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Background Women undergoing cancer-related mastectomy and reconstruction are facing multiple treatment choices where post-surgical satisfaction with breasts is a key outcome. We developed and validated machine learning algorithms to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer. Methods We trained, tested, and validated three machine learning algorithms (logistic regression (LR) with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) to predict clinically important differences in satisfaction with breasts at 2-year follow-up using the validated BREAST-Q. We used data from 1553 women undergoing cancer-related mastectomy and reconstruction who were followed-up for two years at eleven study sites in North America from 2011 to 2016. 10-fold cross-validation was used to train and test the algorithms on data from 10 of the 11 sites which were further validated using the additional site's data. Area-under-the-receiver-operating-characteristics-curve (AUC) was the primary outcome measure. Results Of 1553 women, 702 (45.2%) experienced an improved satisfaction with breasts and 422 (27.2%) a decreased satisfaction. In the validation set (n = 221), the algorithms showed equally high performance to predict improved or decreased satisfaction with breasts (all P > 0.05): For improved satisfaction AUCs were 0.86–0.87 and for decreased satisfaction AUCs were 0.84–0.85. Conclusion Long-term, individual patient-reported outcomes for women undergoing mastectomy and breast reconstruction can be accurately predicted using machine learning algorithms. Our algorithms may be used to better inform clinical treatment decisions for these patients by providing accurate estimates of expected quality of life., Highlights • Optimal treatment for women undergoing mastectomy and reconstruction is unclear. • Can machine learning algorithms predict long-term patient-reported outcomes? • An international cohort of 1553 women was used for the algorithm development. • External validation showed good performance with AUCs of 0.84–0.87 • accurate estimates of expected quality of life may help inform treatment decisions.

PURPOSE Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but identification of need is difficult. We sought to develop and test a tool to accurately predict an individual's risk of financial toxicity based on clinical, demographic, and patient-reported data prior to initiation of breast cancer treatment. PATIENTS AND METHODS We surveyed 611 patients undergoing breast cancer therapy at MD Anderson Cancer Center. We collected data using the validated COmprehensive Score for financial Toxicity (COST) patient-reported outcome measure alongside other financial indicators (credit score, income, and insurance status). We also collected clinical and perioperative data. We trained and tested an ensemble of machine learning (ML) algorithms (neural network, regularized linear model, support vector machines, and a classification tree) to predict financial toxicity. Data were randomly partitioned into training and test samples (2:1 ratio). Predictive performance was assessed using area-under-the-receiver-operating-characteristics-curve (AUROC), accuracy, sensitivity, and specificity. RESULTS In our test sample (N = 203), 48 of 203 women (23.6%) reported significant financial burden. The algorithm ensemble performed well to predict financial burden with an AUROC of 0.85, accuracy of 0.82, sensitivity of 0.85, and specificity of 0.81. Key clinical predictors of financial burden from the linear model were neoadjuvant therapy (βregularized, .11) and autologous, rather than implant-based, reconstruction (βregularized, .06). Notably, radiation and clinical tumor stage had no effect on financial burden. CONCLUSION ML models accurately predicted financial toxicity related to breast cancer treatment. These predictions may inform decision making and care planning to avoid financial distress during cancer treatment or enable targeted financial support. Further research is warranted to validate this tool and assess applicability for other types of cancer.

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 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.

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.

