Background Superparamagnetic iron oxide nanoparticles (SPIO) have been used as a tracer for sentinel lymph node (SLN) localization in breast cancer, demonstrating comparable performance to the combination of radioisotope (RI) and blue dye (BD). Methods A systematic literature search and meta-analysis with subgroup and meta-regression analysis were undertaken to update the available evidence, assess technique evolution, and define knowledge gaps. Recommendations were made using the GRADE approach. Results In 20 comparative studies, the detection rate was 97.5 per cent for SPIO and 96.5 per cent for RI ± BD (risk ratio 1.006, 95 per cent c.i. 0.992 to 1.019; P = 0.376, high-certainty evidence). Neoadjuvant therapy, injection site, injection volume or nodal metastasis burden did not affect the detection rate, but injection over 24 h before surgery increased the detection rate on meta-regression. Concordance was 99.0 per cent and reverse concordance 97.1 per cent (rate difference 0.003, 95 per cent c.i. −0.009 to 0.015; P = 0.656, high-certainty evidence). Use of SPIO led to retrieval of slightly more SLNs (pooled mean 1.96 versus 1.89) with a higher nodal detection rate (94.1 versus 83.5 per cent; RR 1.098, 1.058 to 1.140; P < 0.001; low-certainty evidence). In meta-regression, injection over 24 h before surgery increased the SPIO nodal yield over that of RI ± BD. The skin-staining rate was 30.8 per cent (very low-certainty evidence), and possibly prevented with use of smaller doses and peritumoral injection. Conclusion The performance of SPIO is comparable to that of RI ± BD. Preoperative injection increases the detection rate and nodal yield, without affecting concordance. Whether skin staining and MRI artefacts are reduced by lower dose and peritumoral injection needs to be investigated.
39 Background: Image-guided vacuum-assisted biopsy (VAB) is increasingly used after completion of neoadjuvant chemotherapy (NAC) to assess residual disease in the breast, facilitate risk-adaptive surgery and potentially identify exceptional responders who may not require surgical intervention. The aim of this analysis was to investigate the diagnostic performance of a standardized post-NAC VAB protocol, developed following retrospective analysis of institutional data (1). Methods: Prospective cohort study of patients with HER2 positive and triple negative (TN) invasive ductal carcinoma, treated with NAC, who had partial/complete imaging response and underwent post-NAC VAB to aid surgical planning between 02/2018 and 06/2019. The aim of VAB was to sample the site of residual imaging abnormality (breast residuum <2cm) previously marked by clip insertion. Pathologic complete response (pCR) was defined as no residual disease in the breast (ypT0). Diagnostic accuracy of VAB was calculated using final surgical pathology as the reference standard. Simple descriptive statistics were performed. Results: 26 eligible patients underwent post-NAC VAB. This was representative in 23 cases. The overall pCR rate was 46.2% (42.1% for HER2 positive, 57.1% for TN phenotypes). The post-NAC VAB false negative rate (FNR) was 9.1% (95% CI: 0-26.1) and the negative predictive value (NPV) was 90.91% (95% CI: 60.27-98.51) with an overall accuracy of 86.96% (95% CI: 66.41-97.22). Conclusions: This data suggests that post-NAC VAB may reliably predict pCR in patients with HER2 positive and TN invasive ductal carcinoma with good response to NAC. Further technical refinements in VAB technique, standardization in patient selection and prospective trials are warranted to further explore the role of post-NAC VAB in supporting minimal or no surgery trials. References 1. Tasoulis MK, Roche N, Rusby JE, Pope R, Allen S, Downey K, Nerurkar A, Osin P, Wilson R, MacNeill F. Post neoadjuvant chemotherapy vacuum assisted biopsy in breast cancer: Can it determine pathologic complete response before surgery? J Clin Oncol 2018;36 (Supplement): abstr 567.
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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