywords: pregnancy; breast cancer; anti-tumor T cells; regulatory T cells; tumor-associated antigens Received: July 08, 2017 Accepted: August 04, 2017 Published: September 08, 2017 ABSTRACT Compared to nulliparous women, parous women have an up to 50% lower lifetime risk of developing breast cancer. An endogenous mechanism to prevent the development of cancer is the destruction of tumor cells by T cells that recognize tumor-associated antigens (TAA). Since a number of TAA are also highly present in the breast and placenta of pregnant women, we investigated the induction and characteristics of spontaneous T cell responses against TAA during pregnancy. To this end, we collected peripheral blood from healthy nulliparous, primigravid and parous women, as well as from breast cancer patients. IFN-γ ELISpot assays were performed to measure the intensity and specificity of T cell responses against 11 different TAA. The impact of TAA-specific Treg cells on anti-TAA responses was assessed by performing the assay before and after depletion of CD4 + CD25 + T cells. The antigenic specificities of these Treg cells were analyzed by the Treg specificity assay. Furthermore, we conducted flow cytometric analyses to determine the memory phenotype and cytokine secretion profile of TAA-specific T cells. Our results demonstrate that pregnancy induces functional and long-lived memory and effector T cells that react against multiple TAA. These persist for many decades in parous females, but are not found in age-matched females without children. We also detected TAA-specific Treg cells, which suppressed strong effector T cell responses after delivery. Nulliparous breast cancer patients displayed median TAA-specific effector T cell responses to be decreased threefold compared to parous patients, which could be restored in vitro after depletion of Treg cells.

With current advances in neoadjuvant systemic therapy (NST) and improved breast imaging, the potential of nonoperative therapy for invasive breast cancer has emerged as a viable option when utilizing meticulous image-guided percutaneous biopsy to document pathologic complete response. Feasibility clinical trials utilizing this approach are being performed by teams of investigators from single and multicenter/cooperative groups around the world. Imaging alone after NST lacks sufficient sensitivity and specificity in predicting pCR and therefore cannot be utilized for clinical selection of patients for omission of surgery. Imaging with adequate sampling after NST of the residual lesions (or around the remaining clip if a complete radiologic response occurs) appears to be essential in selecting patients with pCR to lower the false-negative rates based on initial reported feasibility studies to identify pCR without surgery that range from 5 to 49%. In this manuscript, recently completed, ongoing, and planned clinical feasibility trials and a new omission of surgery trial are described. Drastic rethinking of all diagnostic and therapeutic management strategies that are ordinarily utilized for patients who receive standard breast cancer surgery is required. A roadmap of essential questions and issues that will have to be resolved as the field of nonoperative breast cancer management advances is described in detail.