Objective We developed, tested, and validated machine learning algorithms to predict individual patient-reported outcomes at 1-year follow-up to facilitate individualized, patient-centered decision-making for women with breast cancer. Summary background data Satisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction. Current decision-making relies on group-level evidence which may lead to sub-optimal treatment recommendations for individuals. Methods We trained, tested, and validated three machine learning algorithms using data from 1921 women undergoing cancer-related mastectomy and reconstruction conducted at eleven study sites in North America from 2011 to 2016. Data from 1921 women undergoing cancer-related mastectomy and reconstruction were collected prior to surgery and at 1-year follow-up. Data from 10 of the 11 sites was randomly split into training and test samples (2:1 ratio) to develop and test three algorithms (logistic regression with elastic net penalty, Extreme Gradient Boosting tree, and neural network) which were further validated using the additional site's data.Accuracy and area-under-the-receiver-operating-characteristics-curve (AUC) to predict clinically-significant changes in satisfaction with breasts at 1-year follow-up using the validated BREAST-Q were the outcome measures. Results The three algorithms performed equally well when predicting both improved or decreased satisfaction with breasts in both testing and validation datasets: For the testing dataset median accuracy= 0.81 (range 0.73-0.83), median AUC= 0.84 (range 0.78-0.85). For the validation dataset median accuracy= 0.83 (range 0.81-0.84), median AUC= 0.86 (range 0.83-0.89). Conclusion Individual patient-reported outcomes can be accurately predicted using machine learning algorithms, which may facilitate individualized, patient-centered decision-making for women undergoing breast cancer treatment.

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

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.

In patients with operable early breast cancer, neoadjuvant systemic treatment (NST) is a standard approach. Indications have expanded from downstaging of locally advanced breast cancer to facilitate breast conservation, to in vivo drug-sensitivity testing. The pattern of response to NST is used to tailor systemic and locoregional treatment, that is, to escalate treatment in nonresponders and de-escalate treatment in responders. Here we discuss four questions that guide our current thinking about 'response-adjusted' surgery of the breast after NST. (i) What critical diagnostic outcome measures should be used when analyzing diagnostic tools to identify patients with pathologic complete response (pCR) after NST? (ii) How can we assess response with the least morbidity and best accuracy possible? (iii) What oncological consequences may ensue if we rely on a nonsurgical-generated diagnosis of, for example, minimally invasive biopsy proven pCR, knowing that we may miss minimal residual disease in some cases? (iv) How should we design clinical trials on de-escalation of surgical treatment after NST?

Background There is a well‐known correlation between obesity, sedentary lifestyle, and breast cancer incidence and outcome. The Arbeitsgemeinschaft Medikamentöse Tumortherapie (AGMT) exercise study was a multicenter, randomized clinical trial and assessed the feasibility and efficacy of physical training in 50 breast cancer patients undergoing aromatase inhibitor treatment. Methods Postmenopausal, estrogen receptor‐positive breast cancer patients under aromatase inhibitor treatment were randomized 1:1 to counseling and unsupervised training for 48 weeks (unsupervised arm) or counseling and a sequential training (supervised arm) with a supervised phase (24 weeks) followed by unsupervised physical training (further 24 weeks). Primary endpoint was the individual maximum power output on a cycle ergometer after 24 weeks of exercise. A key secondary endpoint was the feasibility of achieving 12 METh/week (metabolic equivalent of task hours per week). Results Twenty‐three patients (92%) in the unsupervised arm and 19 patients (76%) in the supervised arm with early‐stage breast cancer completed the study. After 24 weeks, the supervised arm achieved a significantly higher maximum output in watt (mean 132 ± standard deviation [SD] 34; 95% confidence interval [CI] 117‐147) compared to baseline (107 ± 25; 95%CI 97‐117; P = 0.012) with a numerically higher output than the unsupervised arm (week 24 115 ± 25; 95%CI 105‐125; P = 0.059). Significantly higher METh/week was reported in the supervised arm compared to the unsupervised arm during the whole study period (week 1‐24 unsupervised: 18.3 (7.6‐58.3); supervised: 28.5 (6.7‐40.1); P = 0.043; week 25‐48; P = 0.041)). Conclusion This trial indicates that patients in an exercise program achieve higher fitness levels during supervised than unsupervised training.

