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Background About 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. Methods Uni- 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. Results In 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 < 0.001], multicentric disease on imaging before NST (OR, 2.74; 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). Conclusion For 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 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 complicationsthis has to be part of internal team discussions and should be considered in future trials for comparable results.

565 Background: Neoadjuvant systemic treatment elicits a pathologic complete response (pCR) in up to 80% of women with breast cancer. In such cases, breast surgery, the gold standard for confirming pCR in the breast, may be considered overtreatment. So far, no approach alone – e.g. imaging, vacuum-assisted biopsy (VAB) – has accurately detected and excluded residual disease without surgery in multicenter prospective trials. We evaluated the ability of Artificial Intelligence algorithms to securely identify patients with residual tumor in the breast to safely select patients who might be spared from surgery. Methods: We collected multicenter, international data from 570 women who were included in prospective trials with initial stage I-III breast cancer of all biological subtypes and at least partial response on imaging, undergoing VAB before guideline-adherent surgery. We trained an ensemble of algorithms (including Regularized Regression, Support Vector Machines, and Neural Network) using 27 patient, tumor and VAB variables. Data were randomly partitioned into training and test sample with a 3:1 ratio and developed with 10-fold cross-validation. Primary endpoint was the sensitivity to diagnose residual disease by algorithm compared to surgery. Diagnostic performance of the algorithm was further evaluated on an external, independent dataset. Results: The algorithm was able to reliably identify women with residual disease before surgery (see table): Sensitivity for the internal test set was 96.9% (94 of 97; 95%CI 91.2-99.4%) and for the external, independent dataset 96.2% (26 of 27; 95%CI 80.4-99.9%). Most informative predictor of residual disease were tumor cells diagnosed in the VAB specimen, DCIS in the initial diagnostic biopsy, grading, and largest diameter on imaging after neoadjuvant treatment. Conclusions: Safely selected patients without residual disease as assessed by our algorithm may be spared by breast surgery in future trials. [Table: see text]

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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 &lt;5mm or &gt;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.