Patients with carcinoma in situ (CIS) of the bladder refractory to bacillus Calmette-Guerin (BCG) treatment are usually treated with cystectomy. Therefore, new treatment options with preservation of the urinary bladder are needed. The objective of the study was to investigate the feasibility, safety and efficacy of a novel targeted alpha-emitter immunotherapy for CIS after BCG treatment failure. A pilot study was conducted in 12 patients (age range 64–86 years, ten men, two women) with biopsy-proven CIS of the bladder refractory to BCG treatment. The patients were treated intravesically with a single instillation (one patient was treated twice) of the alpha-emitter 213Bi coupled to an anti-EGFR antibody (366–821 MBq). The primary aims of the study were to determine the feasibility of treatment with the 213Bi-immunoconjugate and evaluation of adverse effects. Therapeutic efficacy was monitored by histological mapping of the urinary bladder 8 weeks after treatment and at different time points thereafter. The study proved that intravesical instillation of the 213Bi-immunoconjugate targeting EGFR is feasible. No adverse effects were observed and all blood and urine parameters determined remained in their normal ranges. Therapeutic efficacy was considered satisfactory, in that three of the 12 patients showed no signs of CIS 44, 30 and 3 months after treatment. Intravesical instillation of 213Bi-anti-EGFR monoclonal antibody was well tolerated and showed therapeutic efficacy. Repeated instillation and/or instillation of higher activities of the 213Bi-immunoconjugate might lead to better therapeutic outcomes. A phase I clinical trial is planned.

Septic shock is a common medical condition with a mortality approaching 50% where early diagnosis and treatment are of particular importance for patient survival. Novel biomarkers that serve as prompt indicators of sepsis are urgently needed. High‐throughput technologies assessing circulating microRNAs represent an important tool for biomarker identification, but the blood‐compartment specificity of these miRNAs has not yet been investigated. We characterized miRNA profiles from serum exosomes, total serum and blood cells (leukocytes, erythrocytes, platelets) of sepsis patients by next‐generation sequencing and RT‐qPCR (n = 3 × 22) and established differences in miRNA expression between blood compartments. In silico analysis was used to identify compartment‐specific signalling functions of differentially regulated miRNAs in sepsis‐relevant pathways. In septic shock, a total of 77 and 103 miRNAs were down‐ and up‐regulated, respectively. A majority of these regulated miRNAs (14 in serum, 32 in exosomes and 73 in blood cells) had not been previously associated with sepsis. We found a distinctly compartment‐specific regulation of miRNAs between sepsis patients and healthy volunteers. Blood cellular miR‐199b‐5p was identified as a potential early indicator for sepsis and septic shock. miR‐125b‐5p and miR‐26b‐5p were uniquely regulated in exosomes and serum, respectively, while one miRNA (miR‐27b‐3p) was present in all three compartments. The expression of sepsis‐associated miRNAs is compartment‐specific. Exosome‐derived miRNAs contribute significant information regarding sepsis diagnosis and survival prediction and could serve as newly identified targets for the development of novel sepsis biomarkers.

Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy is increasingly used in metastatic castration-resistant prostate cancer. We aimed to estimate the absorbed doses for normal organs and tumor lesions using 177Lu-PSMA IT 0.12 ± 0.06 Gy/GBq for the liver; and 0.55 ± 0.14 Gy/GBq for the parotid, 0.64 ± 0.40 Gy/GBq for the submandibular, and 3.8 ± 1.4 Gy/GBq for the lacrimal glands. Absorbed organ doses were relatively constant among the 4 different cycles. Tumor lesions received a mean absorbed dose per cycle of 3.2 ± 2.6 Gy/GBq (range, 0.22-12 Gy/GBq). Doses to tumor lesions gradually decreased, with 3.5 ± 2.9 Gy/GBq for the first, 3.3 ± 2.5 Gy/GBq for the second, 2.7 ± 2.3 Gy/GBq for the third, and 2.4 ± 2.2 Gy/GBq for the fourth cycle. SUVs of pretherapeutic PET moderately correlated with absorbed dose (r = 0.44, P < 0.001 for SUVmax; r = 0.43, P < 0.001 for SUVmean) and moderately correlated with the change of SUV (r = 0.478, P < 0.001 for SUVmax, and r = 0.50, P < 0.001 for SUVmean). Conclusion: Organ- and tumor-absorbed doses for 177Lu-PSMA I&T are comparable to recent reports and complement these with information on an excellent correlation between the 4 therapy cycles. With the kidneys representing the critical organ, a cumulative activity of 40 GBq of 177Lu-PSMA I&T appears to be safe and justifiable. The correlation between pretherapeutic SUV and absorbed tumor dose emphasizes the need for PSMA-ligand PET imaging for patient selection.

2047 Background: Financial burden caused by cancer treatment is associated with material loss, distress, and poorer outcomes. Financial resources exist to support patients but objective identification of individuals in need is difficult. Accurate predictions of an individual’s risk of financial toxicity prior to initiation of breast cancer treatment may facilitate informed clinical decision making, reduce financial burden, and improve patient outcomes. Methods: We retrospectively surveyed 611 patients who had undergone breast cancer therapy at MD Anderson Cancer Center to assess the financial impact of their care. All patients were over 18 and received either a lumpectomy or a mastectomy. We collected data using the FACT-COST patient-reported outcome measures alongside other financial indicators including income and insurance status. We extracted clinical and perioperative data from the electronic health record. Missing data were imputed using multiple imputation. We used this data to train and validate a neural network, LASSO-regularized linear model, and support vector machines. Data were randomly partitioned into training and validation samples (3:1 ratio). Analyses were informed by international PROBAST recommendations for developing multivariate predictors. We combined algorithms into a voting ensemble and assessed predictive performance using area under the receiver operating characteristics curve (AUROC), accuracy, sensitivity, and specificity. Results: In our validation sample, 48 of 203 (23.6%) women reported FACT-COST scores commensurate with significant financial burden. The algorithm predicted significant financial burden relating to cancer treatment with high accuracy (Accuracy = .83, AUROC = .82, sensitivity = .81, specificity = .82). Key clinical predictors of financial burden from linear models were neo-adjuvant therapy (βregularized 0.12) and autologous, rather than implant-based, reconstruction (βregularized 0.10). Conclusions: Machine learning models were able to accurately predict the occurrence of financial toxicity related to breast cancer treatment. These predictions may be used to inform decision making and care planning to avoid financial distress during cancer treatment or to enable targeted financial support for individuals. Further research is warranted to further improve this tool and assess applicability for other types of cancer.

Background and objectives: Pathological complete response after neoadjuvant chemotherapy for women with primary breast cancer is achieved in up to 60%. Identification of these women by imaging is imprecise – even clear residual tumors on breast imaging after neoadjuvant chemotherapy frequently show pathologic complete response. Thus, pathologic evaluation of the surgical specimen is the gold standard. However, having women to undergo breast-conserving or even ablative breast surgery to diagnose pathologic complete response could be considered overtreatment. To potentially avoid these surgeries, we first evaluated the accuracy of an image-guided vacuum-assisted biopsy (i.g.VAB) alone to diagnose and rule out residual disease in the breast. Second, we assessed the accuracy of the combination of i.g.VAB with breast imaging results after neoadjuvant chemotherapy. Methods: This multicenter, prospective, diagnostic trial took place at 21 trial sites in Germany (March 2017 to May 2019). 398 women consented, presented with initial clinical tumor stage 1-3 breast cancer of all biological subtypes with partial or complete response by imaging after neoadjuvant chemotherapy and underwent i.g.VAB and guideline-adherent breast surgery. First, we compared i.g.VAB alone - guided either by ultrasound or mammography – with the gold standard evaluation of the surgical specimen to detect pathological residual disease. A pathologically confirmed residual tumor in the surgical specimen but no residual tumor in the i.g.VAB defines a false-negative finding. The study was powered to detect a false-negative rate of the i.g.VAB below 10% (primary efficacy analysis). Second, we combined the results of the i.g.VAB and the breast imaging exams (ultrasound and mammography) after neoadjuvant chemotherapy. A positive test (=residual tumor) was either a residual tumor in the i.g.VAB OR tumor seen on the imaging exam. Thus, a false-negative finding is a pathologically confirmed residual tumor in the surgical specimen but no residual tumor in i.g.VAB AND no residual tumor on the breast imaging exam (secondary explorative analysis). The study was stopped for futility after planned interim analysis. Results: For the whole cohort (n=398) and primary efficacy analysis, i.g.VAB alone failed to detect a residual tumor in 37 of 208 cases with a residual tumor in the surgical specimen (false-negative rate (FNR) = 17.8% (95% CI: 12.8-23.7%)). Only the subgroup in which i.g.VAB used 7 G needles showed a FNR < 10%. The secondary explorative analysis detected residual tumor with a FNR of 6.2% (3.4-10.5%) – 13 out of the 208 cases with a residual tumor in the surgical specimen were false-negative. While this approach increased sensitivity, it decreased specificity but still identified 95 of 190 (50.0%) pathologic tumor-free women. Conclusion The i.g.VAB alone did not reach the anticipated diagnostic accuracy in this study. However, combining the results of i.g.VAB and breast imaging after neoadjuvant chemotherapy detects residual disease in women with breast cancer with a FNR This finding will guide ongoing and future trials aiming for tailoring, de-escalating, and potentially avoiding unnecessary surgery in women without residual disease after neoadjuvant chemotherapy. Trial registration: NCT02948764 Protocol publication: DOI: 10.1186/s12885-018-4760-4 Table 1Primary efficacy analysisSecondary, explorative analysisresidual disease in i.g.VAB specimenno residual disease in i.g. VAB specimenresidual disease in i.g.VAB specimen OR imaging after NACTno residual disease in i.g.VAB specimen AND no tumor on imaging after NACTresidual disease in surgery specimen(n=208)1713719513no residual disease in surgery specimen(n=190)3*1879595FNR (95% CI)17.8% (12.8-23.7)6.2% (3.4-10.5)NPV (95% CI)81.4% (75.3-86.6)88.0% (80.3-93.4)PPV (95% CI)85.9% (80.3-90.4)67.2% (61.5-72.6)*whole tumor was removed by i.g.VAB, thus not false positiveNACT=neoadjuvant chemotherapyi.g.VAB=image-guided vacuum-assisted biopsyFNR=false-negative rateNPV=negative predictive valuePPV=positive predictive value Citation Format: Joerg Heil, André Pfob, Hans-Peter P Sinn, Geraldine Rauch, Paul Bach, Benedikt Schaefgen, Walter Weber, Sherko Kuemmel, Toralf Reimer, Markus Hahn, Marc Thill, Jens-Uwe Blohmer, Michael Golatta, RESPONDER Investigators. Diagnosing residual disease and pathologic complete response after neoadjuvant chemotherapy in breast cancer patients by image-guided vacuum-assisted breast biopsy: Results of a prospective multicenter trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr GS5-03.

Aim: Zirconium-89 has gained great interest for PET, when imaging at late time points is required. Desferrioxamine B (DFO), is mostly used for this radionuclide as bifunctional chelator (BFC) and w ...

Background: The interobserver agreement for (68)Ga-PSMA-11 Positron Emission Tomography/Computed Tomography (PET/CT) study interpretations in patients with prostate cancer is unknown. Methods:(68)Ga-PSMA-11 PET/CT was performed in 50 patients with prostate cancer for biochemical recurrence (n = 25), primary diagnosis (n = 10), biochemical persistence after primary therapy (n = 5) or staging of known metastatic disease (n = 10). Images were reviewed by 16 observers who used a standardized approach for interpretation of local (T), nodal (N), bone (Mb), or visceral (Mc) involvement. Observers were classified as having low (300 studies; n = 6). Histopathology (n = 25, 50%), post-external beam radiation therapy prostate-specific antigen (PSA) response (n = 15, 30%), or follow-up PET/CT (n = 10, 20%) served as standard of reference (SOR). Observer groups were compared by overall agreement (% patients matching the SOR) and Fleiss' κ with mean and corresponding 95% confidence interval (CI). Results: Agreement among all observers was substantial for T (κ=0.62, 95%CI 0.59-0.64) and N (κ=0.74, 95%CI 0.71-0.76) staging and almost perfect for Mb (κ=0.88, 95%CI 0.86-0.91) staging. Level of experience positively correlated with agreement for T (κ=0.73/0.66/0.50 for high/intermediate/low experience, respectively), N (κ=0.80/0.76/0.64), and Mc staging (κ=0.61/0.46/0.36). Interobserver agreement for Mb was almost perfect irrespective of prior experience (κ=0.87/0.91/0.88). Observers with low experience, when compared to intermediate and high experience, demonstrated significantly lower median overall agreement (54% versus 66% and 76%, P = 0.041) and specificity for T staging (73% versus 88% and 93%, P = 0.032). Conclusion: The interpretation of (68)Ga-PSMA-11 PET/CT for prostate cancer staging is highly consistent among observers with high levels of experience, especially for nodal and bone assessments. Initial training on at least 30 patient cases is recommended to ensure acceptable performance.

In patients with coronary heart disease, both arterial stiffness and wave reflections are increased and predict unfavorable cardiovascular events. Cardiac rehabilitation has the goal to reduce risk factors and slow the progression of the disease. The aim of this study was to prospectively determine the impact of an ambulatory cardiac rehabilitation program on pulsatile hemodynamics. Male patients after coronary interventions, bypass surgery, or acute coronary syndromes underwent exercise and resistance training. Before and after the program, pulsatile hemodynamics was measured. Exercise capacity was assessed with an incremental cycle ergometer protocol. A detailed two-dimensional and Doppler echocardiogram was obtained for systolic and diastolic left ventricular function. A total of 27 men participated in the study. After the intervention (n = 24), carotid–femoral pulse wave velocity decreased significantly from 8.7 (standard deviation (SD): 1.7) to 7.9 (SD: 1.9) m/s (p = 0.019), and augmentation index normalized for a heart rate of 75/min decreased significantly from 20.4 (SD: 8.7) to 17.5 (SD: 8.1; p = 0.017). The results suggest that a structured ambulatory rehabilitation program may improve pulsatile hemodynamics in coronary artery disease (CAD) patients.

Positron emission tomography (PET) agents targeting the prostate-specific membrane antigen (PSMA) are currently under broad clinical and scientific investigation. (68)Ga-PSMA HBED-CC constitutes the first (68)Ga-labelled PSMA-inhibitor and has evolved as a promising agent for imaging PSMA expression in vivo. The aim of this study was to evaluate the whole-body distribution and radiation dosimetry of this new probe.Five patients with a history or high suspicion of prostate cancer were injected intravenously with a mean of 139.8 ± 13.7 MBq of (68)Ga-PSMA HBED-CC (range 120-158 MBq). Four static skull to mid-thigh scans using a whole-body fully integrated PET/MR-system were performed 10 min, 60 min, 130 min, and 175 min after the tracer injection. Time-dependent changes of the injected activity per organ were determined. Mean organ-absorbed doses and effective doses (ED) were calculated using OLINDA/EXM.Injection of a standard activity of 150 MBq (68)Ga-PSMA HBED-CC resulted in a median effective dose of 2.37 mSv (Range 1.08E-02 - 2.46E-02 mSv/MBq). The urinary bladder wall (median absorbed dose 1.64E-01 mGv/MBq; range 8.76E-02 - 2.91E-01 mGv/MBq) was the critical organ, followed by the kidneys (median absorbed dose 1.21E-01 mGv/MBq; range 7.16E-02 - 1.75E-01), spleen (median absorbed dose 4.13E-02 mGv/MBq; range 1.57E-02 - 7.32E-02 mGv/MBq) and liver (median absorbed dose 2.07E-02 mGv/MBq; range 1.80E-02 - 2.57E-02 mGv/MBq). No drug-related pharmacological effects occurred.The use of (68)Ga-PSMA HBED-CC results in a relatively low radiation exposure, delivering organ doses that are comparable to those of other (68)Ga-labelled PSMA-inhibitors used for PET-imaging. Total effective dose is lower than for other PET-agents used for prostate cancer imaging (e.g. (11)C- and (18)F-Choline).

Submukose Raumforderungen im Osophagus werden haufig bei der Gastroskopie entdeckt, entweder als Zufallsbefund oder weil sie symptomatisch in Form von Passagestorung manifest werden. In der grosen Mehrzahl der Falle handelt es sich um gutartige Tumoren, meistens Leiomyome; eine Vielzahl anderer, aber seltener Tumoren konnen ebenfalls vorkommen. Die Endosonographie ist die Methode der Wahl, um diese Raumforderungen im Hinblick auf Dignitat und auch Notwendigkeit der Verlaufskontrolle abzuklaren. Bei Wachstum, vermehrter Symptomatik und ungewisser Dignitat sollte eine endoskopische oder chirurgische Resektion erfolgen. Nur sie erlaubt letztlich eine eindeutige histologische und damit prognostische Zuordnung. Im vorliegenden Fall handelt es sich um ein Lymphangiom, einen gutartigen, im Osophagus auserst seltenen Tumor.

Assessment of regurgitant flow by the flow convergence method is based on reading absolute velocities from color Doppler maps. Velocity overestimation by high pass filtering above 100 Hz has been reported. An extremely low filter, however, is impracticable in patients. A ratio of pulse repetition frequency (PRF)/filter of 10/1 usually results in good quality color maps as judged visually. We studied in vitro the influence of RPF and filter on the absolute velocities within color maps of the flow convergence, keeping PRF/filter at 10/1. The color maps were also compared with computerized flow simulations. Flow across different orifice plates was scanned using two different setups for each flow condition: low velocity setup (PRF 600-2500 Hz, filter 50-300 Hz) and high (PRF 1500-6000 Hz, filter 200-600 Hz). From the color maps, velocity profile curves were read along the flow center line across the flow convergence. The high velocity setup provided artefact-free color maps at a distance d = 2-4 through 8-11 mm to the orifice, the low setup at d = 6-8 through 18 mm. Within the overlapping range (d = 6-8 through 8-11 mm), the resulting curves showed no significant differences in local velocity, with a slight trend towards higher velocities with the high velocity setup (2.2-2.9%). The simulations agreed well with color Doppler except for slightly lower values at d10-12 mm. Changes in PRF and filter have no significant influence on the absolute velocities displayed within color maps as long as PRF/filter is kept close to 10/1.

AIM Evaluate within a pilot setup feasibility and safety of minimally invasive needle-biopsy of sentinel nodes guided by SPECT/US as compared to surgical removal while defining optimal needle for follow-up trial. METHOD AND MATERIALS As pretrial test phase of the MinimalSNB study, 38 breast cancer patients (6 centers) were taken a needle-biopsy of their sentinel lymph nodes (SLNs) under guidance of SPECT/US (SentiGuide by SurgicEye, Munich, DE). All patients were indicated for a surgical SLN biopsy which was performed immediately after the needle-biopsy. For the test phase, 4 different biopsy systems were tested: HistoCore 14G (BIP, Tuerkenfeld, DE), elite 10G and 13G (Mammotome, Cincinnati, OH, US) and CASSI II 10G (Scion Medical Technologies, Boston, MA, US). Histopathological examination (H&E, step-sectioning) of needle-biopsies and surgically removed SLNs were compared. RESULTS No single complication was reported. Occasionally, small hematomas could be found close to the SLN during surgery. Duration of complete procedure (imaging, needle placement, biopsy) took in average 17min. A learning curve was observed in duration (average after 5 biopsies 12min). 1-14 samples were taken of each SLN (average 5 samples). Final pathological examination of material harvested with both methods matched in 34 cases (33 negatives, 1 positive). The needle biopsy failed to detect metastases in 2 pN1 SLNs. In 1 case, the surgically resected tissue did not contain lymph nodes and the needle biopsy remained the only information on nodal status. In 1 case a metastasis found in needle-biopsy motivated a second reading of an originally negative SLN which resulted in the upstaging of the patient. In both cases a metastases was missed by needle-biopsy, the retrieved lymph tissue was minimal (1x 14G sample, 1x 10G sample tangential to node). CONCLUSION SPECT/US showed to be a valid method for percutaneous detection of SLNs and needle-guidance. Sampling SLNs with a needle seems safe and feasible. However it requires proper training and user experienced with axillary needle-biopsies. Retrieving more tissue (more cores and larger lumen needles) improves diagnostic power of needle-biopsy. These considerations will be taken within the upcoming MinimalSNB trial. CLINICAL RELEVANCE Sentinel lymph biopsy today is a surgical diagnostic procedure with an nonzero morbidity. Moving it out of the operating theatre to a needle-based intervention has a huge impact on the burden of this procedure for the patient as well as relevant improvements in logistics, workflow and radiation burden. Citation Format: Paepke S, Pfob CH, Ohlinger R, Gruber I, Thill M, Blohmer JU, Kuehn T, Hahn M, Kiechle M, Wendler T. Can sentinel node staging be performed using a minimally invasive needle-biopsy? Results of German multicentric pilot study. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-01-16.

In patients with coronary heart disease, both arterial stiffness and wave reflections are increased and predict unfavorable cardiovascular events. Cardiac rehabilitation has the goal to reduce risk factors and slow the progression of the disease. The aim of this study was to prospectively determine the impact of an ambulatory cardiac rehabilitation program on pulsatile hemodynamics. Male patients after coronary interventions, bypass surgery, or acute coronary syndromes underwent exercise and resistance training. Before and after the program, pulsatile hemodynamics was measured. Exercise capacity was assessed with an incremental cycle ergometer protocol. A detailed two-dimensional and Doppler echocardiogram was obtained for systolic and diastolic left ventricular function. A total of 27 men participated in the study. After the intervention (n = 24), carotid–femoral pulse wave velocity decreased significantly from 8.7 (standard deviation (SD): 1.7) to 7.9 (SD: 1.9) m/s (p = 0.019), and augmentation index normalized for a heart rate of 75/min decreased significantly from 20.4 (SD: 8.7) to 17.5 (SD: 8.1; p = 0.017). The results suggest that a structured ambulatory rehabilitation program may improve pulsatile hemodynamics in coronary artery disease (CAD) patients.

Aims: The flow convergence method enables the determination of flow across restrictive orifices. It was validated for planar orifice plates and atrioventricular valves. However, its quantitative application to aortic regurgitation is complicated due to the complex valve anatomy which distorts the converging flow field. An open angle formed by the leaflets causes relatively lower velocities in the region near the orifice, resulting in underestimation of flow. Confinement of the flow convergence region by the ascending aorta relatively increases the velocities at greater distance to the orifice and can cause overestimation of flow. We hypothesized that there is a region at intermediate distance to the orifice, where both these effects on the flow field are minimal, so that the local velocity there is only a function of flow. Methods and results: In a flow model, aortic regurgitation was simulated. The flow convergence was imaged by color Doppler. Different scanning directions were used (analogue to apical and parasternal approach). Velocity profiles across the flow convergence were read along the line from the scanhead to the orifice. At a distance of 7 mm to the orifice, a uniform value was found for the ratio of local velocity v(7 mm)/flow (=0.28cm−2). Variations in size (3.5 to 7 mm), site (central versus lateral) and leaflet angle (planar versus inverted funnel) of the orifice and in the scanning approach had only a minimal effect on this value. Conclusion: The aortic regurgitant flow convergence is characterized by a relatively uniform V (7 mm)/Q. Independent of variations in the anatomy and the scanning approach, this value directly reflects flow.